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M Sc Finance and Economics

FM4T9 International Finance

Year: 2014-15

Exam Candidate Number: 44566

Word Count: 5762

Capital flows & their asymmetric impact on the volatility

of financial markets: Evidence from India

"The copyright of this dissertation rests with the author and no quotation from it or information derived

from it may be published without prior written consent of the author."

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Abstract

The purpose of this paper is to study the impact of International capital flows on the Indian stock

market. We study the impact on the first moment by using the Granger causality test and VaR analysis

while we study the impact on the second moment by modeling the returns using the GARCH model

and including variants of capital flows as explanatory variables. We find that there exists a (Granger)

causal effect from stock market returns to capital flows and not a reverse effect, implying capital flows

don’t influence returns in the Indian stock market. We modeled the conditional volatility of Sensex and

Nifty and found significant role of capital flows in explaining the volatility in the stock market.

Moreover, we find a significant asymmetric effect caused by capital outflows. We conclude by

performing robustness checks using different sub periods. In the process, we find interesting evidence

regarding higher contribution of capital flows to market volatility in the crisis period.

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Table of Contents

1. Introduction 4

2. Literature Review 6

3. Data 8

4. Methodology 10

5. Results and Discussion 14

Granger Causality test

Volatility Modeling

FIIs and the Volatility in the Stock Market

6. Conclusion and Discussion 25

7. Bibliography 26

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Introduction

After attaining independence in 1947, India’s economic policy was characterized by protectionism in

the form of import substitution, planning, License Raj and regulation (Mohan, 2008). In terms of

international economics, the economy was closed. “There was little depth in the foreign exchange

market as most such transactions were governed by inflexible and low limits and also prior approval

requirement” (Mohan, 2005). The investment decisions in the economy were mostly dictated by the

government rather than the fundamentals of market allocation. India had very restrictive capital

controls. India’s current account started showing signs of distress in the late 80’s. The dual shock of

Gulf Crisis and weakening export markets precipitated the balance of payments crisis of 1991. India

witnessed dwindling foreign reserves and it found itself with the capacity to pay for only three weeks of

imports. India finally obtained assistance from IMF and the process of dismantling industrial and

import licensing began under the new leadership of P.V. Narasimha Rao with Manmohan Singh as

Finance Minister (Cerra & Saxena, 2002). “After the launch of the reforms in the early 1990s, there

was a gradual shift towards capital account convertibility. From September 14, 1992, with suitable

restrictions, FIIs and Overseas Corporate Bodies (OCBs) were permitted to invest in financial

instruments” (ISMR, 2010).

The capital flows which were earlier marked by small scale official concessional finance, gained

momentum from the 1990s (Mohan, 2008). The Capital flows underwent a compositional shift from

being predominantly debt creating to non debt creating post 1991’s liberalization (Mohan, 2008).

Initially, pension funds, mutual funds, investment trusts, Asset Management companies, nominee

companies and incorporated/institutional portfolio managers were permitted to invest directly in the

Indian stock markets. In 1996-97, the qualified financial institutional investors included registered

university funds, endowment, foundations, charitable trusts and charitable (Changes to the SEBI

Regulations, 1995). “Till December 1998, investments were related to equity only as the Indian gilts

market was opened up for FII investment in April 1998” (ISMR, 2010).

Foreign Investment in India can be carried out in several ways including via investments in listed

companies which are done by Foreign Institutional Investors (FIIs), via direct investment called the

Foreign Direct Investment (FDI) and other categories including American/Global Depository Receipts,

by non residents Indians etcetera (ISMR, 2010).

International Capital flows are often advocated as a natural consequence of market forces which help in

channelizing the capital from capital abundant countries to capital scarce countries, accelerating

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economic growth by financing the industrialization and growth process. However, the portfolio capital

is usually short term in nature and it can lead to an economy being exposed to enhanced volatility and

sudden withdrawal risks. We see that world economy is tending towards increased globalization with

more and more economies liberalizing their stock markets to international portfolio investment flows.

This provides the benefits of diversification to the investors and a reliable flow of capital to the host

country. However, we are also witnessing greater incidence of financial crisis in recent times often

caused by “capital flight”. This has caused the enhanced capital flows to be seen in the suspicious light.

Capital flows are also called “hot flows” sometimes to highlight their short term speculative nature and

tendency of abrupt withdrawal at the slightest sign of distress in the market. This behavior exhibited by

investors, is often seen as herd behavior which sometimes takes a life of its own and makes the market

outcome drift away from the fundamentals.

In this paper, we attempt to shed light on the impact of such capital flows summarized by “Foreign

Institutional Investors” (FIIs) on the returns and the volatility of Indian stock market. We study the

direction of causality between the two and then study the impact of capital flows on the volatility of the

market.

