Modeling the distribution of extreme returns in the Chinese stock market

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6 Citations (Scopus)

Abstract

It is well known that extreme share returns on stock markets can have important implications for financial risk management. In this paper, we are concerned with the distribution of the extreme daily returns of the Shanghai Stock Exchange (SSE) Composite Index. Three well-known distributions in extreme value theory, i.e., Generalized Extreme Value (GEV), Generalized Logistic (GL) and Generalized Pareto distributions, are employed to model the SSE Composite index returns based on the data from 1991 to 2013. The parameters for each distribution are estimated by using the Power Weighted Method (PWM). Our results indicate that the GL distribution is a better fit for the minima series and that the GEV distribution is a better fit for the maxima series of the returns for the Chinese stock market. This is in contrast to the findings for other markets, such as the US and Singapore markets. Our results are robust regardless of the introduction of stock movement restriction and the global financial crisis. Further, the implications of our findings for risk management are discussed.

Original languageEnglish
Pages (from-to)263-276
Number of pages14
JournalJournal of International Financial Markets, Institutions and Money
Volume34
DOIs
Publication statusPublished - 1 Jan 2015

Fingerprint

Chinese stock market
Modeling
Extreme returns
Composite index
Shanghai stock exchange
Generalized extreme value distribution
Generalized Pareto distribution
Financial risk management
Singapore
Logistics/distribution
Global financial crisis
Extreme value theory
Logistics
Extreme values
Risk management
Stock market

Keywords

  • Chinese stock market
  • Extreme returns
  • Extreme value theory
  • Risk management

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance

Cite this

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title = "Modeling the distribution of extreme returns in the Chinese stock market",
abstract = "It is well known that extreme share returns on stock markets can have important implications for financial risk management. In this paper, we are concerned with the distribution of the extreme daily returns of the Shanghai Stock Exchange (SSE) Composite Index. Three well-known distributions in extreme value theory, i.e., Generalized Extreme Value (GEV), Generalized Logistic (GL) and Generalized Pareto distributions, are employed to model the SSE Composite index returns based on the data from 1991 to 2013. The parameters for each distribution are estimated by using the Power Weighted Method (PWM). Our results indicate that the GL distribution is a better fit for the minima series and that the GEV distribution is a better fit for the maxima series of the returns for the Chinese stock market. This is in contrast to the findings for other markets, such as the US and Singapore markets. Our results are robust regardless of the introduction of stock movement restriction and the global financial crisis. Further, the implications of our findings for risk management are discussed.",
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