The distribution of extreme share return in different Malaysian economic circumstances
DOI:
https://doi.org/10.11113/mjfas.v16n1.1356Keywords:
Value-at-risk (VaR), Extreme share returns, Bursa Malaysia, Kuala Lumpur Stock Exchange, Generalized lambda distribution (GLD)Abstract
This study evaluated the performance of probability distribution in various financial periods by investigating the effect of economic cycle on extreme stock return activity. Malaysian stock price KLCI data from 1994–2008 were split into three economy periods correspond to the growth, financial crisis, and the recovery. Four prevalent distributions specifically generalized lambda distribution (GLD), generalized extreme value (GEV), generalized logistic (GLO), and generalized pareto (GPA) were employed to model weekly and monthly maximum and minimum Kuala Lumpur Composite Index (KLCI) share returns. The L-moment approach was used to estimate the parameter while k-sample Anderson darling (k-ad) test was applied to measure the goodness of fit estimation. In conclusion, GLD is the most appropriate distribution representing a weekly maximum minimum return for overall three economic scenarios in Malaysia.
References
Ariff, M., Abubakar, S. Y. 1999. The Malaysian financial crisis: Economic impact and recovery prospects. The Developing Economies, 37(4), 417–438.
Asquith, W. H. 2007. L-moments and TL-moments of the generalized lambda distribution. Computational Statistics & Data Analysis, 51(9), 4484–4496.
Bloom, N., Floetotto, M., Jaimovich, N., Saporta‐Eksten, I., Terry, S. J. 2018. Really uncertain business cycles. Econometrica, 86(3), 1031–1065.
Chen, N.-F., Roll, R., Ross, S. A. 1986. Economic forces and the stock market*. The Journal of Business, 5921236(3), 383–403.
Danielsson, J., Hartmann, P., de Vries, C. 1998. The cost of conservatism. Risk, 11(1), 101–103.
Fama, E. F. 1965. The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.
Garcia, R., Mantilla-Garcia., D., Martellini, L. 2014. A model-free measure of aggregate idiosyncratic volatility and the prediction of market returns.
Journal of Financial and Quantitative Analysis, 49(5–6), 1133–1165.
Gomes, J., Kogan, L., Zhang, L. 2003. Equilibrium cross section of returns. Journal of Political Economy, 111(4), 693–732.
Gray, J. B., French, D. W. 1990. Empirical comparisons of distributional models for stock index returns. Journal of Business Finance & Accounting, 17(3), 451–459.
Harris, R. D. F., Kucukozmen, C. C. 2001. The empirical distribution of stock returns: Evidence from an emerging european market. Applied Economics Letters, 8(6), 367–371.
Hosking, J. R. M. 1990. L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society, 52(1), 105–124.
Hussain, S. I., Li, S. 2015. Modeling the distribution of extreme returns in the chinese stock market. Journal of International Financial Markets, Institutions and Money, 34, 263–276.
Longin, F. M. 1996. The asymptotic distribution of extreme stock market returns. Journal of Business, 69(3), 383–408.
Longin, François M. 2000. From value at risk to stress testing: The extreme value approach. Journal of Banking & Finance, 24(7),1097–1130. http://linkinghub.elsevier.com/retrieve/pii/S0378426699000771.
Marsani, M. F., Shabri, A., Jan, N. A. M. 2017. Examine generalized lambda distribution fitting performance: An application to extreme share return in malaysia. Malaysian Journal of Fundamental and Applied Sciences, 13(3), 229-236.
Moore, G. H., Shiskin, J. 1967. Front matter, indicators of business expansions and contractions. In Indicators of Business Expansions and Contractions (pp. 10–16). New York: Columbia University Press.
Peiro, A. 1994. The distribution of stock returns: International evidence. Applied Financial Economics, 4(6), 431–439.
Scholz, F. W., Stephens, M. A. 1987. K-sample Anderson–Darling tests. Journal of the American Statistical Association, 82(399), 918–924.
De Silva, H., Sapra, S., Thorley, S. 2001. Return dispersion and active management. Financial Analysts Journal, 57(5), 29–42.
Solnik, B., Roulet, J. 2000. Dispersion as cross-sectional correlation. Financial Analysts Journal, 56(1), 54–61.
Theodossiou, P. 1998. Financial data and the skewed generalized T distribution. Management Science, 44(12-part-1), 1650–1661.
Tolikas, K. 2014. Unexpected tails in risk measurement: Some international evidence. Journal of Banking and Finance, 40(1), 476–493.
Xub, Y. J. 1995. A generalization of the beta distribution with applications. Journal of Econometrics, 66(1), 133–152.
Zamowitz, V., Boschan, C. 1975. Cyclical indicators: An evaluation and new leading indexes. Business Conditions Digest, 1975(May), 5–22.