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Literature Review

A lot has been written and documented about the capital flows which bring the promise of finance for

investment and carry the threat of de stabilizing the markets and in extreme cases, distortion of

macroeconomic outcome. Fischer (1997) points out two most important arguments in favour of capital

account liberalization. First, it’s an inevitable step in the path of economic development and second it

facilitates an efficient allocation of savings which leads to growth and welfare. However, he also

highlights the challenges in the form of vulnerabilities, overreactions, spillover effects and crisis. One

of his suggestions, to maximize benefits and minimize risks, is phased liberalization by retaining some

capital controls in transition, which has been the guiding principal of Indian capital account

liberalization throughout. Singh and Weisse (1998) bring to light the impact of such portfolio capital

outflows on the macroeconomic parameters by highlighting the 1994 Peso crisis of Mexico. The

Mexican markets received unprecedented amount of capital in anticipation of economic growth in

response to its reforms. This caused a 436% rise in its stock index. However, the economic growth

notwithstanding the surge in capital, turned out to be just 2.5% accompanied by a fall in the private

savings by 10%. It became a classic case of a credit financed consumption boom. (McKinnon and Pill,

1996). This caused an economy wide crisis which affected the entire Latin American economy.

Since the advent of phased opening of Indian stock markets to Foreign Institutional Investors, many

researchers have attempted to study its impact on the recipient stock market. Some studies have focused

on the first moment, the impact on the mean return in the stock market, while others have studied the

impact on the volatility of the market. Former class of studies include Chakrabarti (2001) who

highlighted the positive correlation between the stock returns and capital flows and noticed that capital

flows are primarily explained by recipient market returns and not to a great extent by international and

domestic variables. He also establishes the lack of causality from capital flows to market returns

contradicting the view that FIIs determine the returns in stock market. This unidirectional causality

from returns to FIIs along with no reverse causality is also confirmed by Kumar (2009). Saxena and

Bhadauriya (2011) used Granger test on the daily returns data and found a similar lack of bi-directional

causality between the returns and FIIs. However, Chandra (2012) finds a bidirectional link between

capital flows trading volume and returns but the flow of causality from flows to returns prevails over a

very short term. Paliwal & Vashishtha (2011) use monthly data to conclude the reverse causality which

is further confirmed by VaR analysis. So, the evidence on the direction of causality has been mostly

mixed.

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There are however very few studies on the second moment, the volatility of stock markets in the

context of FIIs. Behera (2012) uses the OLS on the returns data and the GARCH modeling on daily

data from 2002 to 2010 to test the significance of FIIs in the conditional variance equation and finds the

coefficient on the FIIs to be significant and positive. Joo & Mir (2014) carry out the stochastic

modeling of returns using the GARCH model on the monthly returns and FIIs data to establish a

significant impact of capital flows on Sensex and Nifty. Batra (2004) uses the E-GARCH model to

study the sudden volatility shifts over different periods focusing on monthly data and concludes that

Indian markets are more volatile post the reforms of 1991. Dhillon & Kaur (2007) use EGARCH and

TGARCH along with the gross purchases and the gross sales data of capital flows on the daily data to

conclude that the impact of FIIs on volatility is persistent and dies out slowly. Garg and Bodla (2011)

conduct a returns and volatility modeling and find a negative and significant coefficient on the capital

flows.

The approach of this paper includes the elements of both the genres of studies where we study returns

and then focus mainly on the volatility. The studies in this area mostly deal with monthly data so we try

to contribute by using the daily data on extended time period. Since stock markets usually reflect the

information immediately, daily data might shed more light on this relationship. Also, our study includes

the most recent time period which saw global economy change in the context of global crisis. We also

constructed a dummy variable which facilitates the assessment of capital outflows and their impact on

volatility, instead of focusing only on net capital inflows. We also perform the robustness check using

different sub periods.

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Data

Indian Financial Markets

India stock market is represented mainly by the two most widely tracked indices: Bombay Stock

Exchange’s Sensex (Sensitive Index) and National Stock Exchange’s CNX Nifty. “S&P BSE SENSEX,

first compiled in 1986, was calculated on a "Market Capitalization-Weighted" methodology of 30

component stocks representing large, well-established and financially sound companies across key

sectors” (Bombay Stock Exchange, 2014). It is now calculated as per the Free-float methodology. “The

CNX Nifty is a well diversified 50 stock index accounting for 23 sectors of the economy” (National

Stock Exchange, 2014). We can treat these two indices as representative of the Indian stock market.

BSE and NSE together contributed 99.7 percent of the total turnover in cash market, of which NSE

accounted for 84.1 percent in the total turnover whereas BSE accounted for 15.6 percent of the total

turnover in cash market (SEBI Annual Report, 2013-14).

