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Review of Quantitative Finance and Accounting

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01-02-2015 | Original Research

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

Authors: Taufiq Choudhry, Ranadeva Jayasekera

Published in: Review of Quantitative Finance and Accounting | Issue 2/2015

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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 efficiency 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.

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Harel et al. (2011) provide an analysis of efficient markets.

Cho and Engle (1999), finding evidence of asymmetric effects in betas of US firms, claim that this implies that 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).

Of course, the crisis has carried on beyond 2010.

Veronesi (1999) presents a two state continuous time hidden Markov chain model to explain the stock market under-reaction to bad news in good times. We use a BEKK GARCH framework analysing the impact of good and bad news in good and bad periods and present empirical evidence for 5 major industry classifications leading up to and during the recent financial crisis period.

Dwyer and Lothian (2012) state that cross-country evidence and analyses of individual countries suggest a common explanation to the cause of the financial crisis is likely to be based in rapid credit expansion and economic growth. Dias and Ramos (2013) study the behaviour of the banking sector of 40 countries during the period 2007–2010. They show that although there were periods of intense contagion, the impact was uneven among sample countries. Marsh and Pfleiderer (2012) provide a discussion of black swans and the financial crisis.

However, Kamin and DeMarco (2012) conclude that issues with U.S. Sub Prime mortgages more plausibly were a wake-up call about banking problems around the world than a direct cause of those problems.

See Shin (2009). Reflections on Northern Rock.

In autumn 2008, financial markets did move very much in sync, with stock prices around the world falling by 30 % or more (Bartram and Bodnar 2009 ).

On average the Index declined by approximately 10 % between the periods 2004 to 2007 and 2007 to 2010.

These figures were sourced from the financial times and the BBC website.

However, sentence et al. (2012) state that the substantial increase in the UK house prices and capital inflows associated with growth of private sector debt combined with a large financial sector exposed to foreign developments led many observers to expect a worse experience than has transpired.

We thank the referee for suggesting the correlation and covariance tests.

See Markowitz (1952), Sharpe (1964) and Lintner (1965) for details of the CAPM.

According to Klemkosky and Martin (1975), betas will be time-varying if excess returns are characterized by conditional heteroscedasticity.

Hansen and Richard (1987) have shown that omission of conditioning information, as is done in tests of constant beta versions of the CAPM, can lead to erroneous conclusions regarding the conditional mean variance efficiency of a portfolio.

This is also referred to as the “leverage effect”.

Christie (1982) shows that equity volatility is increasing in financial leverage, and hence there is a negative relationship between the variance of returns and the value of equity. However, Christie (1982) and Black (1976) point out that financial and operational leverage is not enough to fully account for the asymmetry of volatility.

According to Brooks and Henry (2002), if the risk premium is increasing in volatility, and if beta is a proper measure of the sensitivity to risk, then time variation and asymmetry in the variance–covariance structure of returns may lead to time variation and asymmetry in beta.

Thus we estimate the BEKK model for each firm to create 25 individual time varying betas.

The BEKK description relies heavily on Choudhry and Jayasekera (2012).

We estimate the BEKK GARCH to obtain \({\hat{\text{H}}}_{{ 1 2,{\text{t}}}}\) and \({\hat{\text{H}}}_{{ 2 2,{\text{t}}}}\) for each firm to estimate the betas.

The ADF tests are applied with six lags maximum.

Previous research by Braun et al. (1995) support the overreaction theory (asset mispricing) by finding a weak 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).

There are two possible explanations. (1) Leverage based—viewing equity as a call option for the firm’s assets—if the value of a leveraged firm drops, its equity becomes highly leveraged, causing an increase in volatility (Black 1976; Christie 1982). (2) Positive relation between volatility and the expected market risk premium—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, Engle and Wooldridge, 1988; Engle, Ng and Rothschild, 1990; Campbell and Hentschel 1992).

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

“Appendix” shows how repeated attempts at predicting the oil price made by the US Department of Energy exhibited huge deviations from the actual price levels, thus serving to illustrate the difficulty in predicting the movements in the oil prices.

This is intuitive as the demand for food is relatively inelastic.

VSTOXX Index, developed by Deutsche Borse and Goldman Sachs is a measure of volatility in the Eurozone. It measures implied volatility on options across all maturities.

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Title: Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis
Authors: Taufiq Choudhry
Ranadeva Jayasekera
Publication date: 01-02-2015
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 2/2015
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-013-0404-6

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