1

Aniakor

Oil in Nigeria

Social sciences

Economics

okeke chioma m

Digitally Signed by: University of Nigeria,

Nsukka

DN : CN = okeke chioma maryrose

O= University of Nigeria, Nsukka

OU = Innovation Centre

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CHAPTER ONE

Introduction

1.1 Background of the study

The stock market, a market for resource mobilization contributes to

economic growth through the specific services, it performs either directly

of indirectly. Notable among the functions of stock market are

mobilization of savings, creation of liquidity, risk diversification, improved

dissemination and acquisition of information, and enhanced incentive for

corporate control. Improving the efficiency and effectiveness of these

functions through prompt delivery of their services can augment the rate

of economic growth (Bhide, 1993). At any stage of a nation‟s

development, both the government and the private sectors would require

long-term capital for instance, companies would need to build new

factories, expand existing ones or buy new machinery. Government

would also require funds for the provision of infrastructure. All these

activities require long-term capital, which is provided by a well

functioning stock market that is already using all publicly and privately

available information for its efficient functioning and in the formation of its

prices. Prominent among the necessary ingredients required for the

efficient functioning of the stock market is information about the

consequences of oil price

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shocks. It is reasonable to expect that the stock market would absorb the

information about the impact of such a shock and incorporate it into

stock prices very quickly. Since asset prices are the present discounted

value of the future net earnings, of firms, both the current and the future

impact of such a shock should be absorbed into prices and returns

without having to wait for those impacts to actually occur.

Changes in the price of crude oil are often considered an important

factor for understanding fluctuations in stock prices. For example, the

Financial Times and on August 21, 2006,attributed the decline of the US

stock market to an increase in crude oil prices caused by concerns about

the political stability in the Middle East (including the Iranian nuclear

program, the fragility of the cease fire in Lebanon, and terrorist attacks

by Islamic militants). The same newspaper on October 12, 2006 argued

that the strong rallies in global equity markets were due to a slide in

crude oil prices that same day.

It is well documented that the conditional volatilities of stock

market indices change over time. Many researchers are intrigued by the

cause of these changes, and a large literature exists where time series

data on financial and macroeconomic variables are studied in relation to

stock market data. Hamilton (1983) presents

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an influential article, which shows that almost all US recessions since the

Second World War, have been preceded by oil shocks. Mork (1994)

surveys the extensive literature on oil and the macro economy, following

Hamilton (1983) and demonstrates a clear negative correlation between

oil prices and aggregate measures of output or employment. Moreover,

Hamilton (1985) argues that oil shocks are exogenous events since the

causes can be attributed to historical events, e.g the Iraq invasion of

Kuwait in 1990. Since stock prices in theory, equal the discounted

expectations of future cash-flows (dividends), which are likely to be

affected by macroeconomic movements, they are possibly affected by oil

shocks. Also, an oil price increase acts like an inflation tax on

consumption, reducing the amount of disposable income for consumers.

Non oil producing companies‟ face higher fixed costs, which are passed

on to higher consumer prices. These effects decrease in company

wealth, lowering their dividends. Oil is just as any asset bought and sold

in the financial markets; in fact, oil is the most traded asset in the world.

Important meeting places for trading oil is the international Petroleum

exchange in London and the New York Mercantile Exchange. In Nigeria,

the price of oil is determined by factors such as the US exchange rate,

the

5

international price in dollars, market conditions and taxes (DPR Bulletin

2006).

The Nigerian stock exchange, which is an automated exchange, is

to day currently trading almost 300 company stocks. There are oil

related stocks such as Chevron oil, Afroil Plc, Oando Plc represented on

the list. These stocks are naturally more sensitive to variations in the oil

price than other stocks that are not dependent on oil. In our study, we

are looking at the Nigerian stock market as a whole and how oil price

volatility affects its performance.

1.2 Statement of the problem

One of the determinants of the Overall growth of an economy

depends on how efficiently the stock market performs its allocative

functions of capital. As the stock market mobilizes savings, concurrently,

it allocates a large proportion of it to the firms with relatively high

prospects as indicated by its rate of returns and level of risk (Bhide,

1993). The importance of this function is that capital resources are

channeled by the mechanism of the forces of demand and supply to

those firms with relatively high and increasing productivity thus

enhancing economic expansion and growth (Alile, 1997). This resultant

effect of a boost in the economy

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leading to growth is also consequent upon the shifting of society‟s

savings to higher return investments.

However, existing literature on financial market indicates that stock

markets respond quickly to economic uncertainly indicating prominent

implications on investment returns if generated by oil price uncertainty.

Economic theory stated that certain amount of variation in price is

needed for business to thrive. Without it, there may not be need to

hedge and where there are no hedgers, there are no speculator and

investment becomes homogenous of degree zero (Mabro, 2001).

However, to a petroeconomy as Nigeria, petroleum price variations

generate uncertainty which may have negative implications for their

revenue profile, fed through the multiplier effect and onto

macroeconomic variables such as real stock returns. Due to the central

role oil plays in the functioning of our economy, changes in energy prices

are not the same thing as changes in the price of most other goods. Oil

price shocks can have macroeconomic consequences, in terms of higher

inflation, higher unemployment and lower output.

Economic literature has shown that oil price shocks have proven

particularly troublesome for the stock market of most economies. The

literature holds that since stock prices in theory are likely to be affected

by macroeconomic movements, they are possibly affected by oil price

shocks. It states that when oil prices rise suddenly, the overall inflation

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rate is temporary pushed up because other prices do not instantly adjust

and fall. At the same time, the overall cost of production rises and

producers must cut back production which causes the contraction in

output and employment. Also an oil price increase acts like an inflation

tax on consumption reducing the amount of disposable income for

consumers. Non oil producing companies face higher fixed costs which

are passed on to higher consumer prices. These effects decrease

company wealth, lowering their dividends.

Interestingly, existing literature shows that several studies that link

oil prices to stock markets have been carried out in other countries (Kaul

and Seyhun, 1990. Hamiton, 1930, Jones and Kaul, 1996, Huang, et al,

1996, Sadorsky 1999, Basher and Sardosky, 2004. More recently Yoon

(2004) argues that if oil price has an impact on the macroeconomy, then

it should also affect the stock markets. His work gives us a good review

of the link between oil price the macroeconomy, and once again it shows

that oil has an effect on different economies around the world. In Nigeria,

however, apart from few studies on the contribution of stock market to

economic growth and studies on stock market determinants, among

others, evidence provided in the literature seems to suggest that there

have been very sparse empirical studies. Infact none known to us that

link oil price shocks market has been published in Nigeria.

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Since countries differ by their diverse macroeconomic

environments, country – specific studies are necessary to find the extent

oil price shocks affects their stock markets since the impact of oil price

shocks on the stock market appears to be asymmetric.

From the foregoing, it is imperative that the conduct of a country-

specific study in Nigeria is not only required, but also necessary. In light

of this, we propose to employ the vector Autoregrssion (VAR) framework

to address the following questions: Do oil price shocks transmits to stock

market in Nigeria? What is the pattern of response of the Nigerian stock

market to oil price fluctuations? How does the augmenting variable in the

link between oil price shocks and the Nigerian stock market, respond to

shocks on oil price fluctuations?

1.3 Objective of the study

This work intends to analyze the relationship between oil price

shocks and the stock market in Nigeria. Thus the following objectives

would be addressed:

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i. To determine the impact of oil price shocks on stock market in

Nigeria.

ii. To investigate the pattern of response of the Nigerian stock market

to shocks on oil price fluctuations.

iii. To determine the response of the augmenting variables in the link

between oil price shocks and the stock market in Nigeria to shocks

on oil price fluctuations.

1.4 Statement of hypotheses

Based on this research work, the following hypotheses have been

suggested:

i. An oil price shock has no impact on the stock market in Nigeria.

ii. Stock market in Nigeria has no significant impulse response to

shocks on oil price fluctuations.

iii. The augmenting variables in the link between oil price fluctuation

and stock market in Nigeria has no significant impulse response to

shocks on oil price fluctuations.

1.5 SIGNIFICANCE OF THE STUDY:

The stock market long has been viewed as an information

collection and processing institution.

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The most crucial challenge faced by the Nigeria stock market

today has been the efficiency with which it processes such information

for the overall benefit of investors. The lead role of this study in providing

one of such information cannot be over emphasized.

Understanding the links between oil price shocks and its events on

the stock market is of great importance for a financial hedger, portfolio

manager, asset allocation, or other financial analysts.

Since asset price are the presented discounted value of the future

net earnings of firms, information from this study, would enable firms to

absorb oil price shocks into process and returns very quickly without

having to wait having to wait for those impacts to actually occur.

The outcome of this research would also assist the students, the

government, members of the academia and policy makers in the

provision of a framework upon which further research on the effects of oil

price shocks on stock market behaviour can be carried out.

Moreover, the research would not only facilitate growth in the stock

market but also promote growth in the financial market

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through the provision of one of the key information needed for the

efficient functioning of the financial market.

Lastly, the study will fill the research Lacuna on the effects of oil

price shocks on the behaviour of the Nigeria stock market.

1.6 Scope and limitation of the study

This study would cover the period 1970 to 2009. The choice of the

period is based on data availability. Many macroeconomic variables that

may have influence on the stock market in Nigeria would not be

considered except oil price, interest rates, industrial production, and

inflation. The study is also limited by finance and time.

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CHPATER TOW

Literature review

2.1 Theoretical literatures

Development in the oil sector especially as its price affects

macroeconomic variables of contemporary global economies have

attracted researchers‟ attention, recently. While exhaustive literature has

focused mainly on the effects and the transmission mechanism of oil

price fluctuations and general macroeconomic variables and mainly from

the demand side; this study, while focusing on the supply side will

equally attempt to study the phenomenon from the demand side. A

leading school of thought argues that energy price increases leads to

improved macroeconomic performance. As Kandi (2000) for example

holds, a rise in price of a major commodity generates a positive spill over

effect.

Economic theory suggest the economics suffer from recessions

due to presence of “sticky prices” if markets adjusted instantly, then

recessions could be avoided: wherever economic condition changed,

price and wage changes would automatically bring the economy back to

full employment. In actuality, however, there are menu costs, 1

information costs, uncertainty, and contrasts in our economy that make

prices stocky. As a result, adjustment takes time and unemployment and

economic contraction can result in the interim.

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Against this background, Iwayeni (1995) and Moreno (2003)

opined that oil price upturn has both direct and indirect effect

respectively. This price upturn shifts the supply curve upward generating

higher incomes to owners of factors. From the Keynesina angle, he

asserts that this monetary expansion boosts the disposable in comes of

economic units which consequently beef up aggregate demand in the

system. In an attempt to meet this increased demand, producing units

expand production. This action of business units through the multiplier

effect will lead to increases in output and employment, ceteris paribus.

The monetarist behavoural hypothesis seems to uphold this

synthesis but differs in the transmission mechanisms. To them, a

monetary expansion from fiscal operations will force interest rates to fall.

Since investors borrow extensively to finance investment, which pays of

at a lower interest rate, the fall in the rate of interest precipitates a leap in

investment and so, does output and employment, mutates mutandis

(Bhatia, 1998 and Dorubusch, et al 2001).

In the same vein, developing a theoretical model for three types of

countries; developed, exporter and developing, Moreno (2003) observes

that a rise in oil price transfers incomes from the developed world to

exporting countries, improving petrol economies balance of payment

positions and consequently raising fiscal surplus. To him, this fiscal

expansion creates two effects in aggregate demand. First, it stimulates

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tradable goods imported and second, it pushes non-tradable prices up

due to the shower supply response. These twin effects, pop-up an

inflationary process and exchange rate appreciation. The pursuit of the

exchange rate mechanism to its logical conclusion generates two slightly

heterogeneous opinions.

First, with exchange rate rising, much of foreign currencies

produce limited domestic currency, causing a monetary contraction. This

in turn leads to high interest rates and low investment with output and

employment trailing behind. Given an upward review of exchange rate,

domestic goods become more expensive relative to foreign ones. This

high cost of producing goods whose foreign counterparts are available at

cheaper rates, discourages investment (which leads to low level of

output and employment), reduces company wealth, lowers stock market

dividends and encourages import and capital flight (which exacerbate

inflationary tensions).

On the other hand, is the school of though which believes that

increases in energy price only leads to economic crises-the classic

Dutch Disease Syndrome. The school according to Roomer (1987). In

Iwayemi, (1995) Outlines three macroeconomic effects of respectively.

The resource-pull effect involves the migration of factors from the

productive sectors to the less productive energy sector leading to decline

in productivity and employment thus exacerbating the poverty and

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inequality cleavages. The monetary effect derives from the actions of the

monetary authorities to monetize price booms, leading to excess

liquidity. From the Walrasian equilibrium approach, excess liquidity must

be balanced by excess demand for goods. Where the equilibrium

condition is absent, especially in developing economics, inflation

becomes cataclysmic wit massive decline in output, employment and

other variables as financial markets.

From the evidence provided in the literature so far, oil price does

not affect macroeconomic variables without passing through a number of

transmission channels. However, Fama (1981). Asserts that it is not the

level of oil prices that affects economic growth and inflation but rather

the change in energy prices. Thus, if policy makers wish to mitigate the

effect of oil prices on out put and inflation, they should be concerned with

rising oil prices and

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should not be concerned with “high” oil prices even if the high prices are

permanent. He argued that the only permanent macroeconomic effect of

higher oil prices is their negative effects on the terms of trade2.

Permanently, higher oil prices lead to a one time permanent decline in

the terms of trade and, significantly, the standard of living of a country‟s

consumers, all else equal.

