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ORI GINAL RESEARCH

Level of efficiency in the UK equity market: empiricalstudy of the effects of the global financial crisis

Taufiq Choudhry • Ranadeva Jayasekera

� Springer Science+Business Media New York 2013

Abstract This paper investigates the effect of good or bad news (the asymmetric effect)

on the time-varying beta of firms in the UK during good periods (booms) and bad periods

(recessions). Daily data from twenty five UK firms of different sizes and from different

industries are applied in the empirical tests. The data ranges from 2004 to 2010, which

includes the current global financial crisis. The time-varying betas are created by means of

the bivariate BEKK GARCH model, and then linear regressions are applied to test for the

asymmetric effect of news on the beta. The asymmetric effects are investigated based on

both market and non-market shocks. Most firms and industries seem to support the market

efficiency hypothesis during both periods. However, the level of market efficiency seems to

decline significantly from the pre-crisis to crisis period. Both the results of market effi-

ciency and declining market efficiency from the pre-crisis to crisis periods provide ample

evidence of the asymmetric effect of the financial crisis on the beta of UK firms.

Keywords Asymmetric effect � Time-varying beta � BEKK � Market

efficiency � Asset mispricing

JEL Classification G1 � G12

1 Introduction

The controversy surrounding the abnormality of stock prices has been the subject of

extensive research over the past few decades. Essentially two competing mutually

T. Choudhry (&) � R. JayasekeraSchool of Management, University of Southampton, Southampton SO17 1BJ, UKe-mail: [email protected]

R. Jayasekerae-mail: [email protected]

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Rev Quant Finan AccDOI 10.1007/s11156-013-0404-6

independent hypotheses have emerged, each explaining certain aspects of stock price

behaviour.

The first, which we describe as ‘asset mispricing’, puts forth a behavioural finance

argument to explain certain anomalies of stock price behaviour. These studies present an

explanation of the evident over/under reaction of stock prices to information This essen-

tially suggests market inefficiency. De Bondt and Thaler (1989), Chopra et al. (1992),

Ritter (1991), Loughran and Ritter (1995), Spiess and Affleck-Graves (1995), and Dharan

and Ikenberry (1995) all present evidence of market inefficiency in terms of overreaction to

information. Evidence of under-reaction is just as frequent, as shown by Ball and Brown

(1968), Bernard and Thomas (1990), Jegadeesh and Titman (1993), Cusatis et al. (1993),

Desai and Jain (1997), Ikenberry et al. (1996), Lakonishok and Vermaelen (1990), Iken-

berry et al. (1995), Michaely et al. (1995), Asquith (1983), Agrawal et al. (1992), Roll

(1986), Ikenberry and Lakonishok (1993) and, more recently, Frazzini (2006).

The alternative argument for ‘‘market efficiency’’ serves to enforce the efficient market

hypothesis; Fama (1970, 1991), Fama and French (1992, 1993, 1998, 2002).1 Chan (1988)

and Ball and Kothari (1989), all provide evidence that the beta of individual stock rises

(falls) in response to abnormally negative (positive) returns, and argue that this asymmetric

response to good and bad news explains the performance of stock returns, i.e. there exists

predictive asymmetry in conditional betas’ response to shocks. Ball and Kothari (1989)

argue that this asymmetric response to good and bad news explains the performance of

over/under reaction experienced by the ‘‘winners’’ and ‘‘losers’’ stocks. They show that in

an efficient market time-varying expected returns are caused by: a variation in expected

returns on the market portfolio; the relative risk of a firm’s investments; and leverage.

Thus, if the firm beta changes asymmetrically in response to news (shocks) this provides

support for the efficient market hypothesis (Cho and Engle 1999).2 Therefore the detection

of asymmetry in beta lends support to the market efficiency theory as the actual degree of

mispricing is now less because some of it can be explained by the change in beta.

Our research views this controversy from a different perspective, through the analysis of

stock returns of UK firms leading up to and during the financial crisis of 2007–2010.3 Thus,

we investigate the asymmetric effect of beta during good and bad periods to good and bad

news, and shed fresh insight to the controversy of the ‘‘abnormalities of stock prices’’, i.e.

whether the hypothesis of ‘increased market efficiency’ or ‘asset mispricing’ would better

fit the empirical observations as an economy slides from a boom to a rescission. We focus

on the UK equity market, analysing the relative effects on five major industrial segments.

Our main hypothesis in this paper is to investigate whether any asymmetric effect exist in

betas of UK firms during the pre-crisis and the crisis periods? The bivariate BEKK

GARCH model is employed to estimate the time-varying beta for each firm. The theory of

time-varying beta is based on Bodurtha and Mark (1991). Then linear regression is used to

capture the effects of a period when the UK economy slid from relative prosperity (pre-

crisis) to a recession, i.e. the current financial crisis period. We define the pre-crisis period

(which we refer to as the ‘‘good’’ period) from January 2004 to June 2007 and the crisis

period (which we refer to as the ‘‘bad’’ period) to commence from June 2007 to September

2010. Thus, we investigate 7 years of daily data, classified under 5 major industries

1 Harel et al. (2011) provide an analysis of efficient markets.2 Cho and Engle (1999), finding evidence of asymmetric effects in betas of US firms, claim that this impliesthat abnormities of stock prices can be explained by changes in expected returns through a change in beta,thus supporting the claims of Chan (1988) and Ball and Kothari (1989).3 Of course, the crisis has carried on beyond 2010.

T. Choudhry, R. Jayasekera

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covering 25 companies in total. The industries under study are: Banking, Retail, Food,

Construction and Oil. Although there is an extensive body of literature on the controversy

surrounding asset mispricing and market efficiency, to the best of our knowledge there is

no published research4 which looks at this puzzle during this financial crisis period, and

thus our findings make a valuable contribution in this area. Also, given the lack of research

in this area for the UK this paper makes a vital contribution to the literature.

We proceed by describing and explaining briefly the global financial crisis of

2007–2010 in Sect. 2. The conditional CAPM and the time-varying beta are presented in

Sect. 3. In Sect. 4 we describe the data and the BEKK GARCH modelling framework

employed. The GARCH results are briefly described in Sect. 5. Section 6 explains the

theoretical underpinnings of time varying beta and the general framework that we employ

to capture the effect of good and bad news leading up to and during the financial crisis

period. In particular we explain how we interpret the asymmetric betas, justifying our

arguments. The asymmetric effect results and their interpretation are in Sect. 7. We con-

clude in Sect. 8.

2 Global financial crisis of 2007–2010

The current financial crisis that first hit the global economy in the summer of 2007 is

without precedent in post-war economic history. However, although its size and extent

are exceptional, the crisis still has many features in common with similar financial-stress

driven recession episodes in the past. The crisis was preceded by a long period of rapid

credit growth, low risk premiums, abundant availability of liquidity, strong leveraging,

soaring asset prices and the development of bubbles in the real estate sector. Over-

stretched leveraging positions rendered financial institutions extremely vulnerable to

corrections in asset markets.5 As a result, a turn-around in a relatively small corner of the

financial system (the US subprime market) was sufficient to topple the whole structure.6

Such episodes have happened before (e.g. Japan and the Nordic countries in the early

1990s, the Asian crisis in the late-1990s, the US S&L crisis in the mid-1980s), however

this time is different as the crisis has had a truly global affect (European Commission

2009).

Initially the UK companies affected were those directly involved in home construction

and mortgage lending such as Northern Rock7and Countrywide Financial, as they could no

longer obtain financing through the credit markets.

4 Veronesi (1999) presents a two state continuous time hidden Markov chain model to explain the stockmarket under-reaction to bad news in good times. We use a BEKK GARCH framework analysing the impactof good and bad news in good and bad periods and present empirical evidence for 5 major industryclassifications leading up to and during the recent financial crisis period.5 Dwyer and Lothian (2012) state that cross-country evidence and analyses of individual countries suggest acommon explanation to the cause of the financial crisis is likely to be based in rapid credit expansion andeconomic growth. Dias and Ramos (2013) study the behaviour of the banking sector of 40 countries duringthe period 2007–2010. They show that although there were periods of intense contagion, the impact wasuneven among sample countries. Marsh and Pfleiderer (2012) provide a discussion of black swans and thefinancial crisis.6 However, Kamin and DeMarco (2012) conclude that issues with U.S. Sub Prime mortgages more plau-sibly were a wake-up call about banking problems around the world than a direct cause of those problems.7 See Shin (2009). Reflections on Northern Rock.