International Capital Flows

“The foreign investments in India contributed by the FIIs/SAs stood at INR 15.93 trillion in 2013-14,

an increase of 19.3 percent over the previous year” (SEBI Annual Report, 2013-14). In the wake of

such strong flows contributed by FIIs, there’s a need to rigorously test their contribution to volatility.

We use the daily time series data on Sensex stock index and Nifty stock index from January 1, 2005 till

December 31, 2014. We use a 10 year time window for this study as we can’t assume that economic

conditions, markets and nature of investors remain the same from the outset in 1991 till 2014. Besides,

various studies have been conducted before 2010 and this time period includes new period and also

captures the response at the time of financial crisis of 2007-08 and the time period after it. Also, based

on previous literature, we don’t have to worry about the adverse impact of Global Financial Crisis of

2007-08 as I perform robustness check by running the tests on data from 2009-14 separately to account

for any drastic change.

We obtain the daily data for indices from the official website of Bombay Stock Exchange (BSE,

www.bseindia.com) which has a dedicated archive for daily data. We obtain the daily data on NSE

nifty from its official website (NSE, www.nseindia.com). The daily data on the FIIs is taken from the

official SEBI website (www.sebi.org). The data obtained on the indices is in the form of prices. We

modify the data using natural logarithm to obtain the returns data. In the words of Campbell, Lo and

MacKinlay (1997), there are two reasons to prefer returns over prices. First is, since from the point of

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view of investor, his investing activity doesn’t affect the prices, so the investment technology is

constant returns to scale and hence return is complete and scale free summary of investment

opportunity. The second reason is that for empirical purposes, returns have more attractive properties

like stationarity and erodicity and hence are easier to deal with, econometrically. We define returns as:

Rettof Sensex = 100 ∗ Ln Sensext

Sensext−1

Rettof Nifty = 100 ∗ Ln Niftyt

Niftyt−1

We use FIIs at level because they are characterized by random movements and don’t have a discernable

trend as opposed to indices which are growing over time. Also, we find that there are certain missing

entries for data in FIIs. So, in order to avoid the mismatching of data as per dates, we first sort returns

data as per dates and later, match the corresponding FIIs data on that date. We find that we have very

few (less than 10) observations (at random time intervals) out of approximately 2435 observations

which have no match and we drop them to avoid any date clashes.

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Methodology

Once we have the returns data we can carry out the empirical analysis. Before we move on to conduct

any kind of econometric tests or modeling on these time series, we have to perform the stationarity test

which is often a prerequisite. Stationarity tests are important to eliminate the possibility of spurious

regressions or absurd correlation. It’s often pointed out in the econometric literature that if two series

have a trend such that they are growing steadily, they would show a high degree of correlation

automatically even though they are not truly related. This is how Tsay (2005) defines stationarity:

“A time series is said to be weakly stationary if its first and second moments are time invariant. In

particular, the mean vector and the covariance matrix of a weakly stationary series are constant over

time.” (Tsay, 2005, p.300) We conduct the following test:

Augmented Dickey Fuller (ADF) test (Said & Dickey, 1984) is a unit root test which tests the following

equations.

∆𝐑𝐞𝐭𝐭 = 𝛂 + 𝛃𝐭 + 𝛄𝐑𝐞𝐭𝐭−𝟏 + 𝛅𝟏∆𝐑𝐞𝐭𝐭−𝟏 + … . . + 𝛅𝐩−𝟏∆𝐑𝐞𝐭𝐭−𝐩+𝟏 + 𝛆𝐭

∆𝐅𝐈𝐈𝐬𝐭 = 𝛂 + 𝛃𝐭 + 𝛄𝐅𝐈𝐈𝐬𝐭−𝟏 + 𝛅𝟏∆𝐅𝐈𝐈𝐬𝐭−𝟏 + … . . + 𝛅𝐩−𝟏∆𝐅𝐈𝐈𝐬𝐭−𝐩+𝟏 + 𝛆𝐭

The unit root test is conducted based on the null hypothesis γ=0 against the alternative hypothesis

of γ<0. We obtain a negative number as the augmented Dickey–Fuller (ADF) statistic. A more negative

number or a larger magnitude number with negative sign leads to rejection of the hypothesis that there

is a unit root. If we find the presence of a unit root then we modify the data to make it stationary

(Mahadeva, L. & Robinson, 2004).