2.2 The history of oil price shocks

SINCE World War II we have experienced three major oil price

shocks that affected the financial markets around the world strongly. In

1973 – 74, what became to be known as the first oil crises erupted. The

OPEC countries reduce their export of oil to countries supporting Israel

in the Arabic-Israeli war. They also decided to drastically increase the

price. Over a four month period, the price of crude oil had increased by

more than 250% (National Ency clopedia). The result of this was a more

from a booming economy into a recession for most petro economies of

the world. The second oil crises that hit were a product of unsettlement

in the Middle East due to the trainman revolution and later the Iran-Irag

war. After these two oil rises, plans for alternative fuels was a question

that rose in people‟s mind.

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In 1990, the Gulfwar erupted. The USA led invasion of irag caused

the price of oil again to increase drastically as can be seen in graph 2.0.

The price of the OPEC Reference Basket increases drastically, from $

15-34 per barrel this implies an increase of the price by over 120%.

In recent years there have been historically high oil prices. In 2004,

the oil price in nominal terms was very high, though as they in real terms

have not been as high as earlier oil price shocks. Events such as the

hurricane Katrina in 2005, and again unsettlement in the middle east

with the its led war on terror in Iraq has had an influence on the price of

oil. The price of the OPEC-basket was fluctuating around $70 (US) per

barrel in the summer of 2006. Presently, the price has risen beyond

$100 (US) per barrel.

Since 1999, we have experienced a steady increase in the price.

The graph below indicates the fluctuations of the oil price during the time

period that we examine in our paper. What one can see is a peak when

the Gulf crisis occurred and that there is a sharp drop in the price in

1985-86. This decrease occurred because OPEC put forward a new

pricing scheme that resulted in decline of the oil price.

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Graph 2.0 Historical oil Prices (1980-2006)

1970 1980 1990 2000 2010

years

Historical oil prices

___ price

80

60

40

20

0

Op

ec p

ric

e b

asket

19

Source OPEC bulletin 2007

2.3 Oil’s importance to Nigeria:

Since the 1970‟s, the import of oil products into Nigeria have

increased by more than 100%. The rise in oil import became prominent

in the late 90‟s as a result of damaged refineries and internal crisis

witnessed in the Niger Delta which is the hub of Nigeria‟s oil production

(NNPC, 2006). Today, more than half of all energy consumption in

Nigeria is based on oil.

Importance to stress the difference between oil price shocks from

external events like war and from cyclical movements in the oil price.

These two events have different effects on the market. A moderate

increase of the oil price is something Nigeria can cope with but a more

drastic oil price shock with the economy harder.

For example, in 1973/74 when the international price of petroleum

rose from $3 US per barrel (hereafter, Pb) to $11 US Pb, the Nigeria

government finances and external reserve grow by 63% and 92%

respectively. In 1979 when price shot up from $14 US Pb to $40 US Pb,

fiscal outlays, output, money supply and investment all escalated by

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91% 4.5%, 66% and (3% respectively (Watts, 200 and CBN, 2004). By

the end of 1981, when price fell, the economy contracted, exchange rate

collapsed while external debt mounted. In the wave of this, out declined

by 20% with an outrageous declaration of investment from 13% to less

than 5%. The austerity that permeated the economy at that time

promoted some researchers (Oyejide 1987, Neary and Wijibeigen, 1986,

1986 and Gelb, 1988, for example) to question whether petroleum is a

blessing or curse. In line mamer, when the Gulfwar shot price up from

$11 US Pb to about $40 US Pb, fiscal outlays soared by 100%, output

286% while inflation skyrocketed from 7.5% in 1990 to over 70% in

1994/95 period (CBN, 2004).

The consumption of oil products can be split into four main

categories; industry, transport and industrial machinery, House and

service, production of district heating and electricity. In the industry, oil is

mainly as source of power for machineries and in the production and

heating of facilities.

In transports, oil is mainly used as fuel for vehicles and presented

a large percentage of total energy consumption, but oil is also used for

air and sea transports. Since the 1970-80s, the oil consumption in this

category has increased much despite increased taxes, to further

understand the oil situation in Nigeria, diagram 2.0 is presented to

describe the shares of energy sources in Nigeria 2003 (DPR).

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Diagram 2.0 Pie chart indicating energy types in Nigeria, 2003

Oil related products represent more than half of energy production

in Nigeria, 2003 as can be seen in the diagram, above, comparing

figures from the 1970‟s and 2003 can give a fair description of the

development on the Nigerian energy market. Since the 1970‟s the

Nuclear power

Biological fuel

Oil

Coal

Gas

Water

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country‟s energy consumption has increased over all (NNPC 2005). The

share of oil consumption has increased with more than half. Coal has

drastically reduced and gas has drastically increased with a large

margin.

Nuclear power, biological fuels, and water and wind power as can

be seen above has a small share of our energy consumption today

contrary to events-most developed country that seeks alternative source

of oil.

Tables 2.1 Nigeria Oil Dependence in 1980 and 2003

1980 2003

Oil price, $/barrel 36.4 29.7

Oil import (N=million) 210.2 303,144.8

GDP (=N =million) 49632.3 6061700.00

Total expenditure (N =million) 14968.5 1225965.9

Oil import/Tot. Exp:% 1.40 24.72

Oil import/GDP% 0.42 5.0%

Source: NNPC 2004

Percentage calculated from given figures. (In table 2.1), it is

interesting to see that oil import as a percentage of total expenditure has

drastically increased in Nigeria. This gives us an indication that oil

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dependence in Nigeria has increased since the 1980‟s. just as in total

expenditure, the proportion of oil GDP has significantly increased.

2.4 Empirical literature

Officer (1973) is first to present evidence of a relationship between

the market factor (aggregate stock market) variability and business cycle

fluctuations, as measured by industrial production. Schwartz (1989)

performs vector autoregressions and finds weak evidence that

macroeconomic volatility can predict stock market volatility. The volatility

of bond returns and the growth rates of the producer price index, the

monetary base and industrial production, are used as macroeconomic

variables.

Jones and Jaul (1996) belong to the first authors to analyze the

reaction of international stock markets to oil shocks by current and future

changes in real cash flows and/or changes in expected returns. Their

study considered stock markets in the US, Canada, UK and Japan,

taking different institutional and regulatory environments into account.

Except for the UK, oil prices allow to predict stock returns and output

through 1991 in the other three countries. It shows that in the post war

period, oil price like had a “significant, and (on average) detrimental

effect on the stock market of each country”.

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It is “dramatic” in the case of Japan and less important for Canada.

The stock returns of each country-except UK-are negatively affected by

both current and lagged oil price variables.

The latter are negatively more significant. This raises the question

whether oil shocks induce any variation in expected stock returns or

whether the stock markets are in efficient. Due to measurement errors

for all macroeconomic variables, the authors emphasize that „the true‟

effect of oil shocks on stock market returns are likely to be even

stronger. The results showed that the effects of oil show on the US and

Canadian stock markets can be explained completely by their effects on

current and future real cash flows.

However, real cash flows and expected return proxies cannot

explain the fluctuations on the stock markets of these two countries the

authors assume that post war oil shocks seem to have generated

volatility.

Sardosky (1999) and Papapetrou (2001) contributed to further

studies of stock markets. Sardorskys analysis is based on monthly data

from 1947 to 1996-in contrast to quarterly data used in the study of

Jones and Kaul. The analysis showed that an oil price shock has a

negative and statistically significant initial impact on stock returns. Higher

production costs to decline. An efficient stock market will react with an

immediate decline in stock prices. Thus, individual oil price shocks

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depress real stock returns. Sardosky divided the period of his analysis,

1947 to 1996, into two sub-periods. The analysis showed that oil price

shocks had a larger impact after 1986. Thus, there‟s rather a change in

dynamics than a change in the response of the system to these shocks.

Finally Sardosky concludes “oil price shocks had a significant impact on

real stock returns although this impact was strongest after 1986

(……………)”. Papapetrou 92001) estimated that real oil stock returns

are affected negatively. This impact lasts for approximately – 4 months.

King, Sentana and Wad Hwani (1994) employ a different approach

and estimate a multivariate factor model, where co movements in stock

return volatility are induced by the volatility of a number of factors. Using

data on not only the US but on sixteen national stock markets, king et al

(1994) try to identify the causes for stock volatility through both

“observable” factors, e.g. Interest rates, industrial production and oil

prices, and “unobservable: factors which reflects the influences on stock

volatility that are to captured by published statistics. Their results display

little support for the observable economic variables. Instead, king, et all.

Contributes to the variability in stock return, and also, to the co-

movements in stock volatility across national markets.

Ciner (2001) extended existing studies on the relationship between

oil price and the stock market by testing for non-linear linkages

considering recent works on His subject (Hamilton 2000). Prior studies

as the one by Hung, Masulis and Stoil 91996) futures to stocks of

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individual companies but showed no impact on a broad based index like

the S and P. thus they concluded that influence of oil price shocks on the

aggregate economy is rather “myth than reality”. Ciner refused this

conclusion criticizing this study for not detecting sufficient non liner

linkages.

Using HMS data, Ciner demonstrates a significant non liner causal

correlation between crude oil future returns to S and P 500 index returns

and evidence that stock index returns also affect crude oil futures. This

indicates a feed back relation between S and P 500 stock returns and

crude oil futures. The analysis for the 1990s (from March 1990) until

March 2000) – when volatility had increased-provided an even stronger

nonlinear relationship than for earlier samples and backed up Sadorsky

(1999).

Basher and Sadorsky (2004), using a multifactor arbitrage pricing

model find strong evidence that oil price risk impacts on returns of

emerging stock markets.

Kaul and Seyhun (1990) examined the influence of the volatility of oil prices or rates of return to assets listed on the New York stock exchange (NYSE) over 1949-1984 using annual data. They regressed real stock returns on expected and unexpected inflation, oil price volatility and growth in industrial production. As expected, coefficients on both inflation variables were insignificant, that of the oil price variable was negative and significant. They found oil not significant in the 1949-65 subperiod but significant in the 1966-84 subperiods, contrary to Hamiton‟s (1983) evidence, they note. Variables investigated the asymmetric effects of oil price shocks on stock markets of some selected western countries. His finding indicate that over the sample period from week one of 1989 to a wee seven teen of 2005, strong evidence of volatility spillover is found for Japan, Norway, the UK and the US. Weak evidence of volatility spillover is found for Sweden over the sample period. Although the empirical results show that volatility spills over from oil to stock markets, new impact surfaces, which illustrate the estimated one period ahead impact of an oil shock, reveal small quantitative effects. The stock market‟s OWA shocks, which are

27

related to other sources of stock market uncertainty than the oil price,

have more prominent implications.

2.5 Limitations of previous studies

The evidence provided in the literature seems to suggest that oil

price shocks and the degree and nature of its effect on the stock market

have become an important empirical debate. The evidence further

suggests that oil price shocks tend to have asymmetric effects on the

stock markets of most pertroecomies.

Most of these studies, Jones and Kaaul (1996), Sadorsky (1999),

Papapetrou (2001), Ciner (2001) and Basher and Sadorsky (2004), used

panel data and were mostly based on cross country analysis while some

were based on their own country analysis without concrete evidence for

the Nigerian case. Since countries differ by their diverse macroeconomic

environments, country-specific studies are necessary to find the extent

macroeconomic variables are affected by variations in oil price as well as

the durations in which these effects last.

Moreso, few existing studies in Nigeria centered more on stock

market efficiency, stock market and economic growth, the impacts of

development programmes (e.g. NEPAD, NEEDS etc) on stock market

development etc. But none, to the best of my

28

knowledge has gone further to address the effects of oil price shocks on

stock market behaviour in Nigeria.

Hence, the fact that there are limited empirical evidence on this

topic in most developing petroecomies like Nigeria and the desire to

produce further empirical evidence, therefore, motivated our interest in

carrying out this study in Nigeria using vector Auto regression (VAR)

model.

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CHAPTER THREE

3.1 Methodology

This study is designed to investigate the effects of oil price shocks

on the market behaviour in Nigeria. The research employs time series

econometrics method using a dynamic model in the form of vector

Autoregression (VAR) model.

However, if after examining the time series properties of the

underlying data, we discover that there is cointegration, VAR model will

be transformed into the vector error correction model (VECM) in order to

estimate the longrun dynamic relationships.

3.2 Model specification:

This research will be guided with the following models drawn from

the objectives of the study. The functional form of the model to be

adopted for this study is represented as follows:

( , , , )t t t t tSMR f OPS INDP INTR INF ……………….(1)

Where

SMRt= Stock Market Return (Proxied by price index of shares)

OPSt= Oil price shocks

INDPt= Industrial Production

INTRt= Interest rate

INFt= Inflation rate

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We expect a positive relationship between stock market return and

industrial production. While, inflation rate, interest rate and oil price

shocks are expected to have negative relationship with stock market

return.

The mathematical form of VAR is

Yt= A1 Yt-n…………+Ap Yt-p + BXt + Et …………………………. (2)

Where Yt is a K vector endogenous variables, Xt is a vector of

exogenous variable, At …………… Ap and B are matrices of confidents

to be estimated and Et is a vector of innovations that may be

contemporaneously correlated with others but are uncorrelated with

exogenous variables.

