Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications

Poon, Ser‐Huang; Rockinger, Michael; Tawn, Jonathan A.

doi:10.1093/rfs/hhg058

Cited by 523 publications

(409 citation statements)

References 82 publications

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“…the center of a distribution. Studies that only compare the Gaussian and Gumbel copula like Longin and Solnik (2001) and Poon et al (2004) may therefore be too limited. We conclude that correlations are useful to characterize dependence in the center, and that the degrees of freedom parameter of the Student's t copula is a good addition to capture stronger dependence in the tails.…”

Section: Discussionmentioning

confidence: 99%

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Selecting copulas for risk management

Kole

Koedijk

Verbeek

2007

Journal of Banking & Finance

255

120

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Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favor of the Student's t copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the Student's t copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant.

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Section: Discussionmentioning

confidence: 99%

“…Being an extreme value copula, it extends the successful univariate extreme value theory techniques in risk management, as shown by Longin (1996) and Jansen et al (2000). In their study of dependence of extreme returns Longin and Solnik (2001) and Poon et al (2004) also use the Gumbel copula.…”

Section: Applying Copulas In Risk Managementmentioning

confidence: 99%

Selecting copulas for risk management

Kole

Koedijk

Verbeek

2007

Journal of Banking & Finance

255

120

View full text Add to dashboard Cite

show abstract

“…These are relatively tight if the variables are asymptotically dependent, but for asymptotically independent variables the bounds differ, with the lower bound being the more likely to provide a better approximation in many cases. In a companion paper, Poon, Rockinger and Tawn (2003), we adopt parametric models for the joint tail distributions. Here we adopt a nonparametric approach.…”

Section: Implications Of Asymptotic Independence For Portfolio Risk Amentioning

confidence: 99%

“…In this paper and a companion paper (Poon, Rockinger and Tawn (2003)), we use techniques developed by Ledford and Tawn (1997) and Coles, Heffernan and Tawn (1999) to distinguish between asymptotically dependent and asymptotically independent variables, and to quantify the degree of dependence for the appropriate dependence class. Here we document the effect of heteroskedasticity on the tail index and tail dependence among international stock market returns, and we examine the effect of volatility filtering on tail estimates.…”

Section: Introductionmentioning

confidence: 99%

Modelling Extreme-Value Dependence in International Stock Markets

2002

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In the finance literature, cross-sectional dependence in extreme returns of risky assets is often modelled implicitly assuming an asymptotically dependent structure. If the true dependence structure is asymptotically independent then current modelling approaches will lead to an over-estimation of the risk of simultaneous extreme events. We use two simple nonparametric measures to identify and quantify the tail dependence among stock returns in five international stock markets. We show that there is strong evidence in favour of asymptotically independent models for the tail structure of stock market returns, and that most of the extremal dependence is due to heteroskedasticity in stock returns processes. Using a range of volatility filters, we find that tail index and tail dependence can be partially captured by models for heteroskedasticity. We find there is no clear reason to prefer one volatility filter over another.

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“…While techniques which assume linear correlations are easy to use, they fail to accurately capture the return comovements if the returns are not normally distributed. Poon et al (2004) confirm that the linear measures of correlations fail to distinguish extreme positive and negative returns. Further, the asymmetric correlation between the stock returns during periods of economic expansion and contraction cannot be explained by the conventional measure of comovements (Beine et al 2008).…”

Section: Sources Of Time Varying Return Comovements During Different…mentioning

confidence: 55%

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

Poshakwale

Mandal

2016

Rev Quant Finan Acc

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We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management.Keywords Emerging Indian equity market Á Asset return comovements Á Economic and non-economic sources Á Copula models Á Markov switching stochastic volatility model JEL Classification G15 Á F36 Á E44

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Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications

Cited by 523 publications

References 82 publications

Selecting copulas for risk management

Selecting copulas for risk management

Modelling Extreme-Value Dependence in International Stock Markets

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

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