After checking for the stationarity we use the VaR model (Tsay, 2005, p.263) to determine the

optimum number of lags for granger causality test. This is a very flexible statistical modeling procedure

as we don’t have to hypothesize beforehand the direction of causality. While setting up the testing

equations we can treat both our variables, returns and the FIIs, as endogenous and include them in an

autoregressive time series. The dynamic analysis for our variables is carried out as follows:

𝐑𝐞𝐭𝐭 = 𝐚𝟎 + 𝛂𝟏𝐑𝐞𝐭𝐭−𝟏 + 𝐚𝟐𝐑𝐞𝐭𝐭−𝟐 +⋯+ 𝐚𝐧𝐑𝐞𝐭𝐭−𝐧 + 𝐛𝟏𝐅𝐈𝐈𝐒𝐭−𝟏 + 𝐛𝟐𝐅𝐈𝐈𝐬𝐭−𝟐 +⋯+ 𝐛𝐧𝐅𝐈𝐈𝐬𝐭−𝐧 + 𝛆𝟏

𝐅𝐈𝐈𝐬𝐭 = 𝐚𝟎 + 𝛂𝟏𝐑𝐞𝐭𝐭−𝟏 + 𝐚𝟐𝐑𝐞𝐭𝐭−𝟐 +⋯ + 𝐚𝐧𝐑𝐞𝐭𝐭−𝐧 + 𝐛𝟏𝐅𝐈𝐈𝐒𝐭−𝟏 + 𝐛𝟐𝐅𝐈𝐈𝐬𝐭−𝟐 +⋯+ 𝐛𝐧𝐅𝐈𝐈𝐬𝐭−𝐧 + 𝛆 𝟏

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We include the n lags of FIIs as well as of Returns in the model treating Returns as dependent variable

and then we estimate the same model using FIIs as the dependent variable. We use the VaR Lag Order

Selection Criteria of EViews to select the optimum lag (the value of “n”) in this case. We have six

criteria for the optimum lag selection in Eviews, namely: Akaike information criterion (AIC), Schwarz

information criterion (SIC or BIC), Hannan-Quinn information criterion (HIC), log likelihood value

(Log L), sequential modified likelihood ratio and the final prediction error. We can pick the minimum

value given by any of these criteria. We pick the criteria suggested by BIC (Schwarz information

criterion) in order to conduct the Granger Causality Test. (This is more a matter of taste than any

econometric preference)

Granger Causality Test

Granger causality test (developed by Granger in 1969) tests the dual hypothesis of the direction of

causality. We come across the phrase “correlation doesn’t imply causation” very often. Whenever we

are dealing with the time series, it becomes imperative to label the various series we are dealing with,

as dependent or independent. This journey from correlation to causation could be very abstract and

could depend on theory and several other factors. In the present case, we use classic statistical test

called the Granger Causality test. This test is a statistical test which tests the dual hypothesis of the

interdependencies and the causality between the two time series. A variable is said to granger cause the

other variable if the its past values provide us information about future values of the other variable and

we are able to predict the variable in a statistically significant way. After determining the appropriate

length using the VaR analysis, we conduct the Granger Test.

It tests two Null Hypotheses:

Index Returns do not Granger Cause FIIs

FIIs do not Granger Cause Index Returns

The F statistic that we get for each of the hypotheses is then compared to the critical F values. Eviews

reports the p values pertaining to the tests. If the P-value is less than the 5% critical value, then we

reject the hypothesis and establish Granger Causality.

Volatility Modeling

After conducting the preliminary analysis of the direction of the causality, we move on to the statistical

modeling of the returns process along with the modeling of the variance of the returns. Since we are

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using the daily data on returns and FIIs, we will have a very wiggly data which would roughly follow a

random walk.

The simplest way to model the volatility of a financial time series is to begin with the econometric

modeling of the returns data. This is crucial as we have to first select an appropriate ARMA

(Autoregressive Moving Average) model to take care of and remove any linear dependencies in the

returns data before we model the volatility. The ARMA (p, q) model looks like:

𝐑𝐞𝐭𝐭 = 𝛂𝟎 + 𝛂𝐢𝐑𝐞𝐭𝐭−𝐢 +

𝐩

𝐢=𝟏

𝛃𝐢𝛆𝐭−𝐢

𝐪

𝐣=𝟏

+ 𝛆𝐭

Where εt = ζtηt

And ηt is the sequence of independent and identically distributed (IID) random variables with mean

zero and variance 1. The ARMA model comprises of two processes: the autoregressive process which

pertains to the “p” lags of the dependent variable itself and the moving average process which deals

with the “q” lags of the noise term of the model. εt is the white noise term here with mean zero and

variance ζt.

The conditional variance equation using generalized autoregressive conditional heteroskedasticity

model: GARCH (m, s) model (developed by Bollerslev in 1986) is of the type:

𝛔𝐭𝟐 = 𝛚𝟎 + 𝛚𝐢𝛆𝐭−𝐢

𝟐

𝐦

𝐭=𝟏

+ 𝛉𝐣𝛔𝐭−𝐣𝟐

𝐬

𝐣=𝟏

In order to determine the ARMA-GARCH model we have to determine the lags (p, q) to be used in the

conditional mean equation and the lags (m, s) to be used in the GARCH equation. We make use of the

population autocorrelation function to plot the data of returns and check for the presence of

autocorrelation. In this paper, we estimated the model for returns by checking all the combinations

where p and q range between 0 and 5 and subsequently fit the GARCH model using the same method.