The structural equations of VAR model for the study are as follows:

0 1 1 2 1 3 1 4 1 1

0 1 1 2 1 3 1 4 1 2

0 1 1 2 1 3 1 4 1 3

0 1

........(1)

..........(2)

..........(3)

t t t t t t

t t t t t t

t t t t t t

t t

SMR OPS INDP INTR INF

OPS SMR INDP INTR INF

INDP OPS INTR SMR INF

INTR OPS

1 2 1 3 1 4 1 4

0 1 1 2 1 3 1 4 1 5

.......(4)

..........(5)

t t t t

t t t t t t

INDP SMR INF

INF OPS INDP SMR INTR

Where the variables are defined as follows

SMRt= Stock Market return,

OPSt=Oil price shocks

INTRt=interest rate

INDPt=industrial production,

INFt= inflation rate.

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, , ,and are called the structural parameters and it as i=1,2 3,4,5, are

the impulse or shocks in the VAR model (stochastic term).

Transforming the structural term of the VAR into a vector error

correction model (VECM) to estimate shorten dynamic relationship, we

have.

0 1 1 .......(6)t t tSMR OPS ECM

The equation for other variables follows this pattern sequentially.

Where is the difference operator

ECM is the error correction term and t is the random error term.

3.3 Model justification:

The choice of a VAR model is made because it is one of the

models that are highly vulnerable to simultaneity bias. It has the ability to

test for weak exogeneity and parameter restrictions. It also assumes that

there is no aprior direction of causality among the variables. It does not

require any explicit economic theory to estimate the model (Gujarate

2003). VAR models after a way of analyzing the dynamic relationships

between variables such as oil price shocks, stock market returns and

other macroeconomic variables. It also allows us to take into account the

delayed response with parsimonious lag structure (Agenor, Mabil and

Youset, 2005). When a direct interpretation of the lagged variables are

used to provide the information regarding the impact of the portion of the

32

independent variables on the dependent variable. This an important

feature of VAR model is its use in estimating residuals called VAR

innovations and it obviates a decision as to what contemporaneous

variables are exogenous with only lagged variables on the right hand

side. It therefore recognizes all variables as dependent variable (Green,

2000).

The transformation of the VAR into the VECM is to account for the

speed of adjustment in the long-run and short-run dynamic of the model

and it has a co-integration restriction embedded in the specification.

Thus, it can also be used on co-integration and non-stationary series.

Hence a VAR is a system of equations in which each endogenous

variable is a liner function of its past values.

3.4 Estimation procedure

3.4 Unit root tests

Unit root tests will be conducted on all the variables using the

Augmented Dickey Fuller (ADF) test in order to eliminate the problem of

autocorrelation by including enough terms so that the error term is

serially uncompleted. Thus, a time series is stationary if its means

variance and auto-covariance remain the same no matter at what point it

is measured (Gujarati, 2003).

33

For a guide to an appropriate specification of the regression

equation, the characteristics after time series data used for the

estimation of the model will be examined to avoid spurious regression,

which results from the regression of two or more non stationary series.

It is as stated below:

1 2 1 1 ........(7)t t t tY sY Y

Where t is pure while noise or error term satisfying all the classical

assumptions.

∆ = difference operator

Yt = each of the series

Yt-1 = the lag of each series.

3.4.2 Cointegration test

This test will be used to establish the existence of long-term

relationship between stock market return (SMR) industrial Production

(INDP) and inflation rate (INF). In regard, the Johansen (1988)

procedure will be used to determine the number of cointegrating vectors.

This approach is chosen because it does not suffer normalization

problem and it is robust to departure from normality (Gujarate, 2003).

3.4.2 Optimal lag-length

34

This is used to know the actual number of lag to be introduced so

as to avoid too much lag or too few lag. Too much lag will consume

degrees of freedom and multi collinearity will set in while too few lag will

lead to specification error. Therefore, based on the Akaike (AIC) or

Schwarze information criterion (SIC), we shall choose the lag level that

has the minimum information criterion for the VAR estimation.

3.4.4 Impulse response function

The impulse response function allow us to study the dynamic

behaviour of each variables of the system by determining whether an

exogenous shock causes short-run or long-run changes in the variables

chosen and other variables in the VECM. An impulse response function

traces the effect of a one standard deviation shock to one of the

innovation on current and future values of the endogenous variables. In

other words, it traces out the response of the dependent variables in the

VAR system to the shocks in the error term.

3.4.5 Variance decomposition analysis

This is an estimate the shows the proportion of variance forecast

error term (Petterson, 2000). For this research work, it will show the

proportion of variance of the forecast error for stock market that can be

attributed to variation to each of the exogenous variables.

35

In addition, a vector error correction model (VECM) will be applied

to estimate short-run dynamic relationships. The VECM has

cointegration relations built into the model so that it restricts the long-

term behaviour of the endogenous variable to converge to their

cointegrating relationships while allowing for short-term dynamic

adjustments.

The co-integrating term is called the error correction term because

the deviation from long-term equilibrium is corrected gradually through

partial short-term adjustment in the VEM; the

36

explanatory power for MC is measured by the value of the adjusted R2.

While in the VAR model, the coefficients are long-run impacts.

3.5 Source of data

Data for this study shall be from secondary sources. The

estimation period is from 1970-2005. The data used in this study are

from the statistical bulletin of the CBN (2004, 2005), CBN Annual Report

and Statement of Account for various years, World Bank Africa data, the

International Financial statistics yearbook of IMF for several years and

the Nigeria stock exchange.

3.6 Econometrics software

The E – Views econometric package shall be utilized in analyzing

the data while excel will be used in imputing the data.

37

CHAPTER FOUR

Result / Data analysis

Unit root test

To test for stationarity or the absence of unit roots, this test is done using

the Augmented Dickey Fuller test (ADF) with the hypothesis which states

as follows: If the absolute value of the Augmented Dickey Fuller (ADF)

test is greater than the critical value either at the 1% , 5% ,or 10% level

of significance , then the variables are stationary either at order zero,

one ,or two. In order to assess the time series properties of the data unit

root tests were completed. The results of the Augmented Dickey Fuller

(ADF) are as follows: The tests indicate that that all the variables are

integrated of order one I (1) process at 5% level of significance.

Table 3: Unit root test

Variable First difference

ADF Test

Statistic

probability

38

D(MC(-1)) -

4.108794

0.0002

D(OPS(-1)) -

4.010831

0.0003

D(INF(-1)) -

6.170950

0.0000

D(PRODUCT(-

1))

-

7.031193

0.0000

D(INTEREST(-

1))

-

3.920838

0.0004

Lag Length Selection

The lag length for the VAR (p) model is determined by using model

selection criteria. The general approach is to fit VAR (p) models with

orders p =0,...,p max and choose the value of p which minimizes some

model selection criteria. Model selection criteria for VAR(p) models have

the form

( ) ln ( ) . ( , )TIC p p C n p ………………………………... (6)

Where 1

1ˆ ˆ( ) T

t t tp T

is the residual covariance matrix without a

degrees of freedom correction from a VAR (p) model, TC is a sequence

indexed by the sample size T, and ( , )n p is a penalty function which

39

penalizes large VAR (p) models. The three most common information

criteria are the Akaike (AIC), Schwarz-Bayesian (BIC) and Hannan-

Quinn (HQ):

22( ) ln ( ) nAIC P p P

T

2ln( ) ln ( ) nT

BIC p p pT

22ln ln( ) ln ( ) nT

HQ p p pT

The AIC criterion asymptotically overestimates the order with positive

probability, whereas the BIC and HQ criteria estimate the order

consistently under fairly general conditions if the true order p is less than

or equal to pmax

Given these data properties, a VAR in the second differences of the non-

stationary variables was estimated. To determine the lag order of the

VAR model several order selection criteria were examined. While the

Akaike Information Criterion (AIC) and the Schwarz Criterion (SC)

indicated 2 lag, we decided to rely on the AIC and SC test results and

estimate the VAR with a constant and VAR(2) lags:

Table 4: The lag length selected by minimizing AIC is p =2:

CRITERION AR(1) AR(2) AR(3) AR(4)

40

Log

likelihood

-131.9129 -131.4327 -384.1633 -34.28664

Akaike AIC 8.495158 8.468482 22.50907 3.071480

Schwarz

SC

9.418878 9.392202 23.43279 3.995200

Impulse Responses

This section analyses the dynamic property of the model using variance

decomposition and impulse response functions. The table below

displays the impulse responses of the stock market variable (MC), oil

price, inflation, industrial production and interest rate to positive stock

market variable.

The x axis gives the time horizon or the duration of the shock whilst the

y-axis gives the direction and intensity of the impulse or the percent

variation in the dependent variable away from its base line level. The

solid line in each graph is the estimated response while the dashed lines

denote the one standard error confidence band around the estimate. It is

41

interesting to note that the error bands are typically symmetric around

the median.

Monte Carlo simulations (with one hundred draws) from the unrestricted

VAR were used to generate the standard errors for the impulse response

and variance decomposition coefficients. The confidence bands for the

response function are 90 % intervals generated by normal

approximation. There is no consensus on an explicit criterion for

significance in a VAR framework. Sims (1987) however suggests that for

impulse responses, significance can be crudely gauged by the degree to

which the function is bounded away from zero, whilst Runkle (1987)

suggests a probability range above 10 percent for variance

decompositions.

Graph 3.0 : Impulse response function

42

The market capitalization increase instantly and significantly in response

to its own shocks but reverse the sign at some late horizons. The

response of MC to OPS is of smaller magnitude. Hamilton (1983) shows

that almost all US recessions since the Second World War, have been

preceded by oil shocks. Inline with stock prices theory, the discounted

expectations of future cash-flows (dividends), which are likely to be

affected by macroeconomic movements, are possibly affected by oil

shocks. The response of the MC to INF (inflation) increases immediately

but declines at later stage.

-2000

-1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to MC

-2000

-1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to OPS

-2000

-1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to INF

-2000

-1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to PRODUCT

-2000

-1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to INTEREST

-10 -5

0 5

10 15 20

1 2 3 4 5 6 7 8 9 10

Response of OPS to MC

-10 -5 0 5

10 15 20

1 2 3 4 5 6 7 8 9

10

Response of OPS to OPS

-10 -5 0 5

10 15 20

1 2 3 4 5 6 7 8 9 10

Response of OPS to INF

-10 -5

0 5

10 15 20

1 2 3 4 5 6 7 8 9 10

Response of OPS to PRODUCT

-10 -5

0 5

10 15 20

1 2 3 4 5 6 7 8 9 10

Response of OPS to INTEREST

-10

-5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to MC

-10

-5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to OPS

-10

-5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to INF

-10 -5

0 5

10 15

1 2 3 4 5 6 7 8 9 10

Response of INF to PRODUCT

-10 -5

0 5

10 15

1 2 3 4 5 6 7 8 9 10

Response of INF to INTEREST

-15000 -10000

-5000 0

5000 10000 15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to MC

-15000 -10000

-5000 0

5000 10000 15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to OPS

-15000 -10000

-5000 0

5000 10000 15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to INF

-15000 -10000

-5000 0

5000 10000 15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to PRODUCT

-15000 -10000

-5000 0

5000 10000 15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to INTEREST

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to MC

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to OPS

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to INF

-2 -1

0 1 2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to PRODUCT

-2 -1

0 1 2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to INTEREST

Response to One S.D. Innovations ± 2 S.E.

43

The response of MC to industrial production indicates an increase

though with a little decline at a later period .this goes inline with Moreno

(2003) assertion that oil price upturn has both direct and indirect effect

respectively. This price upturn shifts the supply curve upward generating

higher incomes to owners of factors. Keynes (1936) asserts that this

monetary expansion boosts the disposable in comes of economic units

which consequently beef up aggregate demand in the system. In an

attempt to meet this increased demand, producing units expand

production. This action of business units through the multiplier effect will

lead to increases in output and employment, ceteris paribus. The

response of stock market to interest rate shows a decline at the initial

period but maintains a steady increase

Variance decompositions

The variance decomposition provides complementary information on the

dynamic behavior of the variables in the system. It is possible to

decompose the forecast variance into the contributions by each of the

different shocks. When calculated by the structural shocks, as in the

present case, the variance decomposition provides information on the

44

importance of various structural shocks explaining the forecast error

variability of the impact of oil price shock on stock market variables. The

multiple graphs are shown below:

Table 5: Variance Decomposition of MC

Perio

d

S.E. MC OPS INF PRODUC

T

INTERES

T

1

1263.65

9

100.000

0

0.00000

0

0.00000

0

0.000000 0.000000

2

1581.25

4

92.1729

1

3.65079

8

3.61492

0

0.064082 0.497290

3

2011.60

3

88.0652

0

2.62985

3

8.71402

8

0.160695 0.430227

4

2374.48

3

83.9579

8

2.51111

8

12.9666

3

0.214339 0.349933

5 0.209247 0.278279

45

2665.09

5

82.9710

3

2.38514

0

14.1563

0

6

2881.64

4

82.8218

7

2.53083

1

14.1142

6

0.209658 0.323379

7

3057.05

2

82.8184

1

2.77476

8

13.6689

5

0.225759 0.512116

8

3200.96

7

82.8049

9

3.00467

7

13.0429

0

0.273710 0.873717

9

3320.11

6

82.7002

0

3.19147

1

12.3662

2

0.331971 1.410135

10

3419.71

6

82.4641

9

3.29803

3

11.7252

3

0.397400 2.115147

Table 5 above shows the variance decomposition over the short term period (1-2 years), medium term (3-4 years) and over the long term (5-10 years). The statistics indicate the percentage contribution of innovations in each of the variables in the system to the variance of the stock market variable. The results show that shocks to the MC itself and

46

the OPS and INF accounts for most of the variability in the over all periods. The shock to MC itself increased from the beginning without a decline over its long term period. From the result table also, not much can be attributed to OPS and INF although over longer periods, its relative contribution increases. This indicates that oil price shock affects other macroeconomic variables. Table 6: Variance Decomposition of OPS

