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
Received: 22 September 2008 / Accepted:
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
REFERENCES
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 Lige.
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.
*Corresponding author; email: wcchin@mmu.edu.my
|