We check the Bayesian Information Criteria (BIC) for all the models and pick the model that minimizes

the BIC criteria. Running various permutations and combinations in Eviews, I find the most suitable

model for BSE stock returns and conditional variance. Financial econometric literature suggests the

presence of leverage effect in the stock data which would mean volatility tends to increase following a

negative news shock more than it does following a positive news innovation. So we check if we can use

the traditional E-GARCH or T-GARCH models to capture that.

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In order to study the contribution of FIIs to the volatility, I add the FIIs as an explanatory variable in

the conditional variance equation and modify the model as follows:

𝛔𝐭𝟐 = 𝛚𝟎 + 𝛚𝐢𝛆𝐭−𝐢

𝟐

𝐦

𝐭=𝟏

+ 𝛉𝐣𝛔𝐭−𝐣𝟐 +

𝐬

𝐣=𝟏

𝛅𝐅𝐈𝐈𝐬𝐭

We then check if the volatility in the stock market and the FIIs flows move together. In order to test

that we check the significance of FIIs and also check if our model improves further.

Next we check if the negative FIIs (capital outflow) cause the spike in volatility which means we use

one period lagged data in the conditional variance equation with the dummy variable which switches on

when FIIs are less than zero or when there’s a net outflow. We then check the significance of this

variable.

𝛔𝐭𝟐 = 𝛚𝟎 + 𝛚𝐢𝛆𝐭−𝐢

𝟐

𝐦

𝐭=𝟏

+ 𝛉𝐣𝛔𝐭−𝐣𝟐 +

𝐬

𝐣=𝟏

𝛅𝐅𝐈𝐈𝐬𝐭−𝟏(𝐅𝐈𝐈𝐬𝐭−𝟏 < 𝟎)

Since, the debate has mainly highlighted the “hot flows” nature of the Foreign Institutional investors

capital flows; we are more interested in the volatility caused by FIIs following the negative news and

the subsequent abrupt withdrawal from the market. The negative news is captured by a negative “news

shock” embedded in the variable FIIst−1(FIIst−1 < 0). We use the GARCH model along with this

asymmetric variable as explanatory variable and check the significance of these explanatory variables

in the conditional variance equation. In order to conclude that a capital withdrawal does cause an

increase in volatility in the market, we expect the coefficient δ to be negative as we are just taking the

negative values of FIIs so both the negatives would make this term positive and indicate a positive

contribution to the volatility.

We also carry out robustness checks by checking the same models for the periods 2007 to 2008 and for

2009 to 2014 where the latter time period is not likely to be affected by the Global Financial Crisis.

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Results and Discussion

Stationarity Tests

Before I used concrete econometric methods to test for stationarity, I conducted a visual inspection to

study if there’s discernible trend in the time series used in this paper. I begin by plotting the BSE

Sensex and NSE Nifty index. I see that there’s a clear upward trend in both the series (graphs on the

left) and this implies this series is not stationary. However, when we plot the returns data, we see that

they both seem fairly stationary with time invariant first and second moments (graphs on the right).

The results of the Augmented Dickey Fuller Test are presented in the table I below. We see that both

the BSE Sensex returns and NSE Nifty returns are stationary and the null hypothesis that they have unit

roots is rejected soundly. We get p values very close to zero. We notice that this highlights the

advantage of using Log returns instead of regular index prices which eliminates the trend inherent in

the BSE and NSE time series and hence eliminates the possibility of spurious regression.

Null Hypothesis: NIFTY_RETURNS has a unit root

Exogenous: Constant

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Lag Length: 0 (Automatic - based on SIC, maxlag=26)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -46.06025 0.0001

Test critical values: 1% level -3.432855

5% level -2.862533

10% level -2.567344

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: SENSEX_RETURNS has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=26)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -45.41075 0.0001

Test critical values: 1% level -3.432855

5% level -2.862533

10% level -2.567344

*MacKinnon (1996) one-sided p-values.

Null Hypothesis: FIIS has a unit root

Exogenous: Constant

Lag Length: 5 (Automatic - based on SIC, maxlag=26)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -12.91158 0.0000

Test critical values: 1% level -3.432861

5% level -2.862535

10% level -2.567345

*MacKinnon (1996) one-sided p-values.

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Next, we move on to the VaR analysis. We run the VaR model and use the optimum lag criterion to

choose the appropriate model for the Granger Causality test. BIC picks no. of lags as 3 for Sensex as

well as Nifty while LR and AIC suggest lags of 6. We perform the Granger Test using the BIC criteria

but we repeated our tests for 6 lags as well, for robustness. The results are presented in the Table II

below.

Granger Tests

Lags: 3

Null Hypothesis: Obs

F-

Statistic Prob.