Level of efficiency in the UK equity market

123

The above graph demonstrates the behaviour of the FTSE 100 index, which can be

regarded as a proxy for the UK economy during the period under consideration. As is

evident, there is a marked decline8 in the index along with its market capitalisation

commencing from the second half of 2007.9 By August 2007 the European Central Bank

injected 170bn euros into the banking market and the Fed had lowered interest rates in an

attempt to revive the credit markets. September 2007 saw the fall of Northern Rock

(Dwyer and Tkac 2009), a UK building society, as it received emergency financial support

from the Bank of England. Shares in Northern Rock fell by 32 % after it emerged that it

had approached the Bank of England for help. September 2008 witnessed Lloyds bank

make a 12.2 bn takeover of the ailing Halifax Bank of Scotland (HBOS), the UK’s largest

mortgage lender, after its shares plummeted amid concerns over the firm’s future. The UK

government invoked a national interest clause to bypass competition law, as this merger

was responsible for close to one-third of the UK’s savings and mortgage market. October

2008 saw the UK government injected £37 billion in an attempt to rescue RBS and Lloyds-

HBOS as the as financial markets collapsed. In April 2009 the UK Chancellor Alistair

Darling revealed that the credit crunch would lead to the largest budget deficit in UK

financial history of £175 bn, with total government debt set to double to £1 trillion by

2014.10 Mr Darling admitted that it will take approximately 10 years to get the budget back

to the position it was in before the credit crunch.11 These events are plotted along the

timeline by the yellow bars across the FTSE 100 index movement.

Turbulent financial market conditions can result in the correlation between the returns

of financial assets to ‘‘breakdown’’. Increasing correlation during volatile market condi-

tions implies a reduction in portfolio diversification benefits and will have obvious

Source: Bloomberg

8 In autumn 2008, financial markets did move very much in sync, with stock prices around the world fallingby 30 % or more (Bartram and Bodnar 2009).9 On average the Index declined by approximately 10 % between the periods 2004 to 2007 and 2007 to2010.10 These figures were sourced from the financial times and the BBC website.11 However, sentence et al. (2012) state that the substantial increase in the UK house prices and capitalinflows associated with growth of private sector debt combined with a large financial sector exposed toforeign developments led many observers to expect a worse experience than has transpired.


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implications in portfolio asset allocation. Table 1a presents the correlation between the five

industrial returns during the pre-crisis period and the crisis period. Similarly Table 1b

shows the covariance between the industrial returns during the pre-crisis period and the

crisis period.12 The top half of both tables show results from the pre-crisis period and the

bottom halves (the italicized values) present results from the crisis period. As stated earlier

industries under study are: Banking, Retail, Food, Construction and Oil. The tables were

constructed from the sector indices sourced from DataStream for the pre crisis and the

crisis period. The analysis of the correlation of returns provides a simple indicator of the

co-movements of stock indices or a rough measure of market interdependence. Thus, the

size and evolution of the correlation between equity markets is important for proper

diversification. The correlation coefficients are all greater than zero and varying in size

depending on the industries (Table 1a). The highest correlation is larger than 0.6 and the

lowest is less than 0.3. The results indicate that correlation between these five industries

increases to some extend from the pre-crisis to the crisis period. For example, the corre-

lation between Banking industry and oil industry increases from 0.447 to 0.531 and

between the industries of construction and Retail, the correlation increases to 0.659 from

0.575. Table 2b indicates positive covariance among all combinations of the returns during

both periods and this result implies that returns behaviour in a similar manner. The

covariance tends to fall from the pre-crisis to the crisis period. For example the covariance

between the industries of Banking and Construction falls from 0.0004 to 0.00032 and

between Retail and Oil to 0.0002 from 0.00031.

3 The (conditional) CAPM and time-varying beta

One of the assumptions of the capital asset pricing model (CAPM) is that all investors have

the same subjective expectations on the means, variances and co-variances of returns.13

12 We thank the referee for suggesting the correlation and covariance tests.13 See Markowitz (1952), Sharpe (1964) and Lintner (1965) for details of the CAPM.

Table 1 A correlation between industrial returns before and during the crisis period

Pre-crisis period 1 Jan 2004–2030 June 2007

Banks Construction Oil Food Retail

a

Banks 1.00 0.536 0.447 0.482 0.589

Construction 0.575 1.00 0.417 0.416 0.575

Oil 0.531 0.539 1.00 0.362 0.383

Food 0.422 0.490 0.546 1.00 0.465

Retail 0.643 0.659 0.473 0.500 1.00

b

Banks – 0.000040 0.000034 0.000027 0.000035

Construction 0.00032 0.000041 0.000030 0.000045

Oil 0.00032 0.00019 – 0.000027 0.000031

Food 0.00019 0.00013 0.00016 – 0.000027

Retail 0.00041 0.00025 0.00020 0.00027 –

Crisis period 1 July 2007–2030 Oct 2010 (italicized values)


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According to Bollerslev et al. (1988), economic agents may have common expectations on

the moments of future returns, but these are conditional expectations and therefore random

variables rather than constant.14 The CAPM that takes conditional expectations into con-

sideration is sometimes known as conditional CAPM. This conditional CAPM provides a

convenient way to incorporate the time-varying conditional variances and co-variances

(Bodurtha and Mark 1991).15 An asset’s beta in the conditional CAPM can be expressed as

the ratio of the conditional covariance between the forecast error in the asset’s return, and

the forecast error and the conditional variance of the forecast error in the market return.

14 According to Klemkosky and Martin (1975), betas will be time-varying if excess returns are charac-terized by conditional heteroscedasticity.15 Hansen and Richard (1987) have shown that omission of conditioning information, as is done in tests ofconstant beta versions of the CAPM, can lead to erroneous conclusions regarding the conditional meanvariance efficiency of a portfolio.

Table 2 Firm description

Company Abbreviation Industry Total sharesoutstanding(millions)

Market capitalization(£ million) asat 10/01/2011

Kingfisher KGF Retail 2361.974 6,190.73

Marks and Spencer MKS Retail 1583.644 6,092.28

Next NXT Retail 183.321 3,792.92

Inchcape INCH Retail 460.505 1,780.31

Home Retail group HOME Retail 818.633 1,665.1

Barclays BARC Banking 12,181.94 33,591.7

HSBC HSBA Banking 17,686.16 118,921.7

Lloyds LLOY Banking 68,074.13 44,328.05

Royal Bank of Scotland RBS Banking 58,458.13 43,225.02

Standard Charter STAN Banking 2,348.155 40,165.19

British Petroleum BP Oil 18,796.54 91,285.4

Royal Dutch RDSB Oil 3,565.953 133,812

BG Group BG Oil 3,386.378 44,886.44

Tullow TLW Oil 888.237 11,991.2

ENSCO ESV Oil 143.397 4,707.806

James Halstead JHD Land and construction 51,941 389.56

SEGRO SGRO Land and construction 741.537 2,126.73

Keller KLR Land and construction 64.311 420.91

Kier Group KIE Land and construction 37.906 514.77

Capital ShoppingCentres Group

CSCG Land and construction 692.673 2,718.05

Tesco TSCO Food and retail 8,029.803 34,500.05

Sainsbury SBRY Food and retail 1,865.939 7,277.16

Morrison MORW Food and retail 2,657.746 7,221.1

Associated British Foods ABF Food and retail 791.674 9,040.92

Unilever ULVR Food and retail 1,310.156 57,760.29


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The following analysis relies heavily on Bodurtha and Mark (1991) and Choudhry and

Jayasekera (2012). Let Ri,t be the nominal return on asset i (i = 1, 2,…,n) and Rm,t the

nominal return on the market portfolio m. The excess (real) return of asset i and the market

portfolio over the risk-free asset return is presented by ri,t and rm,t respectively. The

conditional CAPM in excess returns may be given as

E ri;tjIt�1

� �¼ biIt�1E rm;tjIt�1

� �ð1Þ

where,

biIt�1 = cov Ri;t; Rm;tjIt�1

� �=var Rm;tjIt�1

� �¼ cov ri;t; rm;tjIt�1

� �=var rm;tjIt�1

� �ð2Þ

and E(|It-1) is the mathematical expectation conditional on the information set available to

the economic agent’s last period (t-1), It-1. Expectations are rational, based on Muth’s

(1961) definition of rational expectation, where the mathematical expected values are

interpreted as the agent’s subjective expectations. According to Bodurtha and Mark (1991),

asset i risk premium varies over time due to three time-varying factors: the market’s

conditional variance; the conditional covariance between the asset’s return; and the mar-

ket’s return and/or the market’s risk premium.

