ISSN: 1450-2889 Issue 6 (2010)
© EuroJournals Publishing, Inc. 2010 http://www.eurojournals.com/MEFE.htm Exploring Stability of Systematic Risk: Sectoral Portfolio
Analysis
Ibrahim A.Onour
Arab Planning Institute, Kuwait
E-mail: onour@api.org.kw or ibonour@hotmail.com
Abstracts
Results in this paper support evidence of time-varying systematic risk (beta coefficients) for five sectors, their securities are traded in Kuwait Stock Market. The paper indicates banks, and real estate sectors exhibit relatively wider range of systematic risk variation compared to the other sectors. As higher volatility in risk factor imply additional difficulty in managing and controlling risk, then wider range of systematic risk imply more exposure to risk. This new interpretation of risk evaluation adds a new element to risk assessment tools, since the standard CAPM approach views risk as high or low depending on whether it is greater or lower than the market beta, which is a unit.
Keywords: Systematic risk; Beta; CAPM; GARCH ;Volatility; Asymmetry
JEL Classification Codes: C10, C50, G10
1. Introduction
How should a rational investor measure the risk of stock market investments? The search for an answer to this question became the major task in financial economics and that led to the development of the
Capital Asset Pricing Model (CAPM) which became the centre piece in modern finance textbooks. The
CAPM decomposes risk valuation into risk size (risk premium) and risk price (beta1). According to
CAPM the required rate of return on a company’s stock (or the cost of equity capital) depends on three components among which the stock’s equity beta which measures the risk of company’s stock relative to the market risk; or putting it differently, the risk each dollar invested in equity i contributes to the market portfolio. CAPM predicts that low beta stocks should offer low stock returns and higher beta stocks should offer higher stock returns. This
References: Diebold, F., and Mariano, R., (1995) “Comparing Predictive Accuracy” Journal of Business and Economic Statistics, Vol.13, No.3, pp Engle, R., and Ng, V., (1993) “Measuring and Testing The Impact of News on Volatility” The Journal of Finance, Vol.48, pp Hansen, B., (1994) “Autoregressive Conditional Density Estimation” International Economic Review, Vol Harvey, C., and Siddique, A., (1999) “Autoregressive Conditional Skewness” Journal of Financial and Quantitative Analysis, Vol.34, pp.465-487. Glosten, L., Jagannathan, R., and Runckle, D., (1993) “On the Relation Between The Expected Value and the Volatility of the Nominal Excess Return on Stocks” Journal of Finance, Vol.48, pp. 1779-1802. Faff, R., Hillier, D., and Hillier, J., (2000) “Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques” Journal of Business Finance and Accounting, Vol.27, Fama, E., and French, K., (1992) “The Cross-Section of Expected Stock Returns” Journal of Finance, Vol.47, No.2, pp.427-465. Fama, E., and French, K., (1993) “Common Risk Factors in the Returns on Stocks and Bonds” Journal of Financial Economics, Vol.33, No.1, pp.3-56. Fama, E., and French, K., (1995) “Size and Book-to-Market Factors in Earnings and Returns” Journal of Finance, Vol Fama, E., and French, K., (1996) “Multifactor Explanations of Asset Pricing Anomalies” Journal of Finance, Vol Fama, E., and French, K., (1997) “Industry Costs of Equity” Journal of Financial Economics, Vol.43, No.2, pp.153-193. Jondeau, E., and Rockinger, M., (2000) “Conditional Volatility, Skewness and Kurtosis: Existence and Persistence” Working Paper, HEC School of Management. Kanwer, A., (2006) “Exploring Time Variation in Betas in Pakistan” Manuscript, International Middle East Economic Association Conference, Dubai, UAE Lie, F., Brooks, R., and Faff, R., (2000) “Modelling the Equity Beta Risk of Australlian Financial Sector Companies” Australian Economic Papers, Vol.39, pp.301-311. McKenzie, M., Brooks R., and Faff, R. and Ho Y. (2000) “Exploring the Economic Rationale of Extremes in GARCH Generated Betas: The Case of U.S., Banks.” The Quarterly Review of Moonis, S., Shah, A. (2003) “Testing for Time Variation in Beta in India” Journal of Emerging Markets Finance, Vol.2, No.2, pp.163-180. Patton, A., (2004) “On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Collection” Journal of Financial Econometrics, Vol.2, No.1, pp Yu, J., (2002) “Forecasting Volatility in The New Zealand Stock Market” Applied Financial Economics, Vol.12, pp.193-202. Whister, D., and White, K., (2004): Shazam Software, and Users Reference Manual, Version 10, Northwest Econometrics Ltd. Zakoian, J., and Rabemananjara, R.,(1993) “Threshold ARCH Models and Asymmetries in Volatility” Journal of Applied Econometrics, Vol Middle Eastern Finance and Economics - Issue 6 (2010) 14