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Excess kurtosis of conditional distribution for daily stock returns: the case of Japan

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  • Toshiaki Watana

Abstract

Not only the unconditional distribution but also the conditional distribution of daily asset returns are known to be leptokurtic. Thus, some authors have suggested using ARCH-type models with leptokurtic distributions such as the t-distribution and the generalized error distribution (GED) for the conditional distribution. The purpose of this paper examines what distribution is fit for the conditional distribution of daily Japanese stock returns. In particular, we estimate an exponential GARCH (EGARCH) model developed by Nelson (1991) jointly with the generalized t-distribution, which nests both the Student's t-distribution and the generalized error distribution (GED) employed by other researchers. It is shown that the Student's t-distribution is suited for capturing the excess kurtosis of conditional distribution for daily Japanese stock returns, which does not depend on sample period and is consistent with what Bollerslev et al. (1994) have found by fitting a similar model to daily US stock returns.

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  • Toshiaki Watana, 2000. "Excess kurtosis of conditional distribution for daily stock returns: the case of Japan," Applied Economics Letters, Taylor & Francis Journals, vol. 7(6), pages 353-355.
    • Handle: RePEc:taf:apeclt:v:7:y:2000:i:6:p:353-355
    DOI: 10.1080/135048500351267
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    References listed on IDEAS

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    1. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(3), pages 428-457, December.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    5. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
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    1. Hall, Peter & Yao, Qiwei, 2005. "Approximating conditional distribution functions using dimension reduction," LSE Research Online Documents on Economics 16333, London School of Economics and Political Science, LSE Library.
    2. Asai, Manabu, 2009. "Bayesian analysis of stochastic volatility models with mixture-of-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2579-2596.
    3. Fabio Pizzutilo, 2013. "The Distribution of the Returns of Japanese Stocks and Portfolios," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(9), pages 1249-1259, September.
    4. Fukuhara, Masahiro & Saruwatari, Yasufumi, 2003. "An Analysis of Contagion in Emerging Currency Markets Using Multivariate Extreme Value Theory," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 21(2), pages 113-131, August.
    5. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.

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