The asymmetric effect16 of news on the volatility of stock returns has been investigated

and evidenced in many past studies (Black 1976; French et al. 1987; Nelson 1991; Schwert

1989). The effect refers to the volatility trends in individual stocks and market indices

where one can observe a rise in volatility following negative returns and a fall following

positive returns. The effect can be rationalised in terms of a leverage (financial and

operational) based explanation, or one based on the determinants of market risk premium.

The former stems from the notion of viewing equity as a call option on the value of the

firm’s assets where the option becomes worthless when the asset value falls below the

liabilities (i.e. the strike price). Thus, if the value of a leveraged firm drops, its equity

becomes highly leveraged, causing an increase in volatility17 (Black 1976; Christie 1982).

The second explanation stems from the positive relationship between volatility (which is a

proxy for risk) and the expected market risk premium (the expected return on a stock

portfolio minus the riskless rates). Under the assumption of a rational investor paradigm,

ceteris parabus, an increase in volatility increases the expected return which in turn lowers

the stock price contributing to the asymmetric effect in volatility (Pindyck 1984; Poterba

and Summers 1986; French et al. 1987; Bollerslev et al. 1988; Engle et al. 1990; Campbell

and Hentschel 1992).

Asymmetry in volatility may also imply asymmetry in time-varying beta. If the risk

premium is an increasing function of the volatility, and the beta is a proxy for risk, then the

asymmetric effect in volatility may imply such an effect occurs in beta too18 (Cho and

Engle 1999). Furthermore, if the beta of a leveraged firm’s asset is positive, the beta of the

firm’s equity should rise in response to negative returns, as the firm takes on more leverage.

Thus, the expected equity betas tend to be increasing with leverage. Further, Braun et al.

16 This is also referred to as the ‘‘leverage effect’’.17 Christie (1982) shows that equity volatility is increasing in financial leverage, and hence there is anegative relationship between the variance of returns and the value of equity. However, Christie (1982) andBlack (1976) point out that financial and operational leverage is not enough to fully account for theasymmetry of volatility.18 According to Brooks and Henry (2002), if the risk premium is increasing in volatility, and if beta is aproper measure of the sensitivity to risk, then time variation and asymmetry in the variance–covariancestructure of returns may lead to time variation and asymmetry in beta.


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(1995) and Ball and Kothari (1989), claim that an increase (decrease) in market shocks to

the firms also increases (decreases) beta and leads to a rise (fall) in expected returns on the

market. This should result in a drop in the stock price contributing to the asymmetric

effect. In this paper we investigate whether any asymmetric effect exists in betas of UK

firms during the pre-crisis and crisis periods.

4 BEKK GARCH modelling framework and the data

As stated earlier, the time-varying beta of each firm in this paper is estimated by means of

the BEKK GARCH model.19 The following bivariate GARCH(p,q) model may be used to

represent the returns from asset i and the market portfolio (m). This presentation is termed

by Engle and Kroner (1995) the BEKK model; the conditional covariance matrix is

parameterized as20

yt ¼ lþ et � het�1 ð3Þ

et=Xt�1�N 0; Htð Þ ð4Þ

vech Htð Þ ¼ C0C þXK

K¼1

Xq

i¼1

A0Kiet�ie0t�iAki þ

XK

K¼1

Xp

i¼1

B0KjHt�jBkj ð5Þ

where yt = (rit, rm,t) is a (2 9 1) vector containing excess returns from asset i and the

market portfolio (m), l is a 2 9 1 vector of constant, Aki, i = 1,…,q, k = 1,…K, and Bkj

j = 1,…p, k = 1,…,K are all N 9 N matrices. This formulation has an advantage over the

general specification of the multivariate GARCH in that conditional variance (Ht) is

guaranteed to be positive for all t (Bollerslev et al. 1992). The moving average (MA) term

het-1 is included to capture the effect of non-synchronous trading. According to Susmel

and Engle (1994), non-synchronous trading induces negative serial correlation, and the MA

term allows for autocorrelation induced by discontinuous trading in the asset.

The time-varying beta (b) for asset i is calculated as

bi;t ¼ H12;t=H22;t: ð6Þ

where H12;t is the estimated conditional covariance between the specific asset returns and

market portfolio returns, and H22;t is the estimated conditional variance of the market

portfolio returns from the bivariate BEKK GARCH model. Given that conditional

covariance and conditional variance are time-dependent, the stock beta will be time-

dependent. The time-varying beta defined in Eq. 6 is applied in this paper.21

Daily stock price indices from twenty five individual firms from the UK are applied in

the tests. The data range from January 1, 2004 to October 30, 2010. The total period is

further broken into the pre-crisis period (1 Jan 2004–30 June 2007) and the crisis period (1

July 2007–30 October, 2010). Table 2 presents the basic information about the firms, the

size of the firm and the industry they belong in. Firms under study are chosen based on the

criteria of size and industry. The size of the firm is based on market capitalisation. The

large variation in the size of the firms and the industries they belong to is clearly visible.

19 Thus we estimate the BEKK model for each firm to create 25 individual time varying betas.20 The BEKK description relies heavily on Choudhry and Jayasekera (2012).

21 We estimate the BEKK GARCH to obtain H12;t and H22;t for each firm to estimate the betas.


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The FTSE All stock index was used as a proxy for the market portfolio. Stock returns are

simply created by taking the first difference of the log of the stock index. The return on the

risk-free asset is represented by the return on the three-month UK Treasury bill. The excess

stock return is calculated as the nominal stock returns minus the returns on the bill. All data

are taken from Datastream International.

5 The bivariate BEKK GARCH results

Given the bulkiness of the BEKK results they are not provided to conserve space but they

are available on request. We provide a basic summary of the BEKK results. The BEKK

bivariate GARCH results are quite standard. The ARCH coefficients (A11 and A22) are all

positive and significant implying volatility clustering in both the firm and the market

returns. All ARCH coefficients are less than unity in size. In all models the GARCH

coefficient is significant and positive implying the GARCH effect. A large coefficient of

the GARCH term indicates that shocks to conditional variance take a long time to die out

and volatility persists. Not much evidence is found indicating a linkage between the

volatilities (A12 and A21) and conditional variances (B12 and B21) of the firm and the

market. Some evidence of non-synchronous trading (h) is found, mostly in the firms. The

significant h are mostly positive. To assess the general descriptive validity of the model, a

battery of standard specification tests is employed. Specification adequacy of the first two

conditional moments is verified through the serial correlation test of white noise. These

tests employ the Ljung-Box Q statistics on the standardised (normalised) residuals

(et=H1=2t ) and standardised squared residuals (et=H2

t ). All series are found to be free of

serial correlation (at the 5 % level). The absence of serial correlation in the standardised

squared residuals implies the lack of need to encompass a higher order ARCH process

(Giannopoulos 1995).

Figure 1 presents five of the estimated betas of the twenty five UK firms. The shaded

region shows the crisis period (2007–2010). Change of movement of the beta before the

crisis and during the crisis is clearly visible, especially in the cases of the banking and retail

industries. Besides the basic movement not much can be deduced and analyzed from the

graphs. Graphs of other firm’s betas provide a somewhat similar story and are available on

request.

Table 3 shows the basic statistics of the betas during the pre-crisis (2004–2007) and

crisis period (2007–2010). The mean of most betas are found to be more than unity,

implying that most of the firms under study are more risky than the market. Only in the

food industry does each firm show less riskiness than the market during both periods. For

most firms the beta increases from the pre-crisis to the crisis period. This is as expected,

and this jump in the betas is especially obvious in the banking, retail and food industries. In

addition, most betas are found to be significantly skewed and/or leptokurtic and thus are

found to be non-normal by means of the Jarque–Bera statistics. This result is not unique, as

Choudhry (2002) also provides evidence of non-normal UK firm daily betas. Application

of the OLS requires that all variables are stationary. The stochastic structure of all twenty

five betas is investigated by means of the augmented Dickey-Fuller root test (ADF).22 All

beta are found to be stationary in levels during both periods. This result is not unique

22 The ADF tests are applied with six lags maximum.


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either, as Choudhry (2002) provides similar results for other UK firms, and Brooks and

Henry (2002) for the UK industrial sectors.

