Testing and comparing Value-at-Risk measures

Christoffersen, Peter; Hahn, Jinyong; Inoue, Atsushi

doi:10.1016/s0927-5398(01)00025-1

Cited by 174 publications

(85 citation statements)

References 30 publications

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“…Finally, we perform the unconditional and conditional coverage value at risk exceedances tests (Christoffersen (1998);Christoffersen et al (2001)). The p-values of the unconditional test for the best fitting models and various exceedance probabilities are given in Tables 2-8.…”

Section: Resultsmentioning

confidence: 99%

GARCH Modelling of Cryptocurrencies

Chu

Chan

Nadarajah

et al. 2017

JRFM

272

165

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With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.

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Section: Resultsmentioning

confidence: 99%

GARCH Modelling of Cryptocurrencies

Chu

Chan

Nadarajah

et al. 2017

JRFM

272

165

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“…One is forced to rely on nonparametric tests which have notoriously poor 13 See for example Diebold, Gunther and Tay (1998). small sample properties.…”

Section: Backtesting Tail Density Forecastsmentioning

confidence: 99%

Backtesting Value-at-Risk: A Duration-Based Approach

2003

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“…In a number of papers, VaR was evaluated for developed market economies, using similar methodology to ours (for instance, Degiannakis, 2004;Linsmeier & Pearson, 2000;Duffie & Pan, 1997;Wong, Cheng, & Wong, 2002, Guermat & Harris 2002Alexander & Leigh, 1997;Christoffersen, Hahn, & Inoue, 2001;Su & Knowles, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance of Value at Risk models in the midst of the global financial crisis in selected CEE emerging capital markets

Miletić¹,

Miletić²

2015

Economic Research-Ekonomska Istraživanja

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The aim of this paper is to investigate the performance of Value at Risk (VaR) models in selected Central and Eastern European (CEE) emerging capital markets. Daily returns of Croatian (CROBEX), Czech (PX50), Hungarian (BUX) and Romanian (BET) stock exchange indices are analysed for the period January, 2000 -February, 2012, while daily returns of the Serbian (BELEX15) index is examined for the period September, 2005 -February, 2012. In recent years there has been much research conducted into VaR in developed markets, while papers dealing with VaR calculation in CEE are rare. Furthermore, VaR models created and suited for liquid and welldeveloped markets that assume normal distribution are less reliable for capital markets in emerging economies, such as Central and Eastern European Union member and candidate states. Since capital markets in European emerging economies are highly volatile, less liquid and strongly dependent on the unexpected external shocks, market risk estimation based on normality assumption in CEE countries is more problematic. This motivates us to implement GARCH-type methods that involve time varying volatility and heavy tails of the empirical distribution of returns. We test the hypothesis that using the assumption of heavy tailed distribution it is possible to forecast market risk more precisely, especially in times of crisis, than under the assumption of normal distribution or using historical simulations method. Our backtesting results for the last 500 observations are based on the Kupiec POF and Christoffersen independence test. They show that GARCH-type models with t error distribution in most analysed cases give better VaR estimation than GARCH type models with normal errors in the case of a 99% confidence level, while in the case of a 95% confidence level it is the opposite. The results of backtesting analysis for the crisis period (after the collapse of Lehman Brothers) show that GARCH-type models with t-distribution of residuals provide better VaR estimates compared with GARCH-type models with normal distribution, historical simulations and RiskMetrics methods. The RiskMetrics method in the most cases underestimates market risk.

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Testing and comparing Value-at-Risk measures

Cited by 174 publications

References 30 publications

GARCH Modelling of Cryptocurrencies

GARCH Modelling of Cryptocurrencies

Backtesting Value-at-Risk: A Duration-Based Approach

Performance of Value at Risk models in the midst of the global financial crisis in selected CEE emerging capital markets

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