An analysis of the distribution of extremes in indices of share returns in the US, UK and Japan from 1963 to 2000

Gettinby, G.; Sinclair, C. D.; Power, David; Brown, Richard A.

doi:10.1002/ijfe.280

Cited by 23 publications

(24 citation statements)

References 27 publications

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“…It can also be noticed that the sign of the shape values for the GEV distribution changes over time indicating that there is no unique distribution within the GEV family of distributions that describes the empirical data well. This is in disagreement with Gettinby et al (2004) and Longin (1996), who detected no sign changes when they fitted the GEV distribution to the extreme returns of the UK and US stock markets, respectively.…”

Section: Parameter Estimates and Goodness Of Fit Testcontrasting

confidence: 89%

“…A notable exception is Gettinby et al (2004) who investigated the distribution of extreme share returns in the UK from 1975 to 2000 and found that the Generalised Logistic (GL) distribution describes better than the GEV both the minima and maxima data. Another exception is Da Silva and Mendes (2003) who used Probability Weighted Moments (PWM) to estimate the parameters of the limiting distribution of extremes in 10 Asian stock markets.…”

Section: Introductionmentioning

confidence: 99%

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Value-at-risk and extreme value distributions for financial returns

Tolikas¹

2008

JOR

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The ability of the Generalised Extreme Value (GEV) and Generalised Logistic (GL) distributions to fit extreme financial returns in the stock, commodities and bond markets is assessed. The empirical results indicate that the too much celebrated GEV is not the most appropriate model for the data since the fatter tailed GL is found to provide better descriptions of the extreme returns. Extreme Value Theory (EVT) based VaR estimates are then derived and compared to those generated by traditional methods. The results show that when the focus is on the really ruinous events which are located deep into the tails of the returns distribution, the EVT methods used in this study can be particularly useful since they produce VaR estimates that outperform those derived by the traditional methods at high confidence levels. However, these estimates were found to be considerably higher than those derived by traditional VaR models; consequently leading to higher capital reserves for financial institutions.

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Section: Parameter Estimates and Goodness Of Fit Testcontrasting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Value-at-risk and extreme value distributions for financial returns

Tolikas¹

2008

JOR

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“…This is in accordance with the opinion Gettinby et al (2006) who found that the distribution of GLD is an appropriate distribution compared to the distribution GEV and GPD. Very different from the maximum likelihood method that for each test GOF, amaun extreme exchange rate in all countries to follow the GEV distribution.…”

Section: Resultssupporting

confidence: 92%

Annual Maximum Exchange Rate in Southeast Asia Based on Methods of L-moment and Maximum Likelihood

Akbarizan¹,

Marizal²,

Yendra³

et al. 2016

RJASET

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Abstract:The main objective of this study is to determine the best fitting distribution to describe the annual series of maximum daily exchange rate from 1993 to 2013 for 4 countries in Southeast Asia based on L-moment and Maximum Likelihood (ML). Four three-parameter extreme-value distributions which are considered are Generalized Extreme Value (GEV), Generalized Logistic Distribution (GLD), Generalized Pareto Distribution (GPD) and Pearson III (P3). The estimation of parameters of these distributions is determined using the L-moment and maximum likelihood. The adequacy of the distributions based on parameter estimates computed using the two methods are evaluated using goodness-of-fit tests. When the goodness-of-fit results for these distributions are compared, it is found that, on the average, the performance of L-moment is better than the performance of maximum likelihood. Although the best fitting distribution may very according to the method of estimation and country considered, in most cases, data for the majority of the several countries are found to follow the generalized logistic distribution.

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“…A general discussion of the application of EVT to risk management is proposed by [12,15,16]. However, if the oldest studies made use of raw returns 1 Some of the main studies of univariate EVT in finance include [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

When the U.S. Stock Market Becomes Extreme?

Aboura

2014

Risks

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Over the last three decades, the world economy has been facing stock market crashes, currency crisis, the dot-com and real estate bubble burst, credit crunch and banking panics. As a response, extreme value theory (EVT) provides a set of ready-made approaches to risk management analysis. However, EVT is usually applied to standardized returns to offer more reliable results, but remains difficult to interpret in the real world. This paper proposes a quantile regression to transform standardized returns into theoretical raw returns making them economically interpretable. An empirical test is carried out on the S&P500 stock index from 1950 to 2013. The main results indicate that the U.S stock market becomes extreme from a price variation of ±1.5% and the largest one-day decline of the [2007][2008] period is likely, on average, to be exceeded one every 27 years.

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An analysis of the distribution of extremes in indices of share returns in the US, UK and Japan from 1963 to 2000

Cited by 23 publications

References 27 publications

Value-at-risk and extreme value distributions for financial returns

Value-at-risk and extreme value distributions for financial returns

Annual Maximum Exchange Rate in Southeast Asia Based on Methods of L-moment and Maximum Likelihood

When the U.S. Stock Market Becomes Extreme?

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