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Heavy-tailed value-at-risk analysis for Malaysian stock exchange

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  • Chin, Wen Cheong

Abstract

This article investigates the comparison of power-law value-at-risk (VaR) evaluation with quantile and non-linear time-varying volatility approaches. A simple Pareto distribution is proposed to account the heavy-tailed property in the empirical distribution of returns. Alternative VaR measurement such as non-parametric quantile estimate is implemented using interpolation method. In addition, we also used the well-known two components ARCH modelling technique under the assumptions of normality and heavy-tailed (student-t distribution) for the innovations. Our results evidenced that the predicted VaR under the Pareto distribution exhibited similar results with the symmetric heavy-tailed long-memory ARCH model. However, it is found that only the Pareto distribution is able to provide a convenient framework for asymmetric properties in both the lower and upper tails.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 387 (2008)
Issue (Month): 16 ()
Pages: 4285-4298

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Handle: RePEc:eee:phsmap:v:387:y:2008:i:16:p:4285-4298

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Value-at-risk; Heavy-tail distribution; ARCH models; Financial time series;

References

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  1. Loretan, Mico & Phillips, Peter C. B., 1994. "Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 211-248, January.
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Cited by:
  1. Stavros Degiannakis & Christos Floros & Alexandra Livada, 2012. "Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence," Managerial Finance, Emerald Group Publishing, vol. 38(3), pages 436-452, March.
  2. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
  3. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "Semi-nonparametric VaR forecasts for hedge funds during the recent crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 330-343.
  4. Cheng-Few Lee & Jung-Bin Su, 2012. "Alternative statistical distributions for estimating value-at-risk: theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 39(3), pages 309-331, October.

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