Period

S.E. MC OPS INF PRODUCT

INTEREST

1 11.81462

0.645064

99.35494

0.000000

0.000000 0.000000

2 14.81451

0.524410

96.78412

0.870604

1.493335 0.327527

3 16.56130

0.535447

94.25255

1.409749

3.056073 0.746185

4 17.60101

0.643406

93.56312

1.297840

3.618198 0.877435

5 18.04838

0.791486

91.94258

1.340574

4.455902 1.469463

6 18.47239

1.525833

89.40863

2.093079

4.533845 2.438618

7 18.91069

2.703205

85.79327

3.499154

4.492482 3.511890

8 19.40808

4.178290

81.55044

5.301468

4.379206 4.590598

9 19.92602

5.810920

77.36645

7.164089

4.212152 5.446387

10 20.44791

7.448936

73.52333

8.922545

4.030749 6.074440

47

Table 6 presents the variance decomposition of OPS (oil price shock) .The variation ranged from 99.3 per cent to 73 percent over the ten-year horizon. The result shows that the shock of the OPS to MC ranges from 0 to 7.4 percent, the shock to INF, PRODUCT, and INTEREST ranges from 0 to 8.9 percent, 4.0 and 6.0 percent respectively. Table 7: Variance Decomposition of INF:

Period

S.E. MC OPS INF PRODUCT

INTEREST

1 10.53747

1.965962

0.370248

97.66379

0.000000 0.000000

2 12.56199

5.982325

2.608951

90.25851

1.053325 0.096892

3 12.89340

6.087023

2.542497

88.61450

1.383135 1.372841

4 13.70415

8.422585

2.519136

84.52974

1.704471 2.824070

5 14.18236

10.01964

2.440014

81.88808

1.659405 3.992861

6 14.53362

11.29967

2.349115

79.96467

1.690646 4.695898

7 14.87084

12.55991

2.274645

78.34422

1.660222 5.161003

8 15.17937

13.78668

2.318423

76.81502

1.603742 5.476141

9 15.43983

14.88432

2.494180

75.44397

1.551808 5.625722

10 15.66801

15.84326

2.767336

74.22041

1.506987 5.662004

48

More importantly, the variance decomposition of the INF in table 7

shows that apart from innovations to the INF itself, MC contributes

significantly to the variations in the INF. The variance decomposition

indicates that the shock of INF to INTEREST declined during the short

term but increased in the longer term period. The shock to OPS

maintained a steady ratio.

Table 8: Variance Decomposition of PRODUCT

Period

S.E. MC OPS INF PRODUCT

INTEREST

1 13895.03

1.517802

3.377930

22.52162

72.58264 0.000000

2 14416.01

2.202649

5.859908

20.92685

68.10280 2.907790

3 14586.82

2.602182

6.455388

21.56685

66.52756 2.848017

4 14743.02

2.588915

6.422789

22.82632

65.22437 2.937609

5 14929.58

2.820435

6.370789

23.82055

63.65016 3.338069

6 14955.56

2.900891

6.353814

23.84693

63.44699 3.451373

7 14981.40

2.906405

6.362909

23.87136

63.28254 3.576781

8

15005.82

2.949825

6.362416

23.94958

63.07692 3.661263

9 15032.95

2.993767

6.400679

24.02314

62.84960 3.732817

10 15054.2

3.03704

6.43554

24.0698

62.67194 3.785611

49

4 6 7 6

The Variance Decomposition of PRODUCT (table 8) to itself shows that it accounts for the most of the variability over all periods, the shock of PRODUCT to INF increased within the medium term while the shock to OPS and INTEREST indicates that the shock decreased from the short term period and maintained its decline till the long term period.

Table 9: Variance Decomposition of INTEREST

Period S.E. MC OPS INF PRODUCT INTEREST

1 1.068361 11.03549 0.990523 6.326785 19.94147 61.70572

2 1.283751 15.30571 0.700172 4.615756 14.50681 64.87156

3 1.784360 15.74706 0.805456 25.50750 8.275232 49.66475

4 2.436250 20.67924 0.432318 38.35727 4.443382 36.08779

5 2.870722 23.60963 0.548405 40.99348 3.275018 31.57347

6 3.175618 25.62404 1.069425 41.31957 2.795235 29.19173

7 3.423572 27.19826 1.981870 41.26159 2.430073 27.12820

8 3.634332 28.50654 3.116864 40.91989 2.156679 25.30003

9 3.802999 29.65295 4.254237 40.34875 1.975395 23.76867

10 3.935440 30.61772 5.339240 39.68377 1.860413 22.49886

Equally, the variance decomposition of INTEREST indicates that the

shock to itself increased from the short term period and continued to

decline over the long term periods. The shock of INTEREST to MC and

INF shows a pattern from decreasing in the short term then increased in

the long term. The same is applicable to OPS as it ranges fro 0 to 5.3

percent in the long term.

Vector Error Correction Model (VECM)

50

A Vector error correction with two cointegration equations under is

estimated, for 2 lags. Table 10 shows their estimation

outputs. From the result table, the two cointegration equations yield the

same output regardless of which variables are included in each of them,

since they can be transformed linearly. A short look at the two lower

tables shows that almost all of the variables depend significantly on at

least one cointegration equation

Table 10: VEC with two cointegration equations

Cointegrating

Eq:

CointEq1 CointEq2

MC(-1) 1.000000 0.000000

OPS(-1) 0.000000 1.000000

INF(-1) 255.8502 -9.737480

(206.667) (4.55899)

(1.23798) (-2.13589)

51

PRODUCT(-1) -0.434344 -0.003055

(0.10087) (0.00223)

(-4.30602) (-1.37283)

INTEREST(-1) 782.7873 17.64275

(319.910) (7.05709)

(2.44690) (2.50000)

ECM(-1) -1992.669 -17.38683

(398.220) (8.78459)

(-5.00393) (-1.97924)

C -7729.323 68.07550

Included observations: 37 after adjusting endpoints

Standard errors & t-statistics in parentheses

It seems that the ECM variable is significant although it has only a slight

impact on the outcome and at least one cointegration equation is

justified. In addition the cointegration relationships provide an

opportunity of economic interpretation:

Table 11: The cointegration equations in the 2- lag-VEC model

52

Error

Correction:

D(MC) D(OPS) D(INF) D(PRODU

CT)

D(INTERE

ST)

D(ECM)

CointEq1

0.206490

-

0.001280

-

0.001559

4.166019 0.000148 -7.73E-05

(0.10202)

(0.00123)

(0.00101)

(1.25451) (9.9E-05) (9.4E-

05)

(2.02402)

(-

1.03917)

(-

1.53880)

(3.32082) (1.50621) (-

0.82393)

CointEq2

5.787884

-

0.114308

0.038781

141.5655 0.016541 -

0.000756

(5.63816)

(0.06806)

(0.05600)

(69.3313) (0.00545)

(0.00518)

(1.02656)

(-

1.67954)

(0.69255)

(2.04187) (3.03735) (-

0.14594)

If one looks, e.g., at the 2-lag VEC in table 11 above, what effect does the level of OPS have on MC? Firstly, all, the value of Z1 (sum of the components in CointEq1) has more impact on MC growth than Z2 (-0.001 vs. –0.114). And Z1 is much more (positively) influenced than Z2 is (negatively) dependent on this level. Thus a high MC level yields a high Z1 and this in return is multiplied with a negative number – so MC growth is negatively dependent on OPS levels, a productivity slowdown may be identified. Industrial production (D (PRODUCT) depend positively on the MC. Equally, D (INTEREST) depends positively on the MC

53

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 SUMMARY OF FINDINGS

The main thrust of this research work has been to investigate the

impact of oil price shocks on stock market behaviour in Nigeria.

Specifically, we analyse empirically the dynamic relationships among

stock market return, oil price shows industrial production, interest rate

and inflation rate in Nigeria.

The causality result from the vector error correction model which

indicates that causation runs from oil price shocks to stock market

returns implies that fluctuations in stock market return could be

explained by oil price shocks. Nigeria, being a petro-economy should

weigh the implications of oil price increase on stock market when

considering the revenue to be accrued from oil-price increase.

Apart from inflation rate which initially showed an inconclusive

result in the short run and later collapsed to negative impulse response

with stock market return in the longrun, there is no conclusive evidence

of a particular response of stock market return to changes in industrial

production and interest rate implying that oil price increases act like

inflation tax on consumption which are passed on the higher consumer

prices leading to decrease in company wealth and lowering their

dividends/earnings.

54

Comparing the response of other augmenting variables to shocks

in oil prices, strong evidence is found that oil price shocks only caused

little variations in most of the macroeconomic variables used in our study

while it accounts for most of its variations implying that positive

regulation of stock market activities is an important ingredient for

attractive stock market returns.

5.2 CONCLUSION AND RECOMMENDATIONS

This research was carried out on the autoregressive analysis of

the oil price shocks on stock market behavior in Nigeria. From literature

reviewed it could be said that oil price shocks affect macroeconomic

variables in Nigeria. However, there is no clear cut evidence of the

reflection of oil price shocks on the MC variable. The results of the

impulse response functions and variance decomposition analysis to a

large extent confirmed that oil price shocks are only able to explain a

small proportion of the forecast error variance of these macroeconomic

aggregates.

Though, the result of the variance decomposition indicates that MC

reacts more to its own shocks than shocks from OPS and INF, it

indicates a reasonable variation of INF to stocks from oil price though at

a later period called the longrun. The policy implication is that apart from

stock market own shocks, inflation rate should be considered when stock

market movement is taken into account. Though it is usually advised that

55

Nigeria should diversify her economy away from oil but given the

realities of the empirical findings that may not be enough. Strict

regulation of the activities of the stock market could yield more dividends

given the fact that MC reacts more to its own shocks than that generated

from other endogenous variables

56

REFERENCES

Bhide 91993) “The Hidden costs of stock market liquidity”, Journal of

Financial Economics, 34 (2) 31-51.

Financial Times, “Equities Hit by Oil Rise and geopolitical Angst”. August

21, 2006.

Financial Times, “Oil slide spurs Global Equity rally” October 12,2006.

Department of Petroleum resources Bulletin http://www. Dprnigerig.com.

Alile, Hayford (1997). “Government must divest”. The Business concord

of Nigeria, 2nd dec.; Page 8.

Nigerian Stock Exchange, http://www.Nigeriastockexchange.com

Mabro, R. (2001). Diets oil price volatility matter Oxford Energy

Comment.

Central Bank of Nigeria, Statistical Bulletin, various issues.

Central Bank of Nigeria, Annual Report and Statement of Account,

Various Issues.

Kandil, m. (2000). Macroeconomic Shocks and Dynamics in the Arab

World. IMF/University Wisconsin-Milwall Kee. WI 53201.

Iwayemi, A. (1995). Oil and the Macroeconomy: A perpective on Recent

economic Perofrmance. An NCEMA Publication. Ibadan.

Moreno, E. (2003). Oil Shocks and fiscal effects on Venezuela, 1960-

2001.

57

Bathia, D (1998): Oil price shocks and terms of trade. NEBER working

paper, 6697.

Doribusch, R. et al (2001). Macroeconomics 8th Ed. New York, MC

Graw-Hill.

Fama, E.F. (1981) “Stock Returns, real Acrivity, Inflation and Money,

“American Economic Review, 71, 545-565.

National clopaedia, Webpage, Retrieved 2008-03-08 http://www.ne.se.

Organization of petroleum Exporting Countries (OPEC) webpage

retrieved 2006, 11-15 http//www.opec.org/aboutus.

NNPC National NAPETCOR.(2OO5).Lagos.

Hamilton, J.D(2OOO),”Stock returns and inflation with supply and

demand disturbances,” Review of financial studies, 12,1203-1218.

NNPC bulletin 2006, http://www.nnpcgroup.com

Oyejide, T.A(1986) the oil boom macroeccnomic policies and Nigerian

Agricultural Analysis of a Dutch Disease phenomenon. Anaapolis,

Maryland. An international food policy reseach institute

publication.

Neary,J.P and S. Van Wijnbergen (ed) (1986). National

Resources and Macroeconomy. Cambridge. The MIT Press.

Agenor, Mabli, E . Youset (2005),Public Infrastructure and Private

Investments in the middle East and North Africa.

Policy Research Working Paper, No:3661 World Bank (july).

58

Greene, W.H. (2000) E. Geometric Analysis, 4th Ed. New

Dersey. Prentice hal inc, http//www.prenticehall.com.

Johnsen, S.(1988), „‟Statistical Analysis of Cointegrating vectors‟‟.

Journal of Economic Dynamics and Control,12.

Petterson, Kerry (2000), An introduction to Applied Econometrics. A

time series Approach, Palgive, New York.

Gujarati, D.N. (2003) Basic Econometrics Fourth Edition. Mc Graw-Hill,

Higher Education.

Varian, H.R; (2002), Intermediate Microeconomics, A Modern

Approach, Spith Edition W.W Norton and company, Inc, New York.

Yoon, K. (2004), oil price shocks and the Economy.

Retrieved 2008-03-26, http://web. Missori.edu.

Basher, S. and p. Sadorsky (2004), “Oil Price risk and emerging stock

markets‟‟, Working paper, WUSTL archive.

Hamilton, J. (1983), “Oil and the macroeconomy since world war II”,

Journal of Political economy 91,228-48.

Huang, B,N, M. Hwang, and H.P. Peng (2005) “The asymmetry of the

impact of oil price shocks on economic activities; An application of the

multivariate threshold model, energy economics 27,455-76.