6 The rational and the general approach

Given the evidence on the predictive asymmetry of volatility, we investigate the asym-

metric effect of beta during good and bad periods to good and bad news in the UK equity

market during the pre-crisis and crisis period in order to shed fresh insight on the con-

troversy of the ‘‘abnormalities of stock prices’’. i.e. whether the hypothesis of ‘increased

market efficiency’ or ‘asset mispricing’ would better fit the empirical observations as an

economy slides from a boom to a rescission.

In particular we investigate the following hypothesis. Does any asymmetric effect exist

in betas of UK firms during the pre-crisis and crisis periods? Absence or presence of the

asymmetric effect in betas can lead to two different conclusions. The absence of any

asymmetric effect leads one to conclude that abnormalities of stock return (instances of

asset mispricing) are evidenced, thus demonstrating symptoms of market inefficiency.23

The presence of the asymmetric effect leads one to conclude that abnormalities of stock

returns (instances of asset mispricing) can, at least partially, be explained by changes in

expected returns through a change in beta. If the markets are efficient one would expect to

witness an asymmetric time varying beta in response to good and bad news. This is due to

the following reasons. Firstly, good or bad news both have an asymmetric effect on the

volatility of the stock prices. The asymmetric effect of news on the volatility of stock

returns is well documented (Black 1976; French et al. 1987; Nelson 1991; Schwert 1989).

One can observe a rise/fall in volatility following negative/positive news.24 Secondly, risk

premium is an increasing function of volatility, and the beta is a proxy for risk, thus the

asymmetric effect in volatility may imply such an effect occurs in beta too (Cho and Engle

1999 and Brooks and Henry 2002). If the beta of a leveraged firm’s asset is positive, the

beta of the firm’s equity should rise in response to negative returns, as the firm takes on

more leverage. Thus, the expected equity betas tend to be increasing with leverage just as

does the volatility. Braun et al. (1995) and Ball and Kothari (1989), claim that an increase

(decrease) in market shocks to the firms also increases (decreases) beta and leads to a rise

(fall) in expected returns on the market. This results in a drop in the stock price contrib-

uting to the asymmetric effect. Thus, the presence of market efficiency implies an

asymmetry in the time-varying beta. Conversely an absence of asymmetry indicates market

inefficiency via asset mispricing.

In this paper, the time-varying beta of individual UK firms’ stock returns is investigated

in the context of an asymmetric effect of news, market shocks and idiosyncratic shocks.

23 Previous research by Braun et al. (1995) support the overreaction theory (asset mispricing) by finding aweak asymmetric effect in beta. They conclude, based on the evidence of the low frequency (weekly) data,that betas are not responsive enough to account for the differing return performances of ‘‘winners’’ and‘‘losers’’, and thus support De Bondt and Thaler (1989).24 There are two possible explanations. (1) Leverage based—viewing equity as a call option for the firm’sassets—if the value of a leveraged firm drops, its equity becomes highly leveraged, causing an increase involatility (Black 1976; Christie 1982). (2) Positive relation between volatility and the expected market riskpremium—an increase in volatility increases the expected return which in turn lowers the stock pricecontributing to the asymmetric effect in volatility (Pindyck 1984; Poterba and Summers 1986; French et al.1987; Bollerslev, Engle and Wooldridge, 1988; Engle, Ng and Rothschild, 1990; Campbell and Hentschel1992).


123

Barclays - Banking Industry

2004 2005 2006 2007 2008 2009 2010-1.0-0.50.00.51.01.52.02.53.03.5

Kingfisher - Retail Industry

2004 2005 2006 2007 2008 2009 2010-1.0-0.50.00.51.01.52.02.53.03.5

BP - Oil Industry

2004 2005 2006 2007 2008 2009 2010-1.0-0.50.00.51.01.52.02.53.03.5

Sainsbury - Food Industry

2004 2005 2006 2007 2008 2009 2010-1.0-0.50.00.51.0

1.52.02.53.03.5

Keller - Construction Industry

2004 2005 2006 2007 2008 2009 2010-1.0-0.50.00.51.01.52.02.53.03.5

Fig. 1 Time varying betas


123

Ta

ble

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123

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123

Focusing on these two shocks, we apply a model that allows one to distinguish two shocks

in the beta process. The double beta model specification is used for parsimonious esti-

mation and computability. In this model, market information is used as an explanatory in

the estimation of the volatility and beta of the individual stock returns.

We apply the joint model based on Cho and Engle (1999) and Choudhry and Jayasekera

(2012) detailed below to each individual firm for the bad and good periods as follows. We

use the joint model for conditional time-varying beta (bi,t) which captures both the sys-

temic (market) effects as well as the non market, idiosyncratic effects, and is based on the

assumption that the beta follows an AR (1) process.25

For the good period

bi;tðG:PÞ ¼ cbðG:PÞ þ abðG:PÞ bi;t�1ðG:PÞ � cbðG:PÞ

� �þ diðG:PÞzi;t�1ðG:PÞ

þ dmðG:PÞzm G:Pð Þ;t�1ðG:PÞþet ð7Þ

For the bad period

bi;tðB:PÞ ¼ cbðB:PÞ þ abðB:PÞ bi;t�1ðB:PÞ � cbðB:PÞ

� �þ diðB:PÞzi;t�1ðB:PÞ

þ dmðB:PÞzm B:Pð Þ;t�1ðB:PÞþet ð8Þ

where zi = non-market Shocks, zm = market Shocks, di = Coefficient of the non market

shocks. It shows the level of contribution from the non-market shocks towards the firms

individual beta, dm = Coefficient of the market shocks. It shows the level of contribution

from the market shocks towards the firms individual beta, et = standard error term with

zero mean and constant variance.

Following Cho and Engle (1999), the terms dizi and dmzm, allow for leverage (asym-

metric) effects in the time-varying betas based on non-market and market shocks

respectively. If di is negative and significantly different from zero, the beta (bi) will rise in

response to negative non-market returns (idiosyncratic returns), and fall in response to

positive non-market returns. Thus if di is significant and negative, it could be that there

exists a leverage effect via non-market shocks in the beta process. Similarly, if dm is

negative and significant, the beta rises in response to negative market returns, and falls in

response to positive market returns. In other words, if there is bad news in the market and

such shocks have an asymmetric effect; dm should be significant and negative.

The next step is to establish whether the systemic, market shocks or idiosyncratic, non

market shocks are prevalent in each individual firm using the log likelihood test for each of

the respective periods. The joint model of Eqs. 3 and 4 considers both the non-market and

the market shocks for the time-varying beta. Thus the log likelihood test is used to

determine whether the market shocks or the non market shocks or both are prevalent in the

joint model. Following this filtering process, when only the market shocks are prevalent we

obtain the ‘‘Market model’’, where only the non market shocks are prevalent we get the

‘‘non-market model’’, and the ‘‘joint model’’ is prevalent when both these shocks are

significant. Thus in order to arrive at the non-market model, markets shocks zm,t-1 are

omitted from the joint model by testing the significance of market shocks for the respective

periods.

For the good period for non market effects we test the null (H0) against the alternative

(H1) hypothesis as follows.

25 A zero order for AR in beta gives the beta extreme volatility implying complete stochastic behaviouranalogous to a random walk. Given that beta is a time-varying process, zero order for AR does not seem tobe a realistic model.


123

H0 : dm; G:Pð Þ ¼ 0

H1 : dm; G:Pð Þ 6¼ 0

If the null hypothesis (H0) cannot be rejected, the non-market model is chosen, and it

implies that the beta process is driven only by non-market (idiosyncratic) shocks. Thus we

have the non-market model.


� �þ diðG:PÞzi;t�1ðG:PÞþet ð9Þ

Similarly, the market model is obtained by estimating the significance of non-market

shocks zi,t-1 in the joint model. In this case the non-market shocks zi,t-1 are omitted from

the joint model and we arrive at the market model. The model selection between the joint

model and the market model is based on testing how the non-market volatility affects beta

in the joint model. For market effects on the overall firm beta we test the null (H0) against

the alternative (H1) hypothesis as follows.