FIIS does not Granger Cause

SENSEX_RETURNS 2420 0.39697 0.7552

SENSEX_RETURNS does not Granger Cause FIIS 78.8202 1.E-48

Lags: 3

Null Hypothesis: Obs

F-

Statistic Prob.

FIIS does not Granger Cause

NIFTY_RETURNS 2420 0.45484 0.7139

NIFTY_RETURNS does not Granger Cause FIIS 80.1666 2.E-49

We see that the direction of causality is from BSE Sensex returns to FIIs as we get an F statistic as big

as 78 for the hypothesis that Returns don’t granger cause the capital flows. Also in case of Nifty, we

find that the direction of causal effect is from the returns to FIIs and not the other way round. We are

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not able to reject the hypothesis that Capital flows don’t granger cause the returns. This means FIIs

don’t help in predicting the returns or returns are not caused by the capital flow activity.

After establishing the direction of causality, we model the returns using the OLS on the time series

data. We use the OLS modeling of EViews to check the information criteria for every ARMA (p, q)

model varying the value of p and q between 0 and 5. Maintaining our symmetry, we pick the AR(1)

model using the BIC criteria for Sensex Returns. We repeated the procedure for Nifty returns and found

that BIC suggests a model of AR(1) for Nifty as well. (Notice that AR(1) is the most commonly used

model for the stock returns process. The R squared seems low but this is exactly in line with our

random walk hypothesis. We are taking daily data which is extremely random and hence should have a

lower R squared consistent with the efficient market hypothesis.) After fitting the conditional mean

model which absorbs the linear correlation, we move on to check the correlation in the squared

residuals. We perform the visual inspection of the squared residuals to look for the ARCH effect.

We notice that the residuals from our fitted ARMA model exhibit the tendency of volatility clustering

in the index returns. “This means that volatility evolves over time in a continuous manner- that is,

volatility jumps are rare” (Tsay, 2005, p.80). This means periods of relative calm are followed by low

volatility periods and high volatility is usually followed by similar high volatility. The squared

residuals correlogram plot confirmed the same pattern where the correlation bars were often outside the

asymptotic bounds confirming presence of conditional heteroskedasticity. Next we perform the ARCH

heteroskedasticity test.

This test clearly indicated the presence of conditional heteroskedasticity. We use the GARCH model to

fit the best conditional variance model in the data in conjunction with our conditional mean equation.

Again, we pick the BIC minimizing model which results in GARCH (1, 1) model with normal-

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distribution for Sensex. Also, we fit the conditional variance model for Nifty as well which again

results in the classic GARCH(1,1) model as per the BIC criteria.

The diagnostics tests suggest that the models have absorbed all the clustering effect. We see that the

squared residuals are not significant once we have fitted GARCH (1,1) and we are able to reject the

ARCH-LM test as the p-value is now well above 5% for both the indices.

Next we present the results from asymmetric modeling where we use the T-GARCH model which

further improves our fit. This suggests that returns become more volatile following negative shocks

(negative news in the market) as compared to the positive return shocks. The coefficient on the

asymmetric term is significant and positive which confirms the negative news response.

𝛔𝐭𝟐 = 𝛚𝟎 + 𝛚𝐢𝛆𝐭−𝐢

𝟐𝐦𝐭=𝟏 + 𝛉𝐣𝛔𝐭−𝐣

𝟐 + 𝛅𝛆𝐭−𝟏𝟐 (𝛆𝐭−𝟏 < 𝟎𝐬

𝐣=𝟏 )

The table below shows the results of volatility modeling. In the conditional mean equation, we used the

dependent variable as the returns on SENSEX and used its one lag to model returns. The conditional

variance includes one ARCH term and one GARCH term along with one term of asymmetry which

makes this model the well known model, Threshold GARCH. We see that that the term with

asymmetry is another εt2 term which has a dummy variable δ attached to it. This switches on when the

past shock was negative and switches off when the past shock was positive. As per our theory, the

coefficient of this term should be positive and significant. We find that δ has a value of 0.111 and a p-

value of zero. Hence, it is highly significant.

Sensex

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-1)^2*(RESID(-1)<0) +

C(6)*GARCH(-1)

Variance Equation

C 0.035132 0.004965 7.076260 0.0000

RESID(-1)^2 0.037738 0.006662 5.664964 0.0000

RESID(-1)^2*(RESID(-1)<0) 0.111935 0.014371 7.789108 0.0000

GARCH(-1) 0.891556 0.008045 110.8237 0.0000

19

Nifty

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-1)^2*(RESID(-1)<0) +

C(6)*GARCH(-1)

Variance Equation

CONSTANT 0.039040 0.005412 7.214126 0.0000

RESID(-1)^2 0.040240 0.007261 5.541683 0.0000

RESID(-1)^2*(RESID(-1)<0) 0.117120 0.014736 7.947920 0.0000

GARCH(-1) 0.885608 0.008740 101.3300 0.0000

The above table shows the results of volatility modeling. In the conditional mean equation, we used the

dependent variable as the returns on NIFTY and used its one lag to model returns. The conditional

variance includes one ARCH term and one GARCH term along with one term of asymmetry which

makes this model the well known model, Threshold GARCH. We see that that the term with

asymmetry is another εt2 term which has a dummy variable δ attached to it. This switches on when the

past shock was negative and switches off when the past shock was positive. As per our theory, the

coefficient of this term should be positive and significant. We find that δ has a value of 0.117 and a p-

value of zero. Hence, it is highly significant.