World Bank Africa data, http://www.worldbank.org

International Financial statistics http://www.imf.org.

59

Huang, R., R.. Masulis, and H. Stoll (1996), “Energy shocks and financial

markets”, Jorunal of Futures Markets 16,1, 1-27.

Jones, C. and G. Kaul (1996), “Oil and the stock markets”, Journal of

Finance 51,2,463-91.

King, M., E. Sentana, and S. Wadhwani 91994), “Volatility and links

between national stock markets”, Econometrica 62,4,901-33.

Mork, K. (!994), “Business cycles an the oil market 9special issue)”

Energy Journal 15, 15-37.

Ciher, E. 92001) “Volatility spillover effects from Japan and the U.S. to

the Pacific Basin”, Journal of International Money and Finance 19,207-

33.

Officer, r. (1973), “The variability of the market factor of New York Stock

Exchange”, Journal of Business46,434-53.

Sadorsky, P. (1999), Oil price shocks and stock market activity”. Energy

economics 21, 5, 449-69.

Schwartz, G. (1989), “Why does stock market volatility change over

time?”, Journal of Finance 44, 5 1115-53.

Barsky R.B., and L. Kilian 92004) “Oil and the Macroeconoy since the

1970s”, Journal Economic Perspectives, 18,115-134.

Bermanke, B.S., M. Gertler, and M.W. Watson (1997), “Syetematic

Monetary Policy and Effects of Oil Price Shocks,” Brookings Papers on

Economic Activitity, 1,91-15.

60

Bodenstein, M., C.J. Erceg, and L. Guerrieri (2007), “Oil Shocks and

external Adjustment Mimeo, Federal reserve Board.

Campbell, J.Y. (1991), “A Variance decomposition for Stock Returns,”

Economic Journal 157-179.

Campbell, J.Y., and J.H. Cochrane (1999), “By Force of Habit: A

Consumption-Based explanation of Aggregate Stock Market Behavior,”

Journal of Political Economic 205-251.

Chen, N-F., Roll, R., and S.A. Ross 91986), “Economic Forces and the

Stock market,” of Business 59, 383-403.

Papapetrou, J.H. (2001), Asset pricing, 2nd ed., Princeton University

press: Princeton.

Kaul, G. (1987), “Stock Returns and Inflation: The Role of the Monetary

Sector,” Jorum of Financial Economics, 18,253-276.

Kaul, G., and H.N. Seyhun (1990), “Relative Price Variability, real shocks

and the Stock Market,” Journal of Finance, 45,579-496.

Kilian, L. (2008a), “Exogenous Oil Supply Shocks: How Big Area They

and How Much Matter for the US Economy?” forthcoming: Journal of the

European Ecor. Association.

Killian, L. (2007a) “Not All Oil Price Shocks Area Alike: Disentangling

Demand and Supply Shocks in the Crude Oil Market,” mimeo, University

of Michigan. Available at: http://www-

personal.umich.edu/lkilian/paperlinks.html.

61

Kilian, L. 92007b), “The economic Effects of Energy Price Shock”, invited

by the Journal of Economic Literature.

Kling, J.L. (1985), “Oil Price Shocks and Stock-Market Behavior,”

Journal of Portfolio Management, 12,34-39.

Lee, K., and S. Ni (2002), “On the Dynamic Effects of Oil Price shocks: A

Study Using Level Data,” Journal of Monetary Economics, 49,823-852.

Lettau, M., and S.C. Ludvigson 92005), “Expected Returns and

Expected Dividend growth” Journal of Financial Economics, 76(3), 583-

626.-

Johansen, S. (1995) “Estimation and Hypothesis Testing of

Cointegration Vectors in Gaussian Vector Autoregressive Models”,

Econometrica, 59, 1551-1580.

Mishkin, F. S. (2008) “Symposium on the Monetary Transmission

Mechanism” Journal of Economic Perspectives, Vol. 9, No. 4, Fall pp.

Runkle, D. E. (1987) “Vector Autoregressions and Reality,” Journal of

Business and Economic Statistics, 5 (4), 437-442.

62

Sims, C., 1987 “Bayesian Skepticism on Unit Root Econometrics,”

Journal of

Economic Dynamics and Control, 12, 463-474.

APPENDIX

Data

YEAR MC OPS Inf product INTEREST

1970 4.6 10.1 13.8 12575 3.00

63

1971 4.84 12.05 16 12180 3.00

1972 4.92 12.93 3.2 14988 3.00

1973 5.8 16.15 5.4 8210 3.00

1974 5.5 51.23 13.4 7937 3.00

1975 73 46.74 33.9 9814 4.00

1976 76 49.04 21.2 12104 4.00

1977 113 50.05 15.4 12856 4.00

1978 125 46.89 16.6 14700 5.00

1979 152 94.94 11.8 15783 5.00

1980 176.1 97.46 9.9 18561 6.00

1981 107.1 86.19 20.9 14077 6.00

1982 130.8 74.5 7.7 15903 7.50

1983 133.1 64.69 23.2 10988 7.50

1984 138.5 60.4 39.6 9702 9.50

1985 152.5 55.85 5.5 12235 9.50

1986 175.1 28.71 5.4 11777 9.50

1987 192.9 35.39 10.2 12246 14.00

1988 227.2 27.51 38.3 13945 14.50

1989 272.3 32.05 40.9 14246 16.40

1990 290.7 39.58 7.5 107969 18.80

1991 364.2 32.03 13 16354 14.29

64

1992 483.2 30.03 44.5 15620 16.10

1993 580.3 25.61 57.2 15040 16.66

1994 5889.9 23.27 57 14841 13.50

1995 5397.9 24.35 72.8 14072 12.61

1996 8111 28.72 29.3 14191 11.69

1997 9159.8 25.94 8.5 14249 4.80

1998 10814.5 17.01 10 13276 5.49

1999 13561.1 23.52 6.6 13732 5.33

2000 8111 36.08 6.9 14205 5.29

2001 9159.8 30.1 18.9 15191 5.49

2002 10814.5 30.33 12.9 16724 4.15

2003 13561.1 34.17 14 17670 4.11

2004 12188 44.17 15.4 19437 4.19

2005 12874 60.87 17.9 21267 3.83

2006 12531 70.46 8.4 20351.64 3.14

2007 12703 76.13 5.4 20809.07 3.55

2008 12617 98.5 11.5 20580.36 2.84

2009 12187.8 62.68 12.4 20694.72 2.88

Unit root test

65

ADF Test

Statistic

-4.108794 1% Critical

Value*

-2.6261

5% Critical

Value

-1.9501

10% Critical

Value

-1.6205

*MacKinnon critical values for rejection of hypothesis of

a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(MC,2)

Method: Least Squares

Date: 03/09/12 Time: 06:29

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Variable Coefficient Std.

Error

t-Statistic Prob.

D(MC(-1)) -1.050954 0.255782 -

4.108794

0.0002

D(MC(-1),2) -0.086530 0.168591 - 0.6110

66

0.513258

R-squared 0.577933 Mean

dependent var

-

11.60216

Adjusted R-

squared

0.565874 S.D. dependent

var

2410.407

S.E. of

regression

1588.174 Akaike info

criterion

17.63110

Sum squared

resid

88280418 Schwarz

criterion

17.71817

Log likelihood -324.1753 Durbin-Watson

stat

1.996762

ADF Test

Statistic

-4.010831 1% Critical

Value*

-2.6261

5% Critical

Value

-1.9501

10% Critical

Value

-1.6205

*MacKinnon critical values for rejection of hypothesis of

a unit root.

67

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(OPS,2)

Method: Least Squares

Date: 03/09/12 Time: 06:30

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Variable Coefficient Std.

Error

t-Statistic Prob.

D(OPS(-1)) -1.025400 0.255658 -

4.010831

0.0003

D(OPS(-1),2) -0.011870 0.193711 -

0.061278

0.9515

R-squared 0.470895 Mean

dependent var

-

0.991892

Adjusted R-

squared

0.455778 S.D. dependent

var

20.01952

S.E. of

regression

14.76868 Akaike info

criterion

8.275433

Sum squared 7633.989 Schwarz 8.362510

68

resid criterion

Log likelihood -151.0955 Durbin-Watson

stat

1.838993

ADF Test

Statistic

-6.170950 1% Critical

Value*

-2.6261

5% Critical

Value

-1.9501

10% Critical

Value

-1.6205

*MacKinnon critical values for rejection of hypothesis of

a unit root.

69

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(INF,2)

Method: Least Squares

Date: 03/09/12 Time: 06:30

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Variable Coefficient Std.

Error

t-Statistic Prob.

D(INF(-1)) -1.374967 0.222813 -

6.170950

0.0000

D(INF(-1),2) 0.357929 0.156498 2.287109 0.0283

R-squared 0.573944 Mean

dependent var

0.370270

Adjusted R-

squared

0.561770 S.D. dependent

var

22.22958

S.E. of

regression

14.71574 Akaike info

criterion

8.268251

Sum squared

resid

7579.356 Schwarz

criterion

8.355327

70

Log likelihood -150.9626 Durbin-Watson

stat

2.058047

ADF Test

Statistic

-7.031193 1% Critical

Value*

-2.6261

5% Critical

Value

-1.9501

10% Critical

Value

-1.6205

*MacKinnon critical values for rejection of hypothesis of

a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(PRODUCT,2)

Method: Least Squares

Date: 03/09/12 Time: 06:31

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Variable Coefficient Std. t-Statistic Prob.

71

Error

D(PRODUCT(-

1))

-1.951937 0.277611 -

7.031193

0.0000

D(PRODUCT(-

1),2)

0.308673 0.160730 1.920443 0.0630

R-squared 0.770100 Mean

dependent var

-

72.80114

Adjusted R-

squared

0.763532 S.D. dependent

var

37888.00

S.E. of

regression

18424.17 Akaike info

criterion

22.53325

Sum squared

resid

1.19E+10 Schwarz

criterion

22.62033

Log likelihood -414.8652 Durbin-Watson

stat

2.135593

ADF Test

Statistic

-3.920838 1% Critical

Value*

-2.6261

5% Critical

Value

-1.9501

72

10% Critical

Value

-1.6205

*MacKinnon critical values for rejection of hypothesis of

a unit root.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(INTEREST,2)

Method: Least Squares

Date: 03/09/12 Time: 06:32

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Variable Coefficient Std.

Error

t-Statistic Prob.

D(INTEREST(-

1))

-0.961528 0.245235 -

3.920838

0.0004

D(INTEREST(-

1),2)

-0.092464 0.168648 -

0.548268

0.5870

R-squared 0.533736 Mean

dependent var

0.001081

Adjusted R- 0.520414 S.D. dependent 2.720817

73

squared var

S.E. of

regression

1.884223 Akaike info

criterion

4.157446

Sum squared

resid

124.2604 Schwarz

criterion

4.244523

Log likelihood -74.91276 Durbin-Watson

stat

2.088515

Lag length selection

Date: 03/09/12 Time: 14:21

Sample(adjusted): 1974 2009

Included observations: 36 after adjusting endpoints

Standard errors & t-statistics in parentheses

MC OPS INF PRODUCT INTEREST

MC(-1) 0.483867

0.004524

0.001966

2.280218 5.57E-05

(0.18966)

(0.00262)

(0.00258)

(2.89108) (0.00017)

(0.78871) (0.32009)

74

(2.55121) (1.72795) (0.76114)

MC(-2) 0.090428 -

0.000698

-

0.001707

0.919725 0.000200

(0.16241)

(0.00224)

(0.00221)

(2.47562) (0.00015)

(0.55680)

(-

0.31135)

(-

0.77158)

(0.37151) (1.34428)

MC(-3) 0.097481 -

0.002331

-

0.000595

-0.752525 -0.000371

(0.17610)

(0.00243)

(0.00240)

(2.68436) (0.00016)

(0.55355)

(-

0.95913)

(-

0.24818)

(-0.28034) (-2.29613)

MC(-4) 0.356826 -

0.000922

-

0.000555

-0.973558 8.59E-05

(0.18570)

(0.00256)

(0.00253)

(2.83075) (0.00017)

(- (- (-0.34392) (0.50437)

75

(1.92147) 0.35977) 0.21945)

OPS(-1) -

19.89678

0.405645

0.168419

224.1617 0.014603

(21.0074)

(0.28998)

(0.28614)

(320.223) (0.01926)

(-

0.94713)

(1.39887)

(0.58859)

(0.70002) (0.75824)

OPS(-2) 11.05759

0.095810

-

0.244914

-135.4494 -0.016803

(24.1390)

(0.33321)

(0.32879)

(367.959) (0.02213)

(0.45808)

(0.28754)

(-

0.74489)

(-0.36811) (-0.75929)

OPS(-3) -

8.944024

0.026915

-

0.098231

280.4803 0.035353

(23.1974)

(0.32021)

(0.31597)

(353.606) (0.02127)

(- (- (0.79320) (1.66239)

76

0.38556) (0.08405) 0.31089)

OPS(-4) 4.765348 -

0.208680

0.148384

-483.1829 -0.018125

(19.2594)

(0.26585)

(0.26233)

(293.577) (0.01766)

(0.24743)

(-

0.78495)

(0.56564)

(-1.64584) (-1.02653)

INF(-1) 19.72046

0.306180

0.328718

-289.1214 -0.004861

(20.1456)

(0.27808)

(0.27440)

(307.086) (0.01847)

(0.97890)

(1.10103)

(1.19795)

(-0.94150) (-0.26322)

INF(-2) 34.65716 -

0.062342

-

0.426656

103.5997 -0.107594

(23.5808)

