1 / 24

Tamara Teplova , Professor, Head of Master Program “Financial Markets”,

EUROPEAN FINANCIAL MANAGEMENT SYMPOSIUM. The Validity of the CAPM in the Russian and Kazakhstan Capital Market with Conditional Meansemivariance and Higher-order Moments Specifications. Tamara Teplova , Professor, Head of Master Program “Financial Markets”,

juliecallahan
Download Presentation

Tamara Teplova , Professor, Head of Master Program “Financial Markets”,

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EUROPEAN FINANCIAL MANAGEMENT SYMPOSIUM The Validity of the CAPM in the Russian and Kazakhstan Capital Market with Conditional Meansemivarianceand Higher-order Moments Specifications TamaraTeplova, Professor, Head of Master Program “Financial Markets”, StateUniversity - HigherSchoolofEconomics, EvgeniyaShutova, StateUniversity - HigherSchoolofEconomics, Russia. Beijing ,2011

  2. The Laboratory of Financial Markets Analysis http://fmlab.hse.ru

  3. The popularity of the CAPM in a period of crisis has not decreased • The studies based on the surveys of over 11 thousand US chief financial directors conducted by the Duke University and the CFO Magazine have shown that nearly 75% of respondents used the CAPM framework to take decisions in 2008 and 2009. • Source: Graham, John; Campbell Harvey, Equity risk premium amid a global financial crisis, Evidance from the Global CFO Outlook survey 2009. SSRN WP; Graham, J. R., C. R. Harvey, 2009, The CFO Global Business Outlook: 1996-2009. http://www.cfosurvey.org.

  4. Whether the CAPM beta completely measures systematic risk? Critical assumptions of CAPM: • quadratic investor’s utility function • the assumption of the normal distribution of returnsthe mean and variance suffice to describe thedistribution completely Evidence from emerging markets: • no simultaneous symmetrical and normal distribution of the expected return(high kurtosis, asymmetry) • law liquidity of most equities serious problems such as understating the beta which is calculated using the regression method

  5. Purpose of investigation development of alternative CAPM specifications The replacement the original beta by downside systematic risk measures. Incorporating higher-order moments. Using conditional CAPM instead of unconditional construction.

  6. Methodology Two-step procedure Fama and MacBeth (1973) • The estimation of risk factors of each individual stocks (time-series regressions). • The estimation of the cross-sectional relationship between the mean return of assets and estimated risk factors.

  7. Downside measures of risk • Beta of Harlow and Rao (1989) with benchmark • equal to mean • equal to zero • Beta of Estrada (2002) with benchmark • equal to mean • equal to zero • Gain-Loss Spread (GLS) of Estrada (2008)

  8. Higher-order moments as SystematicRiskFactors • Systematicskewness(co-skewness, third moment, gamma) • The corresponding measure of downside co-skewness risk (HR-gamma) to HR beta: • The measure of systematic downside co-skewness risk (E-gamma): • Systematickurtosis (co-kurtosis, forth moment, delta)

  9. Conditional Capital Asset Pricing Models where when and where Impact evaluation of general market conditions on the adequacy of asset pricing models Theconditionalfour-moment model САРМ:

  10. Literature review Downside Higher-order Conditional framework moments construction Bawa&Lindenberg(1977), Arditti (1971), Pettengill, Sundaram Harlow & Rao (1989), Francis (1975) , and Mathur (1995), Estrada (2002, 2007) Kraus&Litzenberger(1976), Galagedera&Maharaj (2004)

  11. The object of research ASIA ? Kazakhstan Russia 49 Russiancompanies 10 Kazakhstan companies • Source of data: MICEX and KASE • Sample period: January 2006 – December 2010 • Frequencyofdata: weekly returns. • Weeklyreturnsarecalculatedas:

  12. Russian and Kazakhstan stock exchanges Russia Kazakhstan RTS MICEX KASE

  13. Dynamics of KZKAKand MICEX index for the period: 01/2008-12/2010 KZKAK index MICEX index

  14. 30-day volatility of KZKAK and MICEX during 01/2008-12/2010

  15. Top 10 summary statistics of Russian companies: January 2010 –December 2010

  16. Top 10 summary statistics of Kazakhstan companies: January 2010 –December 2010

  17. Testing hypothesis Downside risk measures are better for explaining cross-sectional return variations than traditional beta especially during the crisis. The inclusion of higher-order moments (the gamma coefficient of systematic asymmetry and the delta coefficient of systematic kurtosis) may contribute to the explanatory power of one- and-multi-factor models. Co-skewness plays a more important role in explaining Russian returns while co-kurtosis is consistently influential for Kazakhstan stock returns due to the fact that Russian stocks are more skewed but less leptokurtic than Kazakhstan stocks.

  18. Are traditional and downside beta good measures of risk?

  19. Are traditional and downside co-skewness good measures of risk?

  20. Conclusion The explanatory power does improve in terms of a higher coefficient of determination if the traditional CAPM beta coefficient is replaced by one-sided risk measures. The zero rate of return benchmark, the models display better explanatory power. The downside beta specification of Harlow and Rao (1989) proves to be more efficient in explaining cross-sectional return variations than that of Estrada (2007). Gain-Loss Spread is the best measure of risk among analyzed factors in downside constructions for Kazakhstan market. refutation the hypothesis that the inclusion of higher-order moments may better explain cross-sectional return variations

  21. What next? Future Investigations We postulate that the differences in results are related to the underlying firm characteristics of the companies in the two indices taking into account investors’ expectations and size of companies using portfolio formation via ranking by BV/MV and size of companies

  22. Thank you for your attention!

More Related