20

FIIs and the Volatility in the Stock Market

Next we present the results when we finally add the FIIs as the explanatory variable in the conditional

variance equation. This would give us an insight of how volatility moves along with the movement in

the capital flows. The results are in the following table:

Sensex

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-

1)^2*(RESID(-1)<0) +

C(6)*GARCH(-1) + C(7)*FIIS

Variance Equation

CONSTANT 2.020809 0.531066 3.805194 0.0001

RESID(-1)^2 0.124149 0.043141 2.877737 0.0040

RESID(-

1)^2*(RESID(-1)<0) 0.006121 0.050287 0.121717 0.9031

GARCH(-1) 0.529596 0.124003 4.270829 0.0000

FIIS -0.000246 4.17E-05 -5.892846 0.0000

We see that there’s virtually no change in the significance of any explanatory variable in the

conditional variance equation except the asymmetric term. We see that as opposed to being highly

significant in the previous regression, this term now becomes highly insignificant with p-value of 90%.

We see that much of this effect is absorbed by the FIIs which have a coefficient of negative 0.000246

and a p value of almost zero. This means FIIs is highly significant in the conditional variance equation.

This implies that when FIIs withdraw capital from the market, the volatility increases by a factor of

0.02% and this causes the market to be volatile in the period of withdrawal. However, volatility

decreases when FIIs pour money in the market.

Nifty

GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*RESID(-

1)^2*(RESID(-1)<0) +

21

C(6)*GARCH(-1) + C(7)*FIIS

Variance Equation

CONSTANT 1.250478 0.081513 15.34083 0.0000

RESID(-1)^2 0.447338 0.034619 12.92191 0.0000

RESID(-

1)^2*(RESID(-1)<0) -0.080853 0.052197 -1.548998 0.1214

GARCH(-1) 0.253472 0.037235 6.807389 0.0000

FIIS -0.000115 4.74E-06 -24.25216 0.0000

We notice that just as we witnessed for the Sensex data, there’s no change in the significance level of

any of the explanatory variables in the conditional variance equation except for the asymmetric error

term which has now become insignificant with p value of 12%. However, the FIIS here are highly

significant and the coefficient on the FIIs is a negative 0.0001 which means when FIIs withdraw funds

the volatility increases by 0.01% and when they invest the funds, volatility falls by such percentage.

However, since GARCH is a deterministic equation we can’t be sure that FIIs exert significant

influence just because it made the asymmetric error term insignificant. Hence, we carry out further

modeling.

Next, we attempt to study the impact of capital outflows. We include the dummy variable interaction

type term where we use FIIs only when they are negative and we use zero when they are positive to

study the impact of outflows on the volatility in the next period.

𝛔𝐭𝟐 = 𝛚𝟎 + 𝛚𝐢𝛆𝐭−𝐢

𝟐

𝐦

𝐭=𝟏

+ 𝛉𝐣𝛔𝐭−𝐣𝟐 +

𝐬

𝐣=𝟏

𝛅𝐅𝐈𝐈𝐬𝐭−𝟏(𝐅𝐈𝐈𝐬𝐭−𝟏 < 𝟎)

We found that the coefficient is negative and highly significant as expected. The results are as follows:

Sensex

Variance Equation

CONSTANT 0.024687 0.005567 4.434730 0.0000

22

RESID(-1)^2 0.090127 0.007645 11.78970 0.0000

GARCH(-1) 0.893892 0.008227 108.6592 0.0000

FIIS(-1)*(FIIS(-

1)<0) -4.87E-05 1.81E-05 -2.697079 0.0070

Nifty

Variance Equation

CONSTANT 0.025668 0.005986 4.288135 0.0000

RESID(-1)^2 0.092505 0.008097 11.42435 0.0000

GARCH(-1) 0.891108 0.008731 102.0615 0.0000

FIIS(-1)*(FIIS(-

1)<0) -5.88E-05 1.97E-05 -2.983660 0.0028

The coefficient on capital outflows is -0.0000487 for Sensex and -0.0000588 for Nifty which are both

significant at 5% level of significance and even at 1% significance level. This means that capital

outflows increase the volatility in the market (the negative sign of FIIs and the negative sign of

coefficient means an overall increase in the conditional variance).