(0.32550)

(0.32119)

(359.451) (0.02162)

(- (- (0.28822) (-4.97699)

77

(1.46972) 0.19153) 1.32835)

INF(-3) -

17.79092

0.501485

0.113874

-244.9118 0.026384

(37.2008)

(0.51351)

(0.50671)

(567.065) (0.03410)

(-

0.47824)

(0.97658)

(0.22473)

(-0.43189) (0.77363)

INF(-4) 58.93154

0.732208

-

0.373216

69.52405 -0.041812

(35.8253)

(0.49452)

(0.48797)

(546.097) (0.03284)

(1.64497)

(1.48063)

(-

0.76483)

(0.12731) (-1.27306)

PRODUCT(-

1)

0.017622

0.000153

-

0.000112

-0.347303 -5.62E-05

(0.02173)

(0.00030)

(0.00030)

(0.33128) (2.0E-05)

(- (-1.04836) (-2.81880)

78

(0.81084) (0.51047) 0.37736)

PRODUCT(-

2)

-

0.011793

0.000259

0.000134

-0.233895 -2.71E-05

(0.01809)

(0.00025)

(0.00025)

(0.27579) (1.7E-05)

(-

0.65183)

(1.03737)

(0.54329)

(-0.84808) (-1.63438)

PRODUCT(-

3)

-

0.029806

0.000440

0.000257

-0.287141 -2.15E-05

(0.01956)

(0.00027)

(0.00027)

(0.29819) (1.8E-05)

(-

1.52368)

(1.62789)

(0.96599)

(-0.96295) (-1.19658)

PRODUCT(-

4)

0.068911

0.000535

0.000171

0.169811 -1.88E-05

(0.02341)

(0.00032)

(0.00032)

(0.35680) (2.1E-05)

(0.47593) (-0.87457)

79

(2.94406) (1.65650) (0.53766)

INTEREST(-

1)

-

254.1331

5.650645

-

0.667953

3613.607 0.958370

(262.702)

(3.62627)

(3.57823)

(4004.45) (0.24084)

(-

0.96738)

(1.55825)

(-

0.18667)

(0.90240) (3.97933)

INTEREST(-

2)

-

11.86214

0.141849

0.897124

716.9550 0.168307

(305.513)

(4.21722)

(4.16135)

(4657.04) (0.28008)

(-

0.03883)

(0.03364)

(0.21559)

(0.15395) (0.60091)

INTEREST(-

3)

527.2021 -

5.055127

0.385006

4557.612 0.322011

(225.990)

(3.11951)

(3.07818)

(3444.84) (0.20718)

(- (1.32302) (1.55425)

80

(2.33286) 1.62049) (0.12508)

INTEREST(-

4)

-

354.0908

-

6.652102

0.761240

-6183.635 -0.184737

(272.433)

(3.76060)

(3.71078)

(4152.79) (0.24976)

(-

1.29973)

(-

1.76889)

(0.20514)

(-1.48903) (-0.73966)

C -

665.4996

19.25583

13.05028

11065.84 1.952617

(916.785)

(12.6551)

(12.4874)

(13974.9) (0.84048)

(-

0.72591)

(1.52159)

(1.04508)

(0.79184) (2.32321)

R-squared 0.984237

0.826191

0.683262

0.554272 0.982561

Adj. R-

squared

0.963220

0.594446

0.260945

-0.040033 0.959309

Sum sq.

resids

16848342

3210.345

3125.837

3.91E+09 14.16049

81

S.E.

equation

1059.822

14.62952

14.43569

16155.24 0.971613

F-statistic 46.83082

3.565082

1.617887

0.932639 42.25747

Log

likelihood

-

286.0942

-

131.9129

-

131.4327

-384.1633 -34.28664

Akaike AIC 17.06079

8.495158

8.468482

22.50907 3.071480

Schwarz SC 17.98451

9.418878

9.392202

23.43279 3.995200

Mean

dependent

4823.608

47.64417

20.66667

17587.44 7.892778

S.D.

dependent

5526.242

22.97237

16.79187

15841.26 4.816664

Determinant Residual

Covariance

4.87E+16

Log Likelihood -

947.0626

Akaike Information

Criteria

58.44792

Schwarz Criteria

82

63.06652

Date: 03/09/12 Time: 06:38

Sample(adjusted): 1972 2009

Included observations: 38 after adjusting endpoints

Standard errors & t-statistics in parentheses

MC OPS INF PRODUCT INTEREST

MC(-1) 0.582338

0.001095

0.001466

-0.259846 -0.000131

(0.17915)

(0.00168)

(0.00149)

(1.96996) (0.00015)

(3.25048)

(0.65364)

(0.98115)

(-0.13190) (-0.86348)

MC(-2) 0.433341 -

0.001080

-

0.001858

0.884946 5.10E-05

(0.18450)

(0.00172)

(0.00154)

(2.02872) (0.00016)

(2.34875)

(-

0.62606)

(-

1.20784)

(0.43621) (0.32672)

83

OPS(-1) -

23.43060

0.708180

0.213939

219.8058 0.000480

(21.9631)

(0.20534)

(0.18315)

(241.503) (0.01857)

(-

1.06682)

(3.44874)

(1.16813)

(0.91016) (0.02587)

OPS(-2) 17.48966 -

0.000759

-

0.198324

-203.9284 0.012292

(21.8108)

(0.20392)

(0.18188)

(239.828) (0.01844)

(0.80188)

(-

0.00372)

(-

1.09043)

(-0.85031) (0.66658)

INF(-1) 26.69099

0.229864

0.490730

-54.01115 0.000440

(23.0350)

(0.21537)

(0.19209)

(253.290) (0.01947)

(1.15871)

(1.06732)

(2.55475)

(-0.21324) (0.02261)

84

INF(-2) 22.31560 -

0.044432

-

0.502748

126.5895 -0.089707

(23.1836)

(0.21676)

(0.19332)

(254.923) (0.01960)

(0.96256)

(-

0.20499)

(-

2.60054)

(0.49658) (-4.57677)

PRODUCT(-

1)

0.001973

0.000112

-9.01E-05 -0.217841 -3.80E-05

(0.02102)

(0.00020)

(0.00018)

(0.23112) (1.8E-05)

(0.09389)

(0.57102)

(-

0.51423)

(-0.94254) (-2.14113)

PRODUCT(-

2)

-

0.003266

0.000248

-2.27E-05 0.041320 -2.82E-05

(0.02270)

(0.00021)

(0.00019)

(0.24959) (1.9E-05)

(-

0.14389)

(1.16938)

(-

0.11981)

(0.16555) (-1.47087)

85

INTEREST(-

1)

-

132.8696

1.010253

-

0.465931

2929.179 0.719681

(181.707)

(1.69887)

(1.51523)

(1998.02) (0.15362)

(-

0.73123)

(0.59466)

(-

0.30750)

(1.46604) (4.68469)

INTEREST(-

2)

119.2771 -

2.891666

2.545361

-1596.951 0.428638

(199.966)

(1.86959)

(1.66749)

(2198.80) (0.16906)

(0.59649)

(-

1.54668)

(1.52646)

(-0.72628) (2.53540)

C -

148.6321

18.55315

5.992737

5124.333 1.583355

(904.882)

(8.46022)

(7.54568)

(9949.97) (0.76503)

(-

0.16426)

(2.19299)

(0.79419)

(0.51501) (2.06966)

86

R-squared 0.945474

0.741913

0.593454

0.173234 0.949411

Adj. R-

squared

0.925280

0.646325

0.442882

-0.132975 0.930675

Sum sq.

resids

60679701

5304.237

4219.454

7.34E+09 43.37300

S.E.

equation

1499.132

14.01618

12.50104

16484.25 1.267441

F-statistic 46.81795

7.761592

3.941323

0.565738 50.67174

Log

likelihood

-

325.3068

-

147.7545

-

143.4073

-416.4125 -56.43243

Akaike AIC 17.70036

8.355499

8.126699

22.49540 3.549075

Schwarz SC 18.17440

8.829538

8.600737

22.96943 4.023113

Mean

dependent

4570.016

45.90184

19.80526

17272.26 7.635263

S.D.

dependent

5484.289

23.56827

16.74838

15486.71 4.813740

Determinant Residual

87

Covariance 2.37E+18

Log Likelihood -

1073.480

Akaike Information

Criteria

59.39368

Schwarz Criteria

61.76387

Error correction model

Date: 03/09/12 Time: 07:34

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Standard errors & t-statistics in parentheses

Cointegratin

g Eq:

CointEq

1

CointEq

2

MC(-1)

1.00000

0

0.0000

00

OPS(-1)

88

0.00000

0

1.0000

00

INF(-1)

255.850

2

-

9.7374

80

(206.66

7)

(4.5589

9)

(1.2379

8)

(-

2.1358

9)

PRODUCT(

-1)

-

0.43434

4

-

0.0030

55

(0.1008

7)

(0.0022

3)

(-

4.30602

(-

1.3728

89

) 3)

INTEREST(

-1)

782.787

3

17.642

75

(319.91

0)

(7.0570

9)

(2.4469

0)

(2.5000

0)

ECM(-1) -

1992.66

9

-

17.386

83

(398.22

0)

(8.7845

9)

(-

5.00393

)

(-

1.9792

4)

90

C -

7729.32

3

68.075

50

Error

Correction:

D(MC) D(OPS) D(INF) D(PRODU

CT)

D(INTERE

ST)

D(ECM

)

CointEq1

0.20649

0

-

0.0012

80

-

0.0015

59

4.166019 0.000148 -7.73E-

05

(0.1020

2)

(0.0012

3)

(0.0010

1)

(1.25451) (9.9E-05) (9.4E-

05)

(2.0240

2)

(-

1.0391

7)

(-

1.5388

0)

(3.32082) (1.50621) (-

0.8239

3)

CointEq2

5.78788

4

-

0.1143

08

0.0387

81

141.5655 0.016541 -

0.0007

56

(5.6381

(0.0680

(0.0560

(69.3313) (0.00545)

(0.0051

91

6) 6) 0) 8)

(1.0265

6)

(-

1.6795

4)

(0.6925

5)

(2.04187) (3.03735) (-

0.1459

4)

D(MC(-1)) -

0.70543

6

0.0014

53

0.0023

84

-2.836954 -0.000125 -2.30E-

05

(0.2148

7)

(0.0025

9)

(0.0021

3)

(2.64220) (0.00021)

(0.0002

0)

(-

3.28310

)

(0.5603

0)

(1.1171

7)

(-1.07371) (-0.60415) (-

0.1162

5)

D(MC(-2)) -

0.37263

7

0.0002

88

0.0003

74

0.252870 0.000225 -

0.0001

57

(0.2077

6)

(0.0025

1)

(0.0020

6)

(2.55479) (0.00020)

(0.0001

9)

92

(-

1.79359

)

(0.1148

5)

(0.1810

8)

(0.09898) (1.12300) (-

0.8203

2)

D(OPS(-1)) -

30.2434

8

0.1743

31

0.1020

13

89.07277 -0.010935 -

0.0179

07

(21.348

9)

(0.2577

1)

(0.2120

3)

(262.522) (0.02062)

(0.0196

3)

(-

1.41663

)

(0.6764

7)

(0.4811

1)

(0.33930) (-0.53031) (-

0.9123

4)

D(OPS(-2)) -

18.2700

4

-

0.0058

92

-

0.0595

14

-200.6477 -0.020612

0.0063

01

(21.870

0)

(0.2640

0)

(0.2172

1)

(268.931) (0.02112)

(0.0201

1)

(- (- (- (-0.74609) (-0.97576)

93

0.83539

)

0.0223

2)

0.2739

9)

(0.3134

0)

D(INF(-1))

14.4396

6

-

0.2106

25

0.4912

72

-21.66924 0.114289

0.0189

80

(34.977

5)

(0.4222

2)

(0.3473

9)

(430.111) (0.03378)

(0.0321

6)

(0.4128

3)

(-

0.4988

5)

(1.4141

7)

(-0.05038) (3.38293)

(0.5902

3)

D(INF(-2))

34.8779

3

-

0.5680

16

0.1695

92

-68.23677 0.000992

0.0446

64

(38.863

7)

(0.4691

3)

(0.3859

9)

(477.898) (0.03754)

(0.0357

3)

(0.8974

(-

1.2107

(0.4393

(-0.14279) (0.02642)

(1.2500

94

4) 9) 7) 8)

D(PRODUC

T(-1))

0.07955

7

-

0.0003

94

-

0.0002

14

0.392784 6.75E-05 2.45E-

05

(0.0454

1)

(0.0005

5)

(0.0004

5)

(0.55834) (4.4E-05) (4.2E-

05)

(1.7521

7)

(-

0.7189

2)

(-

0.4751

7)

(0.70349) (1.54021)

(0.5875

5)

D(PRODUC

T(-2))

0.04798

1

-

0.0001

97

-4.59E-

05

-0.179975 1.69E-05 4.30E-

05

(0.0345

3)

(0.0004

2)

(0.0003

4)

(0.42460) (3.3E-05) (3.2E-

05)

(1.3895

7)

(-

0.4736

8)

(-

0.1337

2)

(-0.42387) (0.50603)

(1.3558

8)

95

D(INTERES

T(-1))

-

735.980

1

6.9153

46

-

1.3181

08

-3485.851 -0.592670 -

0.3177

31

(330.78

1)

(3.9929

0)

(3.2852

7)

(4067.54) (0.31949)

(0.3041

0)

(-

2.22498

)

(1.7319

1)

(-

0.4012

2)

(-0.85699) (-1.85502) (-

1.0448

2)