H0 : di; G:Pð Þ ¼ 0

H1 : di; G:Pð Þ 6¼ 0

If the null hypothesis (H0) cannot be rejected, the market model is chosen, and it implies

that the beta is only driven by the market shocks. Thus we have the market model.


� �þ dmðG:PÞzm G:Pð Þ;t�1ðG:PÞþet ð10Þ

we follow similar lines of reasoning for the bad period in arriving at the model selection.

Table 4 provides a summary of the model selections.

7 Asymmetric effects test results

Tables 5, 6, 7, 8, 9 presents the asymmetric effects test (models 1–4) results from the five

industries. For each firm two sets of results are presented, one for the pre-crisis period and

one for the crisis period. Each table presents the results from the joint model, the idio-

syncratic model and the market model. These models are estimated by means of ordinary

least squares, and then the covariance matrix estimates are corrected to allow for more

Table 4 Summary of the model selection

Market model (M.M) Non market model (N.M.M) Joint model (J.M)

Good period Test forH0: d i,, (G.P) = 0H1: d i, (G.P) = 0Select M.M if H0

cannot be rejected

Test forH0: dm,(G.P) = 0H1: dm,(G.P) = 0Select N.M.M if H0

cannot be rejected

Is applicable when H1 for boththe hypotheses for the goodperiod cannot be rejected

Bad period Test forH0: d i,, (B.P) = 0H1: d i, (B.P) = 0Select M.M if H0

cannot be rejected

Test forH0: dm,(B.P) = 0H1: dm,(B.P) = 0Select N.M.M if H0

cannot be rejected

Is applicable when H1 for boththe hypotheses for the badperiod cannot be rejected


123

Table 5 Estimation of the Cho and Engle models (industry = banking)

Company a ab di dm H0: di = 0 H0: dm = 0

Barclays—pre-crisis period

Joint model 1.324a

(99.527)-0.0000(-0.3146)

-0.0592c

(-1.9269)0.0966b

(2.1781)– –

Idiosyncratic model 1.318a

(96.309)-0.0000(-0.3122)

-0.0172(-0.9032)

– 0.8158 –

Market model 1.335a

(101.704)-0.0000(-0.2742)

– 0.0339(1.5660)

– 2.4520

Barclays—crisis period

Joint model 1.493a

(81.962)-0.0000(-1.255)

-0.0203a

(-3.4563)0.0307(1.6021)

– –


(86.055)-0.0000(-1.282)

-0.0133b

(-2.500)– 6.2270** –

Market model 1.507a

(84.890)-0.0000(-1.204)

– 0.0016(0.1033)

– 0.0107

HSBC—pre-crisis period

Joint model 0.852a

(143.120)0.0000(0.7890)

-0.0992a

(-5.4800)0.0589a

(3.4010)– –


(139.130)0.0000(0.7005)

-0.0611a

(-5.8428)– 34.1380*** –

Market model 0.863a

(147.000)0.0000(0.5338)

– -0.0216b

(-2.066)– 4.2690**

HSBC—crisis period

Joint model 0.998a

(159.705)-0.0000(-1.088)

-0.0171a

(-4.7455)0.0027(0.4987)

– –


(162.453)-0.0000(-1.119)

-0.0157a

(-7.0794)– 50.1180*** –

Market model 1.002a

(162.587)-0.0000(-1.3115)

– -0.0148a

(-4.0164)– 16.1319***

LLOYDS—pre-crisis period

Joint model 1.068a

(122.726)0.0000(0.670)

-0.0866a

(-4.3960)-0.0152(-0.7615)

– –


(122.125)0.0000(0.733)

-0.0935a

(-6.6802)– 44.6250*** –

Market model 1.083a

(135.820)0.0000(0.3809)

– -0.0890a

(-6.1870)– 38.2776***

LLOYDS—crisis period

Joint model 1.156a

(108.197)0.0000(0.658)

-0.0126a

(-3.640)-0.0163c

(-1.7190)– –


(117.426)0.0000(0.694)

-0.0151a

(-4.912)– 24.1320*** –

Market model 1.167a

(111.453)0.0000(0.572)

– -0.0330a

(-3.691)– 13.6260***

Royal bank of Scotland—pre-crisis period

Joint model 1.086a

(99.302)-0.0000(-1.622)

0.0268(0.6887)

-0.1336a

(-3.2129)– –


(86.240)-0.0000(-1.616)

-0.0368(-1.2504)

– 1.5634 –


123

robust (complex) behaviour of the residuals. In other words, the residuals are corrected for

autocorrelations.

Table 5 presents the results from the banking industry. In general the crisis period

seems to provide support for the market efficiency albeit to a lesser level compared to the

pre-crisis period. Based on the size of the coefficient (in absolute value) the level of

efficiency seems to decline from the pre-crisis to the crisis period. In the cases of HSBC

and Lloyds both periods require the application of the joint model. Thus the betas are

influenced by both the market and the non-market shocks. These two banks indicate

evidence of market efficiency during both periods. In the case of both banks the significant

coefficient on the non-market shock (di) is negative, implying an asymmetric effect due to

non-market shocks. Thus, for these firms the beta (bi) will rise in response to negative non-

market returns (idiosyncratic returns), and fall in response to positive non-market returns.

In absolute value, the size of the coefficient on the non-market shocks is much less than

unity, implying a small size effect of the non-market shocks on the beta. The coefficient on

the market shock provides a similar conclusion. For the remaining banks the model

selection indicates application of different models during the two periods. Application of

different models between the two periods implies to some extent the effect of the crisis. For

example, for the Royal Bank of Scotland during the pre-crisis period the market model is

Table 5 continued


Market model 1.082a

(102.891)-0.0000(-1.626)

– -0.1099a

(-5.648)– 31.9010***

Royal bank of Scotland—crisis period

Joint model 1.380a

(79.395)0.0000(0.738)

0.0264a

(4.3378)-0.0663a

(-4.3000)– –


(70.138)0.0000(0.372)

0.0211b

(2.5530)– 6.5174** –

Market model 1.354a

(79.194)0.0000(0.415)

– -0.0303a

(-2.7138)– 7.3650***

Standard charter—precrisis period

Joint model 1.185a

(116.546)-0.0000(-0.495)

-0.0618a

(-4.0320)0.0417c

(1.9552)– –


(166.505)-0.0000(-0.507)

-0.043a

(-4.579)– 20.9630*** –

Market model 1.198a

(122.127)-0.0000(-0.568)

- -0.0273b

(-2.0923)– 4.3780**

Standard charter—crisis period

Joint model 1.273a

(115.612)-0.0000(-0.802)

-0.0251a

(-3.7140)0.0226b

(2.2644)– –


(117.009)-0.0000(-0.845)

-0.0162a

(-3.5268)– 12.4381*** –

Market model 1.283a

(118.273)-0.0000(-0.905)

– -0.0104(-1.4316)

– 2.0494

t-statistics in the parenthesesa ,b and c Significantly different from zero at the 1, 5 and 10 % level, respectively

***, ** and * Rejection of the null hypothesis at the 1, 5 and 10 % level, respectively


123

Table 6 Estimation of the Cho and Engle models (industry = retail)


Kingfisher—pre-crisis period

Joint model 1.033a

(93.848)-0.0000(-0.1129)

-0.0244(-1.4208)

0.0409c

(1.9467)– –


(92.881)-0.0000(-0.1411)

-0.0095(-0.7630)

– 0.5821 –

Market model 1.039a

(98.982)-0.0000(-0.1818)

– 0.0171(1.2653)

– 1.6000

Kingfisher—crisis period

Joint model 1.095a

(112.467)-0.0000(-0.9245)

-0.0394a

(-5.5346)0.0464a

(6.4505)– –


(109.837)-0.0000(-1.0143)

-0.0191a

(-3.2893)– 10.8190*** –

Market model 1.114a

(121.276)-0.0000(-0.8131)

– 0.0109b

(1.9664)– 3.8666**

Marks and spencer—pre-crisis period

Joint model 0.5849a

(33.066)0.0000(1.3537)

-0.0663b

(-1.9648)-0.0070(-0.1533)

– –


(31.5937)0.0000(1.3586)

-0.0685a

(-2.6281)– 6.9066*** –

Market model 0.603a

(37.0035)0.0000(1.2045)