We then test this model for 2007 to 2008 to study the impact of financial crisis. We also perform the

robustness check and exclude the period of Global Crisis from our sample and run our results only on

the period from 2009 to 2014. We find that FIIs still remain significant in the whole model and even in

the negative dummy model. However, the coefficient on the dummy during the crisis period is more

than 5 times that of the one during the non crisis times. So we can expect that the impact of FIIs on the

returns from full sample period could be a little overstated but this doesn’t change the fact that it

remains highly significant in both the periods. The results are presented as:

Sensex 2007-2008

Variance Equation

23

CONSTANT 0.098631 0.043528 2.265920 0.0235

RESID(-1)^2 0.118154 0.031037 3.806952 0.0001

GARCH(-1) 0.826478 0.031350 26.36259 0.0000

FIIS(-1)*(FIIS(-

1)<0) -0.000543 0.000212 -2.557107 0.0106

Sensex 2009-2014

Variance Equation

CONSTANT 0.034104 0.009334 3.653803 0.0003

RESID(-1)^2 0.049818 0.012764 3.903089 0.0001

GARCH(-1) 0.899536 0.017963 50.07588 0.0000

FIIS(-1)*(FIIS(-

1)<0) -8.22E-05 2.45E-05 -3.358088 0.0008

Nifty 2007-2008

Variance Equation

CONSTANT 0.106006 0.053550 1.979586 0.0478

RESID(-1)^2 0.118505 0.033315 3.557068 0.0004

GARCH(-1) 0.804884 0.035671 22.56415 0.0000

FIIS(-1)*(FIIS(-

1)<0) -0.000857 0.000232 -3.684703 0.0002

Nifty 2009-2014

Variance Equation

CONSTANT 0.029713 0.008781 3.383736 0.0007

24

RESID(-1)^2 0.047590 0.011968 3.976289 0.0001

GARCH(-1) 0.907886 0.016251 55.86503 0.0000

FIIS(-1)*(FIIS(-

1)<0) -8.07E-05 2.36E-05 -3.414328 0.0006

Sensex: We notice that the coefficient on capital outflows during 2007-08 is 6.6 times that of the

coefficient during 2009-14 which highlights the excess contribution of capital outflows to volatility

during the period of crisis.

Nifty: We notice that the coefficient on capital outflows during 2007-08 is 10.6 times that of the

coefficient during 2009-14 which again signals a very high contribution of capital outflows to the

volatility during the crisis.

This is in line with our theory because this signals that capital outflows not only cause an increase in the

volatility but they also contribute increasingly more to the volatility in the time of crisis. Notice that the

reasons of crisis were external to Indian economy, yet it bore the impact of it in terms of increased

volatility.

25

Conclusion and Discussion

We have conducted a time series analysis of the volume of International Capital Flows in India and

their impact on the mean and variance processes of Sensex and Nifty, the representative stock indices

of Indian Financial market. The results of the Granger Causality suggest the presence of a causal effect

from the returns to capital flows. This means that as the return in the market increases, the foreign

investors rush to invest in the markets and vice versa. There’s however, an absence of causal impact

from capital flows to returns which means that we can’t say that capital flows influence the price

discovery mechanism of the market. These results are consistent with Swami P. Saxena et al (2011) and

Kumar (2009). This suggests that capital flows are price takers when it comes to their investing

activity.

Next we move on to the study of the impact of such hot flows on the volatility of the market which is at

the heart of the debate whether or not capital flows render the recipient market more vulnerable to

shocks and abrupt withdrawal. Also, the withdrawal is potentially more de stabilizing than the rush of

money so we are more worried about the negative news impact on the capital flows. We see that the

highly significant asymmetric error term in the T-GARCH conditional mean equation becomes

insignificant as we add capital flows as one of the explanatory variables in the conditional variance

equation. This means that the asymmetric response of volatility could be subsumed by FIIs. A negative

sign on FIIs coefficient is indicative of the fact that when FIIs fall (there’s a net outflow of capital), the

volatility rises in the market. However, when FIIs put in the money, which is accompanied by and more

often a result of the surge in the returns (in times of economic boom), the volatility comes down as

there’s consistent pumping in of capital which increases the confidence in the market.

We see that when we model only the capital outflows and include them as an asymmetric term in the

conditional variance equation, they are highly significant. In fact, when we break our data to focus on

crisis period and non crisis period, we see that the coefficient on capital outflows is much bigger during

crisis than it is during the non crisis period. This is a natural consequence of “home bias” where

investors try and put their money in the home markets to safeguard themselves from the currency risks.

However, the main concern here is if the benefits of FIIs in the form of market deepening and more

efficient allocation of savings are great enough to accommodate the increased volatility and market

instability that results from capital flight?

26

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