D(INTERES

T(-2))

-

717.021

4

3.0191

83

1.9838

55

-4754.436 -0.159989 -

0.0834

22

(255.96

4)

(3.0897

8)

(2.5422

0)

(3147.53) (0.24723)

(0.2353

2)

(-

2.80126

)

(0.9771

5)

(0.7803

7)

(-1.51053) (-0.64713) (-

0.3545

1)

96

D(ECM(-1)) -

122.745

3

3.3424

20

3.0943

83

-1405.157 0.365154 -

0.1466

64

(459.15

2)

(5.5424

9)

(4.5602

4)

(5646.10) (0.44349)

(0.4221

2)

(-

0.26733

)

(0.6030

5)

(0.6785

6)

(-0.24887) (0.82337) (-

0.3474

5)

D(ECM(-2)) -

205.836

8

0.0148

73

4.2277

21

-10874.50 -0.042196

0.6558

86

(460.26

4)

(5.5559

1)

(4.5712

8)

(5659.77) (0.44456)

(0.4231

4)

(-

0.44721

)

(0.0026

8)

(0.9248

4)

(-1.92137) (-0.09492)

(1.5500

4)

C - - 3261.978 -0.028485

97

843.350

4

0.1895

54

1.8992

40

0.1289

89

(278.24

9)

(3.3587

8)

(2.7635

3)

(3421.56) (0.26875)

(0.2558

1)

(3.0309

2)

(-

0.0564

4)

(-

0.6872

5)

(0.95336) (-0.10599)

(0.5042

5)

R-squared

0.53006

5

0.2137

53

0.5370

76

0.643051 0.696765

0.4297

08

Adj. R-

squared

0.23101

5

-

0.2865

86

0.2424

88

0.415902 0.503798

0.0667

96

Sum sq.

resids

408805

24

5956.8

06

4032.5

38

6.18E+09 38.13846

34.551

95

S.E.

equation

1363.16

0

16.454

91

13.538

73

16762.49 1.316650

1.2532

13

F-statistic 2.830964 3.610788

98

1.77249

6

0.4272

16

1.8231

42

1.1840

54

Log

likelihood

-

309.932

8

-

146.50

61

-

139.28

85

-402.7783 -53.06137 -

51.234

32

Akaike AIC

17.5639

3

8.7300

60

8.3399

21

22.58261 3.678993

3.5802

34

Schwarz

SC

18.2170

1

9.3831

34

8.9929

96

23.23569 4.332068

4.2333

09

Mean

dependent

329.267

0

1.3445

95

0.2486

49

154.2355 -0.003243

0.1307

12

S.D.

dependent

1554.49

0

14.506

94

15.555

47

21932.88 1.869135

1.2972

88

Determinant

Residual Covariance

7.11E+

16

Log Likelihood -

99

1032.8

59

Akaike Information

Criteria

61.343

74

Schwarz Criteria

65.784

65

Date: 03/09/12 Time: 06:40

Sample(adjusted): 1973 2009

Included observations: 37 after adjusting endpoints

Standard errors & t-statistics in parentheses

Cointegratin

g Eq:

CointEq

1

MC(-1)

1.00000

0

100

OPS(-1)

39.2476

8

(11.753

3)

(3.3392

8)

INF(-1) -

126.323

3

(36.602

1)

(-

3.45126

)

PRODUCT( -

101

-1) 0.55423

5

(0.0645

1)

(-

8.59154

)

INTEREST(

-1)

1475.22

4

(159.64

6)

(9.2406

1)

ECM(-1) -

2675.06

102

1

(150.28

3)

(-

17.8002

)

C -

5057.51

7

Error

Correction:

D(MC) D(OPS) D(INF) D(PRODU

CT)

D(INTERE

ST)

D(ECM

)

CointEq1

0.20484

2

-

0.0013

25

-

0.0014

88

4.150413 0.000156 -7.57E-

05

(0.1004

6)

(0.0012

5)

(0.0011

2)

(1.23175) (0.00011) (9.2E-

05)

(2.0389

(-

1.0608

(-

1.3298

(3.36953) (1.40179) (-

0.8183

103

8) 5) 9) 2)

D(MC(-1)) -

0.68347

0

0.0020

61

0.0014

36

-2.628886 -0.000227 -4.46E-

05

(0.2082

1)

(0.0025

9)

(0.0023

2)

(2.55277) (0.00023)

(0.0001

9)

(-

3.28264

)

(0.7960

0)

(0.6192

5)

(-1.02982) (-0.98397) (-

0.2325

1)

D(MC(-2)) -

0.34996

1

0.0009

15

-

0.0006

05

0.467667 0.000120 -

0.0001

79

(0.2008

5)

(0.0025

0)

(0.0022

4)

(2.46252) (0.00022)

(0.0001

8)

(-

1.74244

)

(0.3665

0)

(-

0.2704

8)

(0.18991) (0.54125) (-

0.9682

6)

104

D(OPS(-1)) -

33.6653

7

0.0796

65

0.2497

03

56.65982 0.004895 -

0.0145

44

(20.173

8)

(0.2508

7)

(0.2246

9)

(247.346) (0.02235)

(0.0185

7)

(-

1.66876

)

(0.3175

6)

(1.1113

0)

(0.22907) (0.21897) (-

0.7833

5)

D(OPS(-2)) -

17.7201

3

0.0093

21

-

0.0832

49

-195.4389 -0.023156

0.0057

61

(21.523

9)

(0.2676

6)

(0.2397

3)

(263.898) (0.02385)

(0.0198

1)

(-

0.82328

)

(0.0348

2)

(-

0.3472

6)

(-0.74058) (-0.97092)

(0.2908

3)

105

D(INF(-1))

29.3973

0

0.2031

77

-

0.1543

08

120.0135 0.045094

0.0042

79

(22.627

3)

(0.2813

8)

(0.2520

2)

(277.427) (0.02507)

(0.0208

2)

(1.2992

0)

(0.7220

8)

(-

0.6122

8)

(0.43260) (1.79862)

(0.2055

1)

D(INF(-2))

52.5667

7

-

0.0786

56

-

0.5938

69

99.31660 -0.080838

0.0272

80

(22.832

9)

(0.2839

3)

(0.2543

1)

(279.947) (0.02530)

(0.0210

1)

(2.3022

4)

(-

0.2770

2)

(-

2.3352

1)

(0.35477) (-3.19523)

(1.2982

5)

D(PRODUC - - 0.465767 3.19E-05 1.70E-

106

T(-1)) 0.08726

2

0.0001

81

0.0005

47

05

(0.0426

8)

(0.0005

3)

(0.0004

8)

(0.52329) (4.7E-05) (3.9E-

05)

(2.0445

5)

(-

0.3408

1)

(-

1.1503

2)

(0.89007) (0.67463)

(0.4316

3)

D(PRODUC

T(-2))

0.05585

9

2.05E-

05

-

0.0003

86

-0.105351 -1.96E-05 3.53E-

05

(0.0311

4)

(0.0003

9)

(0.0003

5)

(0.38181) (3.5E-05) (2.9E-

05)

(1.7937

5)

(0.0529

7)

(-

1.1125

5)

(-0.27593) (-0.56711)

(1.2317

1)

D(INTERES

T(-1))

-

865.691

3.3268

4.2803

-4714.511 0.007383 -

0.1902

107

4 98 06 52

(235.37

4)

(2.9269

6)

(2.6215

8)

(2885.86) (0.26080)

(0.2166

1)

(-

3.67793

)

(1.1366

4)

(1.6327

2)

(-1.63366) (0.02831) (-

0.8783

1)

D(INTERES

T(-2))

-

802.687

5

0.6492

38

5.6812

57

-5565.888 0.236308

0.0007

70

(203.55

0)

(2.5312

1)

(2.2671

2)

(2495.67) (0.22554)

(0.1873

2)

(-

3.94345

)

(0.2564

9)

(2.5059

4)

(-2.23022) (1.04774)

(0.0041

1)

D(ECM(-1)) -

119.542

5

3.4310

25

2.9561

48

-1374.819 0.350338 -

0.1498

11

108

(452.29

5)

(5.6244

4)

(5.0376

2)

(5545.46) (0.50116)

(0.4162

4)

(-

0.26430

)

(0.6100

2)

(0.5868

1)

(-0.24792) (0.69906) (-

0.3599

1)

D(ECM(-2)) -

144.952

9

1.6992

18

1.5999

38

-10297.79 -0.323848

0.5960

50

(440.91

3)

(5.4829

0)

(4.9108

5)

(5405.91) (0.48855)

(0.4057

7)

(-

0.32876

)

(0.3099

1)

(0.3258

0)

(-1.90491) (-0.66288)

(1.4689

4)

C

827.146

2

-

0.6378

43

-

1.1998

55

3108.487 0.046477

0.1449

15

(3343.06) (0.30212)

109

(272.66

4)

(3.3906

7)

(3.0369

1)

(0.2509

3)

(3.0335

7)

(-

0.1881

2)

(-

0.3950

9)

(0.92983) (0.15384)

(0.5775

1)

R-squared

0.52319

7

0.1534

00

0.4093

15

0.639956 0.595110

0.4201

84

Adj. R-

squared

0.25369

9

-

0.3251

13

0.0754

50

0.436453 0.366259

0.0924

62

Sum sq.

resids

414779

59

6414.0

51

5145.4

62

6.24E+09 50.92385

35.128

99

S.E.

equation

1342.90

3

16.699

45

14.957

13

16464.97 1.487979

1.2358

59

F-statistic

1.94138

0

0.3205

77

1.2259

89

3.144697 2.600425

1.2821

36

Log - - - -402.9380 -58.40987 -

110

likelihood 310.201

2

147.87

43

143.79

73

51.540

74

Akaike AIC

17.5243

9

8.7499

62

8.5295

86

22.53719 3.914047

3.5427

43

Schwarz

SC

18.1339

3

9.3594

99

9.1391

23

23.14673 4.523584

4.1522

79

Mean

dependent

329.267

0

1.3445

95

0.2486

49

154.2355 -0.003243

0.1307

12

S.D.

dependent

1554.49

0

14.506

94

15.555

47

21932.88 1.869135

1.2972

88

Determinant

Residual Covariance

1.78E+

17

Log Likelihood -

1049.8

78

Akaike Information

111

Criteria 61.615

01

Schwarz Criteria

65.533

45

The interpretation of the cointegration relationships seems somewhat

easier,

because one is more used to handle its input variables. E.g. a Keynesian

effect can be interpreted into the relationship between monetary growth

and the change of the

GNP growth: Higher monetary growth increases the growth of GDP

growth by both

cointegration equations,7 although this effect does not look very

significant. And the

reverse seems even more true: the higher GNP growth, and the lower

the inflation,

the more monetary expansion is stepped up (and vv.) – an effect maybe

reflecting

monetary policy.

112

Impulse response function

- 2000

- 1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to MC

- 2000

- 1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to OPS

- 2000

- 1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to INF

- 2000

- 1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to PRODUCT

- 2000

- 1000

0

1000

2000

1 2 3 4 5 6 7 8 9 10

Response of MC to INTEREST

- 10

- 5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Response of OPS to MC

- 10

- 5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Response of OPS to OPS

- 10

- 5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Response of OPS to INF

- 10

- 5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Response of OPS to PRODUCT

- 10

- 5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Response of OPS to INTEREST

- 10

- 5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to MC

- 10

- 5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to OPS

- 10

- 5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to INF

- 10

- 5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to PRODUCT

- 10

- 5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Response of INF to INTEREST

- 15000

- 10000

- 5000

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to MC

- 15000

- 10000

- 5000

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to OPS

- 15000

- 10000

- 5000

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to INF

- 15000

- 10000

- 5000

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to PRODUCT

- 15000

- 10000

- 5000

0

5000

10000

15000

1 2 3 4 5 6 7 8 9 10

Response of PRODUCT to INTEREST

- 2

- 1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to MC

- 2

- 1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to OPS

- 2

- 1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to INF

- 2

- 1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to PRODUCT

- 2

- 1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of INTEREST to INTEREST

Response to One S.D. I nnovat ions ± 2 S.E.