– -0.0517(-1.4388)

– 2.0702

Marks and spencer—crisis period

Joint model 1.0381a

(58.6119)-0.0000b

(-2.2723)0.0869b

(2.2580)-0.0816b

(-2.5357)– –


(34.095)-0.0000b

(-2.1491)0.0605c

(1.7180)– 2.9510* –

Market model 0.9975a

(86.0985)-0.0000b

(-2.1882)– -0.0087

(-1.1069)– 1.2250

Next—pre-crisis period

Joint model 0.8743a

(95.140)0.0000(0.0109)

0.0056(0.3729)

-0.0589a

(-2.8644)– –


(95.103)0.0000(0.2370)

-0.0145(-1.1490)

– 1.3206 –

Market model 0.873a

(98.387)0.0000(0.013)

– -0.0500a

(-3.5766)– 12.7918***

Next—crisis period

Joint model 1.1108a

(132.075)-0.0000(-0.1746)

-0.0287a

(-4.2634)0.0324a

(4.4611)– –


(131.159)0.0000(0.021)

-0.0140a

(-3.001)– 9.0100*** –


(136.188)0.0000(0.055)

– 0.0051(1.1908)

– 1.4180

Inchcape—pre-crisis period

Joint model 0.9864a

(91.873)0.0000(0.617)

0.0188(1.4516)

-0.1231a

(-5.8971)– –


(94.820)0.0000(0.4900)

-0.0160(-1.1948)

– 1.4277 –


123

applicable and it indicates market efficiency with a negative market shock of small size.

During the crisis period there is some evidence of asset mispricing via the non-market shock.

The coefficient on the non-market shock is positive and significant but also small in size. As

stated earlier, asset mispricing can be in the form of over/under-reaction to information

which essentially suggests market inefficiency. Barclays Bank and Standard Charter also

provide similar results with more evidence of market efficiency than asset mispricing.

Results from the retail industry are shown in Table 6. In general, both periods tend to

provide support for the market efficiency. The model selection does not require a change of

model from pre-crisis to crisis period for all firms except NEXT. Furthermore, in the case

of Inchcape, using the market model in both periods, the coefficients on the market shock

(dm) are negative and significant. There is a decline in the size of the coefficient from the

pre-crisis to crisis period indicating a declining market efficiency. Similar results are

provided by the Home Retail Group via the idiosyncratic model. NEXT also shows similar

results with the market model during the pre-crisis period and the idiosyncratic model

during the crisis period. Declining market efficiency is a clear indication of the affect of the

crisis. Only Kingfisher and Marks and Spencer show some diversity in results between the

two periods. They provide some evidence of asset mispricing. For example in the case of

Marks and Spencer, based on the idiosyncratic model, the non-market shock changes from

a negative to a positive between the two periods. The size of the effect in absolute value is

similar.

Table 6 continued



(96.7686)0.0000(0.6370)

– -0.1061a

(-6.1314)– 37.5930***

Inchcape—crisis period

Joint model 1.0978a

(73.257)-0.0000c

(-1.6980)0.0125b

(2.1653)-0.0549a

(-4.0726)– –


(71.2055)-0.0000(-1.4504)

0.0033(0.4881)

– 0.2383 –


(75.4039)-0.0000c

(-1.7375)– -0.0400a

(-3.3914)– 11.5018***

Home retail group—pre-crisis period

Joint model 0.9071a

(99.1432)0.0000(0.6892)

-0.0248(-1.5909)

0.0070(0.3506)

– –


(100.379)0.0000(0.6623)

-0.0219b

(-2.001)– 4.0030** –


(101.385)0.0000(0.5215)

– -0.0142(-0.9845)

– 0.9693

Home retail group—crisis period

Joint model 1.1462a

(106.793)-0.0000(-0.919)

-0.0244a

(-3.5112)0.0260a

(3.2774)– –


(108.704)-0.0000(-0.9237)

-0.0148a

(-2.8776)– 8.2808*** –


(110.473)-0.0000(-1.0600)

– 0.0031(0.5322)

– 0.2832

t-statistics in the parenthesesa ,b, and c Significantly different from zero at the 1, 5 and 10 % level, respectively



123

Table 7 Estimation of the Cho and Engle models (industry = oil)


British petroleum—pre-crisis period

Joint model 1.053a

(129.290)0.0000(0.2615)

-0.0554a

(-4.0021)0.0664a

(3.9152)– –


(128.287)0.0000(0.3894)

-0.0257a

(-2.7702)– 7.6741*** –

Market model 1.065a

(136.873)0.0000(0.2286)

– 0.0146(1.2146)

– 1.4751

British petroleum—crisis period

Joint model 0.9126a

(151.650)0.0000(0.0303)

0.0052(0.6672)

-0.0168b

(-1.9954)– –


(149.322)0.0000(0.1326)

-0.0041(-0.8607)

– 0.7408 –


(148.685)0.0000(0.0577)

– -0.0120b

(-3.2289)– 10.4256***

Royal Dutch—pre-crisis period

Joint model 1.0581a

(154.770)-0.0000(-0.5463)

-0.0171c

(-1.7684)0.0344a

(2.7367)– –


(158.490)-0.0000(-0.5154)

-0.0026(-0.4099)

– 0.1680 –


(160.877)-0.0000(-0.5784)

– 0.0180c

(1.9475)– 3.7926*

Royal Dutch—crisis period

Joint model 0.9344a

(155.662)-0.0000(-1.2674)

-0.0147b

(-2.3757)0.0113(1.5611)

– –


(156.208)-0.0000(-1.3300)

-0.0067a

(-2.7000)– 7.2889*** –


(157.052)-0.0000(-1.3713)

– -0.0028(-0.8886)

– 0.7896

BG group—pre-crisis period

Joint model 1.1137a

(113.272)-0.0000(-1.4266)

0.0346a

(3.0231)-0.0243(-1.4117)

– –


(114.872)-0.0000(-1.4312)

0.0264a

(3.1450)– 9.8909*** –


(117.368)-0.0000(-1.4741)

– 0.0107(0.8531)

– 0.7278

BG group—crisis period

Joint model 1.0841a

(189.978)-0.0000(-0.174)

0.0205a

(4.7132)-0.0231a

(-3.8321)– –


(190.334)-0.0000(-0.098)

0.0082a

(-3.2685)– 10.6832*** –

Market model 1.077a

(192.118)-0.0000(-0.0802)

– -0.0014(-0.4371)

– 0.1910

Tullow—pre-crisis period

Joint model 1.2202a

(75.280)0.0000c

(1.732)-0.0508a

(-3.8331)0.0359(1.1922)

– –


(76.000)0.0000c

(1.775)-0.0425a

(-4.1652)– 17.3490*** –


123

The results of the oil industry firms (Table 7) provide mixed results. Results provide

evidence supportive of both market efficiency and asset mispricing. British Petroleum is

the only firm that shows evidence of market efficiency during both periods. BG Group and

ENSCO provide evidence of asset mispricing during both periods via the idiosyncratic

mode, although there is indication of a declining asset mispricing. Oil prices can be

difficult to predict thus making it hard for the investors to gauge the risk level. This may

lead to asset mispricing due to the difficulty in determining the premium to compensate for

the estimation of risk resulting from the uncertainty.26 The model selection fails to select

the joint model for any of the firms during any of the two periods.

The results of the food industry firms (Table 8) indicate conclusions similar to the

banking and retail industries. The results from this industry though, do provide support for

the market efficiency and asset mispricing during both periods, especially during the crisis

period. Again the sizes of the coefficient on the market shock (dm) and non-market shock

(di) are smaller during the crisis period, implying declining market efficiency and asset

mispricing. This is clearly visible in the case of Associated British Food where the model

Table 7 continued



(80.514)0.0000c

(1.770)– -0.0309

(-1.1882)– 1.4118

Tullow—crisis period

Joint model 1.0875a

(116.513)0.0000(1.1406)

-0.0028(-0.4268)

0.0108(1.1544)

– –


(116.080)0.0000(1.1833)

0.0019(0.5555)

– 0.3086 –


(117.170)0.0000(1.1502)

– 0.0078c

(1.7459)– 3.0481*

ENSCO—pre-crisis period

Joint model 0.6482a

(35.0147)-0.0000b

(-2.1193)0.0616a

(6.0381)-0.0504b

(-2.0694)– –


(37.7374)-0.0000b

(-2.0647)0.0571a

(5.7372)– 32.9160*** –


(36.141)-0.0000b

(-2.2076)– -0.0122

(-0.4550)– 0.2070

ENSCO—crisis period

Joint model 0.9602a

(90.280)0.0000(0.300)

0.0172a

(4.3981)-0.0163b

(-2.1751)– –


(92.383)0.0000(0.2399)

0.0130a

(4.4767)– 20.0410*** –


(92.4439)0.0000(0.2480)

– 0.0003(0.0549)

– 0.0030



26 ‘‘Appendix’’ shows how repeated attempts at predicting the oil price made by the US Department ofEnergy exhibited huge deviations from the actual price levels, thus serving to illustrate the difficulty inpredicting the movements in the oil prices.


