Sains Malaysiana 38(5)(2009): 745–749

 

 

A Simple Power-Law Tail Estimation of Financial Stock Return

(Penganggaran Hukum-Kuasa Taburan Hujung terhadap Pulangan Saham Kewangan)

 

 

Chin Wen Cheong*

Faculty of Information Technology, Multimedia University

63100 Cyberjaya, Selangor, D.E., Malaysia

 

Abu Hassan Shaari Mohd Nor

Faculty of Economic and Business, University Kebangsaan Malaysia

43600 UKM Bangi, Selangor, D.E., Malaysia

 

Zaidi Isa

Faculty of Science Technology, University Kebangsaan Malaysia

43600 UKM Bangi, Selangor, D.E., Malaysia

 

Diserahkan: 22 September 2008 / Diterima: 11 November 2008

 

 

ABSTRACT

 

This study proposes a simple methodology to estimate the power-law tail index of the Malaysian stock exchange by using the maximum likelihood Hill’s estimator. Recursive procedures base on empirical distribution tests are use to determine the threshold number of observations in the tail estimation. The threshold extreme values can be selected bases on the desired level of p-value in the goodness-of-fit tests. Finally, these procedures are apply to three indices in the Malaysian stock exchange.

 

Keyword: Goodness-of-fit test; Hill estimator; power-law distribution; stock exchange

 

ABSTRAK

 

Kajian ini bertujuan menganggarkan indeks hukum kuasa taburan hujung ke atas bursa saham Malaysia dengan menggunakan penganggar Hill. Prosedur rekursif berdasarkan ujian taburan empirik digunakan untuk menentukan nombor ambang bagi pencerapan di dalam penganggaran hujung. Nilai ambang melampau dipilih berdasarkan kepada aras nilai-p ujian ketepatan padanan. Akhir sekali, prosedur ini dilaksanakan ke atas tiga indek di bursa saham Malaysia.

 

Kata kunci: Bursa saham; penganggar Hill; taburan hukum-kuasa; ujian ketepatan padanan

 

RUJUKAN

 

Bouchaud, J.P. 2001. Power laws in economics and finance: Some ideas from physics. Quantitative Finance 1(1): 105-112.

Clementi, F., Matteo, T.D. & Gallegati, M. 2006. The power-law tail exponent of income distributions. Physica A 370: 49-53.

Coronel-Brizio, H.F. & Hernandez-Montoya, A.R. 2005. On fitting the Pareto-Levy distribution to stock market index data: selecting a suitable cutoff value. Physica A 354: 437-449.

Franke, J., Hardle W.K. & Hafner, C.M. 2004. Introduction to Statistics of Financial Markets. Germany: Springer-Verlag.

Giot, P. 2004. Modelling daily value at risk using realized volatility and ARCH type models. Journal of Empirical Finance 11(3): 379-398.

Goldstein, M.L., Morris, S.A. & yen, G.G. 2004. Problems with fitting to the power-law distribution. The European Physical Journal B 41(2): 255-258.

Hall, P. 1990. Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems. Journal of Multivariate Analysis 32: 177-203.

Hill, B. M. 1975. A simple general approach to inference about the tail of a distribution. Annals of Statistics 3: 1163-1173.

Jorion, P. 2002. Value-at-Risk: The new benchmark for controlling market risk. Chicago: McGraw-Hill.

Lambert, P. & Laurent, S. 2001. Modelling financial time series using GARCH–type models and a skewed Student density. Mimeo. UniversitŽ de Lige.

Loretan, M. & Phillips, P.C.B. 1994. Testing the covariance stationarity of heavy-tailed time series. Journal of Empirical Finance 1: 211-248.

Lux, T. 1996. The stable Paretian hypothesis and the frequency of large returns: an examination of major German stocks. Applied Financial Economics 6(6): 463-475.

Lux, T. 2001. The limiting extremal behaviour of speculative returns, an analysis of intradaily data from the Frankfurt Stock Exchange. Applied Financial Economics 11(3): 299-315.

Sarah, L. 2000. Estimation of Value at risk by extreme value methods. Extremes 3(2): 107-144.

 

*Pengarang untuk surat-menyurat; email: wcchin@mmu.edu.my

 

 

 

 

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