Response table

Response

of MC:

Period MC OPS INF PRODUCT INTEREST

1

1263.659

0.000000

0.000000

0.000000 0.000000

113

(153.983)

(0.00000)

(0.00000)

(0.00000) (0.00000)

2

841.3233

-

302.1312

300.6429

-40.02854 -111.5081

(242.006)

(244.172)

(239.224)

(252.730) (140.702)

3

1122.024

-

123.0240

512.0848

-70.00226 -70.53561

(241.452)

(294.556)

(267.201)

(298.332) (119.952)

4

1081.710

-

187.5178

615.1939

-74.71411 -48.17141

(324.383)

(335.262)

(348.386)

(332.858) (169.685)

5

1076.809

-

166.8205

523.8354

-52.70191 5.969183

(369.844)

(428.087)

(395.819)

(336.003) (205.987)

6

992.0742

-

201.8585

408.1007

-50.47204 84.18765

(379.349) (258.952)

114

(432.609) (427.819) (470.619)

7

928.6686

-

221.7222

324.6687

-60.73511 144.9384

(526.469)

(506.085)

(530.554)

(357.442) (305.054)

8

862.8492

-

220.3343

242.8179

-83.34464 204.1142

(605.552)

(517.384)

(613.940)

(373.854) (366.377)

9

794.8767

-

209.6100

163.5528

-92.46048 256.7467

(712.631)

(585.796)

(705.688)

(370.813) (422.605)

10

726.3303

-

184.0811

89.73579

-99.39815 303.1723

(827.478)

(599.188)

(815.051)

(407.428) (486.742)

Response

of OPS:

Period MC OPS INF PRODUCT INTEREST

1 - 0.000000 0.000000

115

0.948902 11.77645 0.000000

(2.01501)

(1.41871)

(0.00000)

(0.00000) (0.00000)

2

0.500508

8.586452

1.382285

1.810365 0.847834

(2.56231)

(2.91910)

(2.08608)

(2.24702) (1.29037)

3

0.563636

6.789765

1.398535

2.259353 -1.152298

(2.16104)

(2.69278)

(2.38124)

(2.26597) (1.11699)

4

0.724317

5.598338

-

0.392475

1.681342 -0.819537

(1.99784)

(2.78664)

(2.67562)

(2.07805) (1.28821)

5

0.764836

3.105338

0.588378

1.818192 -1.438205

(2.21443)

(3.22037)

(2.86286)

(1.98216) (1.36116)

6

1.621225

2.364508

1.665940

0.977736 -1.880050

116

(2.22301)

(3.21894)

(2.57951)

(2.10685) (1.54778)

7

2.111979

1.311695

2.317601

0.771331 -2.058577

(2.42446)

(3.55807)

(2.58440)

(1.87077) (1.60355)

8

2.464035

0.608324

2.730523

0.655405 -2.175442

(2.71164)

(3.22870)

(2.81316)

(2.21755) (1.76265)

9

2.708049

0.041949

2.911276

0.478424 -2.081613

(3.06309)

(3.57988)

(3.01754)

(2.08014) (1.88541)

10

2.841345

-

0.482747

2.976902

0.359250 -1.942576

(3.31374)

(3.36902)

(3.15736)

(2.46531) (2.12665)

Response

of INF:

Period MC OPS INF PRODUCT INTEREST

117

1

1.477488

-

0.641184

10.41365

0.000000 0.000000

(1.74568)

(1.95231)

(1.42300)

(0.00000) (0.00000)

2

2.693949

1.925073

5.829843

-1.289258 -0.391023

(2.32544)

(2.55133)

(1.82736)

(1.96033) (1.03723)

3 -

0.823848

0.331091

-

2.209375

0.798208 1.459215

(1.48490)

(2.14777)

(2.11843)

(2.17675) (1.00021)

4 -

2.387233

-

0.710205

-

3.381933

0.949600 1.738247

(1.77403)

(2.04083)

(2.34381)

(1.32335) (0.83064)

5 -

2.082189

-

0.420469

-

2.441129

-0.369669 1.651516

(1.30837)

(1.67155)

(2.13308)

(1.24451) (0.79050)

6 - - - -0.483081 1.373952

118

1.927276 0.232627 2.048671

(1.07183)

(1.50747)

(1.62762)

(1.27274) (0.61239)

7 -

1.976713

0.261245

-

2.084630

-0.316802 1.222367

(1.00788)

(1.48111)

(1.58743)

(1.29468) (0.59614)

8 -

1.997776

0.558357

-

1.933953

-0.154232 1.097552

(1.05492)

(1.46104)

(1.43643)

(1.13119) (0.59554)

9 -

1.927734

0.777097

-

1.690444

-0.063991 0.890679

(1.07495)

(1.29636)

(1.40803)

(1.18115) (0.68694)

10 -

1.846778

0.920655

-

1.533456

0.011002 0.698851

(1.16597)

(1.37677)

(1.41266)

(0.93289) (0.73525)

Response

of

119

PRODUCT:

Period MC OPS INF PRODUCT INTEREST

1 -

1711.855

2553.787

-

6594.156

11837.93 0.000000

(2322.34)

(2420.40)

(2507.68)

(1560.68) (0.00000)

2 -

1283.404

2378.299

86.87871

-1181.318 2458.253

(2604.58)

(2751.31)

(2469.99)

(2385.50) (1457.98)

3

979.4029

-

1247.936

1548.710

148.9359 129.8944

(1960.41)

(2157.98)

(2144.74)

(2852.45) (1430.03)

4 -

300.6290

474.2236

-

1930.181

-463.9779 570.2747

(1587.60)

(2310.71)

(2640.46)

(1564.54) (984.777)

5 -

812.0068

-

489.5286

-

1865.380

319.0768 1027.234

(1725.37) (822.591)

120

(1381.96) (1747.23) (1702.02)

6 -

449.2799

-

107.2235

-

493.9182

-199.4424 528.5311

(1301.32)

(1850.03)

(1666.38)

(1529.17) (762.541)

7 -

186.5876

263.7450

-

489.1747

-348.8142 555.1264

(1131.26)

(1282.12)

(1351.66)

(1450.70) (899.724)

8 -

345.0388

213.2607

-

592.3813

-18.92642 465.2117

(1151.94)

(1525.95)

(1342.77)

(1339.99) (791.014)

9 -

351.2046

371.9185

-

601.1827

-18.66508 437.6552

(1104.13)

(1403.57)

(1488.74)

(1279.94) (901.822)

10 -

342.4396

346.4518

-

509.7074

3.374172 378.8965

(1296.74)

(1728.72)

(1449.54)

(1411.63) (926.648)

121

Response

of

INTEREST:

Period MC OPS INF PRODUCT INTEREST

1 -

0.354906

0.106329

-

0.268726

0.477086 0.839229

(0.16044)

(0.15543)

(0.15973)

(0.15044) (0.10135)

2 -

0.355362

-

0.015270

0.062088

-0.107069 0.603977

(0.24280)

(0.25447)

(0.21729)

(0.24514) (0.15719)

3 -

0.499136

0.118770

-

0.857948

-0.156217 0.715682

(0.23798)

(0.28465)

(0.28520)

(0.28812) (0.14281)

4 -

0.852057

-

0.003769

-

1.210157

0.015818 0.748751

(0.39223)

(0.39999)

(0.34103)

(0.35588) (0.18933)

5 - - -0.078531 0.678277

122

0.847527 0.139767 1.049603

(0.44206)

(0.51559)

(0.47417)

(0.45807) (0.23389)

6 -

0.798992

0.250305

-

0.888033

-0.109504 0.584698

(0.48235)

(0.52696)

(0.52281)

(0.49720) (0.28234)

7 -

0.777043

0.352768

-

0.818115

-0.054204 0.485591

(0.53141)

(0.59286)

(0.59538)

(0.47751) (0.33069)

8 -

0.759858

0.423550

-

0.754084

-0.006090 0.402575

(0.62422)

(0.62558)

(0.69238)

(0.47903) (0.38129)

9 -

0.723464

0.451215

-

0.656286

0.028902 0.309669

(0.71927)

(0.69131)

(0.82225)

(0.47692) (0.45246)

10 -

0.673297

0.460047

-

0.557260

0.049373 0.216655

123

(0.83887)

(0.70773)

(0.93336)

(0.51746) (0.52184)

Ordering:

MC OPS

INF

PRODUCT

INTEREST

Variance decomposition

Variance Decomposition of MC

Variance

Decompositio

n of MC:

Period S.E. MC OPS INF PRODUC

T

INTERES

T

1

1263.65

100.000

0.00000

0.00000

0.000000 0.000000

124

9 0 0 0

2

1581.25

4

92.1729

1

3.65079

8

3.61492

0

0.064082 0.497290

3

2011.60

3

88.0652

0

2.62985

3

8.71402

8

0.160695 0.430227

4

2374.48

3

83.9579

8

2.51111

8

12.9666

3

0.214339 0.349933

5

2665.09

5

82.9710

3

2.38514

0

14.1563

0

0.209247 0.278279

6

2881.64

4

82.8218

7

2.53083

1

14.1142

6

0.209658 0.323379

7

3057.05

2

82.8184

1

2.77476

8

13.6689

5

0.225759 0.512116

8

3200.96

82.8049

3.00467

13.0429

0.273710 0.873717

125

7 9 7 0

9

3320.11

6

82.7002

0

3.19147

1

12.3662

2

0.331971 1.410135

10

3419.71

6

82.4641

9

3.29803

3

11.7252

3

0.397400 2.115147

Variance

Decompositio

n of OPS:

Period S.E. MC OPS INF PRODUC

T

INTERES

T

1

11.8146

2

0.64506

4

99.3549

4

0.00000

0

0.000000 0.000000

2

14.8145

1

0.52441

0

96.7841

2

0.87060

4

1.493335 0.327527

3

16.5613

0

0.53544

7

94.2525

5

1.40974

9

3.056073 0.746185

126

4

17.6010

1

0.64340

6

93.5631

2

1.29784

0

3.618198 0.877435

5

18.0483

8

0.79148

6

91.9425

8

1.34057

4

4.455902 1.469463

6

18.4723

9

1.52583

3

89.4086

3

2.09307

9

4.533845 2.438618

7

18.9106

9

2.70320

5

85.7932

7

3.49915

4

4.492482 3.511890

8

19.4080

8

4.17829

0

81.5504

4

5.30146

8

4.379206 4.590598

9

19.9260

2

5.81092

0

77.3664

5

7.16408

9

4.212152 5.446387

10

20.4479

1

7.44893

6

73.5233

3

8.92254

5

4.030749 6.074440

127

Variance

Decompositio

n of INF:

Period S.E. MC OPS INF PRODUC

T

INTERES

T

1

10.5374

7

1.96596

2

0.37024

8

97.6637

9

0.000000 0.000000

2

12.5619

9

5.98232

5

2.60895

1

90.2585

1

1.053325 0.096892

3

12.8934

0

6.08702

3

2.54249

7

88.6145

0

1.383135 1.372841

4

13.7041

5

8.42258

5

2.51913

6

84.5297

4

1.704471 2.824070

5

14.1823

6

10.0196

4

2.44001

4

81.8880

8

1.659405 3.992861

6 1.690646 4.695898

128

14.5336

2

11.2996

7

2.34911

5

79.9646

7

7

14.8708

4

12.5599

1

2.27464

5

78.3442

2

1.660222 5.161003

8

15.1793

7

13.7866

8

2.31842

3

76.8150

2

1.603742 5.476141

9

15.4398

3

14.8843

2

2.49418

0

75.4439

7

1.551808 5.625722

10

15.6680

1

15.8432

6

2.76733

6

74.2204

1

1.506987 5.662004

Variance

Decompositio

n of

PRODUCT:

Period S.E. MC OPS INF PRODUC

T

INTERES

T

1 72.58264 0.000000

129

13895.0

3

1.51780

2

3.37793

0

22.5216

2

2

14416.0

1

2.20264

9

5.85990

8

20.9268

5

68.10280 2.907790

3

14586.8

2

2.60218

2

6.45538

8

21.5668

5

66.52756 2.848017

4

14743.0

2

2.58891

5

6.42278

9

22.8263

2

65.22437 2.937609

5

14929.5

8

2.82043

5

6.37078

9

23.8205

5

63.65016 3.338069

6

14955.5

6

2.90089

1

6.35381

4

23.8469

3

63.44699 3.451373

7

14981.4

0

2.90640

5

6.36290

9

23.8713

6

63.28254 3.576781

8 63.07692 3.661263

130

15005.8

2

2.94982

5

6.36241

6

23.9495

8

9

15032.9

5

2.99376

7

6.40067

9

24.0231

4

62.84960 3.732817

10

15054.2

4

3.03704

6

6.43554

7

24.0698

6

62.67194 3.785611

Variance

Decompositio

n of

INTEREST:

Period S.E. MC OPS INF PRODUC

T

INTERES

T

1

1.06836

1

11.0354

9

0.99052

3

6.32678

5

19.94147 61.70572

2

1.28375

1

15.3057

1

0.70017

2

4.61575

6

14.50681 64.87156

3 8.275232 49.66475

131

1.78436

0

15.7470

6

0.80545

6

25.5075

0

4

2.43625

0

20.6792

4

0.43231

8

38.3572

7

4.443382 36.08779

5

2.87072

2

23.6096

3

0.54840

5

40.9934

8

3.275018 31.57347

6

3.17561

8

25.6240

4

1.06942

5

41.3195

7

2.795235 29.19173

7

3.42357

2

27.1982

6

1.98187

0

41.2615

9

2.430073 27.12820

8

3.63433

2

28.5065

4

3.11686

4

40.9198

9

2.156679 25.30003

9

3.80299

9

29.6529

5

4.25423

7

40.3487

5

1.975395 23.76867

10 1.860413 22.49886

132

3.93544

0

30.6177

2

5.33924

0

39.6837

7

Ordering:

MC OPS INF

PRODUCT

INTEREST

Variance Decomposition of INTEREST

Perio

d

S.E. MC OPS INF PRODUC

T

INTERES

T

1

1.06836

1

11.0354

9

0.99052

3

6.32678

5

19.94147 61.70572

2

1.28375

1

15.3057

1

0.70017

2

4.61575

6

14.50681 64.87156

3

1.78436

0

15.7470

6

0.80545

6

25.5075

0

8.275232 49.66475

4 4.443382 36.08779

133

2.43625

0

20.6792

4

0.43231

8

38.3572

7

5

2.87072

2

23.6096

3

0.54840

5

40.9934

8

3.275018 31.57347

6

3.17561

8

25.6240

4

1.06942

5

41.3195

7

2.795235 29.19173

7

3.42357

2

27.1982

6

1.98187

0

41.2615

9

2.430073 27.12820

8

3.63433

2

28.5065

4

3.11686

4

40.9198

9

2.156679 25.30003

9

3.80299

9

29.6529

5

4.25423

7

40.3487

5

1.975395 23.76867

10

3.93544

0

30.6177

2

5.33924

0

39.6837

7

1.860413 22.49886

134

135