123

Table 8 Estimation of the Cho and Engle models (industry = food)


TESCO—pre-crisis period

Joint model 0.6022a

(61.491)0.0000(0.3386)

0.1417a

(6.2400)-0.2135a

(-9.9630)– –


(57.218)0.0000(0.1765)

0.0663a

(2.9632)– 8.7800*** –


(59.996)0.0000(0.4687)

– -0.1224a

(-6.4621)– 41.7580***

TESCO—crisis period

Joint model 0.8496a

(130.255)0.0000(0.0640)

0.0413a

(4.9658)-0.0527a

(-7.2737)– –


(129.148)0.0000(0.2549)

0.0075(1.3316)

– 1.7732 –


(129.681)0.0000(0.5379)

– -0.0229a

(-4.5405)– 20.6163***

Sainsbury—pre-crisis period

Joint model 0.9046a

(71.881)0.0000(0.3863)

-0.1297a

(-7.7274)0.1740a

(8.3600)– –


(68.447)0.0000(0.1144)

-0.0716a

(-4.0612)– 16.4934*** –


(77.204)-0.0000(-0.2042)

– 0.0680a

(3.0281)– 9.1700***

Sainsbury—crisis period

Joint model 0.9016a

(63.178)-0.0000b

(-2.4187)-0.1116a

(-3.9790)0.1039a

(4.4620)– –


(39.330)-0.0000b

(-2.1472)-0.0668b

(-2.4469)– 5.9873** –


(70.2629)-0.0000b

(-2.0186)– 0.0099

(1.2757)– 1.6270

Morrison—pre-crisis period

Joint model 0.6541a

(48.894)0.0000(0.6237)

0.0853a

(2.8683)-0.1859a

(-6.6402)– –


(39.246)0.0000(0.6042)

0.0502(1.4330)

– 2.0536 –


(61.575)0.0000(1.1366)

– -0.1355a

(-7.8715)– 61.9602***

Morrison—crisis period

Joint model 0.8549a

(167.377)-0.0000(-1.5259)

-0.0196a

(-4.2713)-0.0175a

(-3.9055)– –


(173.752)-0.0000c

(-1.6600)-0.0305a

(-7.1929)– 51.7371*** –


(179.478)0.0000(-1.2782)

– -0.0312a

(-6.8303)– 46.6530***

Associate british food—pre-crisis period

Joint model 0.5570a

(50.833)-0.0000(-0.4346)

0.1912a

(4.0431)-0.1928a

(-5.6200)– –


(36.7359)0.0000(0.1421)

0.1130b

(2.4705)– 6.1033*** –


123

selection picks the joint model during both periods. The positive coefficient on the non-

market shock and the negative coefficient on market shock declines from the pre to the

crisis period without changing the direction of the affect on the beta. Similar analysis can

be extended to the other firms even though different models are employed during the two

periods for each firm. Only Unilever provides somewhat of a unique result. The market

model during the pre-crisis period provides evidence of asset mispricing in the form of a

significant positive coefficient on the market shock. During the crisis period, the coefficient

on the market shock in the joint model is still positive at the same size, however now the

non-market shock is negative indicating some evidence of market efficiency.

The construction and land industry (Table 9) provides results in line with other

industries (except oil). For all firms except Keller different models are selected during both

periods. Both periods support the market efficiency and the declining size of the coeffi-

cients from the pre-crisis to crisis period indicates declining market efficiency. The small

evidence of asset mispricing is quite weak.

So what do our results show? Most firms and industries seem to support the market

efficiency during both periods. The exception to this being the oil industry, though this may

be due to the specific uncertainty surrounding the future oil price determination. The level

of market efficiency however seems to decline significantly from the pre-crisis to the crisis

Table 8 continued



(50.2191)-0.0000(-0.1072)

– -0.0747a

(-4.0529)– 16.4260***

Association British food—crisis period

Joint model 0.8112a

(121.893)-0.0000(-0.0826)

0.0642a

(6.2226)-0.0576a

(-7.5683)– –


(121.002)0.0000(0.0075)

0.0219a

(3.2353)– 10.4670*** –


(120.628)0.0000(0.1500)

– -0.0117b

(-2.5171)– 6.3360**

Unilever—pre-crisis period

Joint model 0.8126a

(99.957)-0.0000(-0.0729)

-0.0474b

(-2.3100)0.0829a

(3.9234)– –


(93.917)0.0000(0.1215)

-0.0169(-1.0503)

– 1.1030 –


(105.460)-0.0000(-0.0214)

– 0.0487a

(2.9128)– 8.4830***

Unilever—crisis period

Joint model 0.7896a

(98.479)-0.0000(-0.6541)

-0.0451a

(-4.7185)0.0487a

(4.8338)– –


(91.3887)-0.0000(-0.7266)

-0.0145a

(-3.0529)– 9.3200*** –


(104.834)-0.0000(-0.6931)

– 0.0171a

(2.704)– 7.3129***




123

Table 9 Estimation of the Cho and Engle models (industry = construction and land)


Keller—pre-crisis period

Joint model 0.4560a

(23.844)-0.0000(-0.5373)

0.0224(1.0329)

-0.2226a

(-7.4935)– –


(27.634)-0.0000(-0.2480)

-0.0165(-0.6665)

– 0.4442 –


(26.219)-0.0000(-0.5182)

– -0.2006a

(-6.7683)– 45.8100***

Keller—crisis period

Joint model 1.0120a

(61.882)-0.0000(-0.4039)

0.0286b

(2.3413)-0.0868a

(-5.2749)– –


(57.991)-0.0000(-0.3119)

0.0027(0.2074)

– 0.0430 –


(63.958)-0.0000(-0.2285)

– -0.0635a

(-5.6392)– 31.8007***

Kier—pre-crisis period

Joint model 0.8499a

(58.456)-0.0000(-0.4207)

-0.0632a

(-3.4516)-0.0070(-0.2403)

– –


(59.294)-0.0000(-0.4294)

-0.0649a

(-4.5247)– 20.4730*** –


(60.782)-0.0000(-0.1584)

– -0.0683a

(-2.9511)– 8.7093***

Kier—crisis period

Joint model 0.9713a

(84.459)-0.0000(-0.6011)

-0.0215a

(-4.0746)0.0132c

(1.8116)– –


(85.704)-0.0000(-0.6035)

-0.0169a

(-3.9754)– 15.8040*** –


(90.152)-0.0000(-0.6783)

– -0.0094(-0.6612)

– 0.4371

James Halstead—pre-crisis period

Joint model 0.2160a

(18.080)-0.0000(-0.6185)

0.0274(1.4927)

-0.0978a

(-5.0644)– –


(22.513)-0.0000(-0.7500)

0.0095(0.4526)

– 0.2099 –


(18.1800)-0.0000(-0.6120)

– -0.0884a

(-4.9327)– 24.3310***

James Halstead—crisis period

Joint model 0.4982a

(43.287)-0.0000(-0.5346)

-0.0158b

(-2.0177)-0.0306a

(-4.0970)– –


(47.768)-0.0000(-0.7155)

-0.0264a

(-3.4232)– 11.7184*** –


(44.621)-0.0000(-0.561)

– -0.0367a

(-5.2736)– 27.8110***

SEGRO—pre-crisis period

Joint model 0.8600a

(76.612)-0.0000(-0.2819)

-0.0097(-0.3809)

-0.0754a

(-2.6745)– –


(73.188)-0.0000(-0.1990)

-0.0372b

(-1.9726)– 3.8911** –


123

period. Our findings show that this decline is most prominent and unambiguous with the

banking industry closely followed by the construction and land industry. These effects

appear to be less pronounced with the retail industry and even less with regards to food,27

although these effects are still prevalent. Both results of market efficiency and declining

market efficiency from the pre-crisis to crisis periods provide ample evidence of the affect

of the current financial crisis on beta.

We present circumstantial evidence by citing the behaviour of the VSTOXX28 Index,

which can be regarded as a proxy for the volatility in the Eurozone from pre-crisis to crisis

period, which strengthens our argument. In general, the volatility in the Eurozone increased

in excess of 226 % during the crisis period (Bloomberg). The average Eurozone volatility

increased from 11.089 from the pre-crisis period to 25.107 during the crisis period. This

behaviour can be interpreted as a decline in the level of market efficiency as this excess

volatility can be attributed to asset prices over/under shooting (i.e. asset mispricing) rel-

ative to their intrinsic values. This in effect would cause a series of repetitive corrections to

Table 9 continued



(81.186)-0.0000(-0.3162)

– -0.0842a

(-5.2800)– 27.8760***

SEGRO—crisis period

Joint model 1.1400a

(113.024)-0.0000(-1.2444)

-0.0007(-0.1538)

0.0122(1.5206)

– –


(115.977)-0.0000(-1.3155)

0.0025(0.6986)

– 0.4880 –


(116.137)-0.0000(-1.2432)

– 0.0115c

(1.8460)– 3.4077*

Capital shopping centre group—pre-crisis period

Joint model 0.9556a

(98.402)0.0000(0.0241)

-0.1039a

(-6.6382)0.0527b

(2.2365)– –


(99.2301)0.0000(0.1304)

-0.0801a

(-8.3980)– 70.5261*** –


(100.142)0.0000(0.6533)

– -0.0585a

(-3.4813)– 12.1194***

Capital shopping centre group– crisis period

Joint model 1.0375a

(122.059)-0.0000(-0.6700)

-0.0120b

(-2.4200)0.0104c

(1.7079)– –


(123.305)-0.0000(-0.7205)

-0.0074b

(-2.1078)– 4.4430** –


(127.661)-0.0000(-0.6801)

– -0.0015(-0.3365)

– 0.1132



27 This is intuitive as the demand for food is relatively inelastic.28 VSTOXX Index, developed by Deutsche Borse and Goldman Sachs is a measure of volatility in theEurozone. It measures implied volatility on options across all maturities.


123

achieve convergence to their intrinsic values in a dynamic sense, thus in theory presenting

more profitable arbitrage opportunities especially for speculators and hedge funds.

Our results have interesting implications to investors, especially those working with

hedge funds. We find that notions of market efficiency hold during the pre-crisis and the

crisis period, however the level of this efficiency declines significantly during the crisis

period. If the markets are efficient this would suggest that the asset prices would converge

almost instantaneously to their intrinsic values, thus wiping off any opportunities of prof-

iting through arbitrage. We find that the level of market efficiency significantly declines as

the UK economy slides into a recession during the crisis period, suggesting a delay in the

convergence of asset prices to their intrinsic values, thus in theory opening up more arbi-

trage opportunities. In a frictionless market value-relevant information is instantaneously

incorporated into market prices. Such a market is deemed to be informational efficient.

Arbitrage is the mechanism that disciplines market prices and achieves this efficiency. In the

absence of trading frictions, costless arbitrage guides the market price discovery towards the

‘‘fundamental values’’ at least in the medium term. Trading costs introduce constrained

arbitrage pressure and the incorporation of information into market prices can be sub-

stantially delayed. Lesmond et al. (2004) conclude that the delay in price adjustment for

security returns simply reflects the costs of arbitrage creating an illusion of anomalous price

behaviour and momentum trading profit opportunity when, in fact, none exists. Their evi-

dence suggests that momentum patterns are largely an artefact of the slow price updating of

high transaction cost stocks. The model we apply assesses the level of market efficiency

based on the share price fluctuations and the speed of the price adjustment process. We find

that the level of efficiency of the UK equity market has dropped from the pre-crisis to the

crisis period, and this may be due to the presence of trading friction in the form of trans-

action costs as suggested by Lesmond et al. (2004). However, we do not instigate the level

of changes in the transaction costs in the UK during the transition from the pre-crisis to

crisis period here and hence do not wish to attribute the decline in the level of market

efficiency purely to this reason. Neither do we study the actual quantum of the arbitrage

profits that can be realised in order to comment on the ‘‘illusory’’ nature of these profits as

claimed by Lesmond et al.(2004), as this is be beyond the scope of this paper. However we

believe that establishing to what extent these theoretical profits can be realised, especially in

the UK equity markets during the crisis period, is an important area that warrants future

research.

8 Conclusion

The controversy surrounding the abnormality of stock prices has been a subject of

extensive research over the past few decades. The controversy can essentially be viewed as

two competing mutually independent hypotheses explaining certain aspects of stock price

behaviour. The first, ‘asset mispricing’, puts forth a behavioural finance argument to

explain certain anomalies of stock price behaviour. The alternative argument, for ‘market

efficiency’ serves to enforce the efficient market hypothesis and provide evidence that the

beta of individual stock rises (falls) in response to abnormally negative (positive) returns,

and argues that this asymmetric response to good and bad news explains the performance

of stock returns.

Given the evidence on the predictive asymmetry of volatility, we investigate the

asymmetric effect of beta during good and bad periods to good and bad news and shed

fresh insight on the controversy of the ‘‘abnormalities of stock prices’’. i.e. whether the


123

hypothesis of ‘increased market efficiency’ or ‘asset mispricing’ would better fit the

empirical observations as an economy slides from a boom to a recession. We focus on

the UK equity market, analysing the relative affects on five major industrial segments

using a bivariate BEKK GARCH approach. This framework is used to capture the affects

of a period when the UK economy slid from relative prosperity (pre-crisis) to a reces-

sion, i.e. the current financial crisis period. We define the pre-crisis period (which we

refer to as the ‘‘good’’ period) from January 2004 to June 2007 and the crisis period

(which we refer to as the ‘‘bad’’ period) to commence from June 2007 to September

2010. Thus, we investigate seven years of daily data, classified under 5 major industries

covering 25 companies in total. The industries under study are; Banking, Retail, Food,

Construction and Oil.

So what do our results show? The GARCH results are quite standard. We document,

for the first time using UK equity market data, the level of market efficiency as the

economy slides from a relative boom to a recession. We find that most firms and

industries seem to support the market efficiency hypothesis during both periods. The

exception to this being the oil industry, which result may be due to the uncertainty

surrounding the future oil price determination. The level of market efficiency, however,

seems to decline significantly from the pre-crisis to crisis period. Some exceptions are

provided by a few firms in the retail industry, perhaps due to the inelastic demand and

less uncertainty surrounding the determination of factor costs. These results have

implications to investors, especially hedge funds, as essentially they would be presented

at least in theory with relatively more profitable arbitrage opportunities during a crisis

period.

As pointed out by Malkiel (2003) pricing irregularities and even predictable patterns in

stock returns can appear over time and even persist for short periods. Markets cannot be

perfectly efficient, or there would be no incentive for professionals to uncover the infor-

mation that gets so quickly reflected in market prices, a point stressed by Grossman and

Stiglitz (1980). In this context our study is a relatively short term study. It is quite plausible

that the markets may behave less efficiently during certain periods, in the short term at

least. As Fama (1998) concludes, the market efficiency hypothesis offers a simple answer

to this type of inefficient period where the market prices stray from the fundamental values.

Specifically, Fama (1998) claims that the expected value of abnormal returns is zero, but

chance generates apparent anomalies that split randomly between overreaction and under-

reaction. On the other hand, consistently generating arbitrage profits by exploiting these

anomalies may not be straightforward. As Timmermann and Granger (2004) claim, stable

forecasting patterns are unlikely to persist for long periods of time and will self-destruct

when discovered by a large number of investors. Both results of market efficiency and

declining market efficiency from the pre-crisis to crisis periods provide ample evidence of

the asymmetric effect of the current financial crisis on beta. Given the status of the current

crisis our results advocate future research in this field using data from different countries,

different firms and using different methods.

Acknowledgments We thank an anonymous referee for several useful comments. Any remaining errorsare the authors’ responsibility.

Appendix

See Fig. 2.


123

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