Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns

Guermat, Cherif; Harris, Richard

doi:10.1016/s0169-2070(01)00122-4

Cited by 59 publications

(53 citation statements)

References 10 publications

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“…A similar observation was made in several earlier studies (see Guermat andHarris, 2002 andBillio andPelizzon, 2000 among others). Even at a 99% confidence level, they did not show any major improvement, as the average realized exception rates were significantly lower than the expected ones.…”

Section: Empirical Analysissupporting

confidence: 90%

“…Angelidis and Degiannakis (2005) opined that "a risk manager must employ different volatility techniques in order to forecast accurately VaR for long and short trading positions", whereas Angelidis et al (2004) argued that "the Arch structure that produces the most accurate VaR forecasts is different for every portfolio". Furthermore, Guermat and Harris (2002) applied an exponentially weighted likelihood model in three equity portfolios (US, UK, and Japan) and proved its superiority to the GARCH model under the normal and the Student-t distributions in terms of two backtesting measures (unconditional and conditional coverage).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Backtesting VaR models:a two-stage procedure

Angelidis¹,

Degiannakis²

2007

JRMV

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Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets (US stock, gold and dollar-pound exchange rate markets), long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models.

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Section: Empirical Analysissupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Backtesting VaR models:a two-stage procedure

Angelidis¹,

Degiannakis²

2007

JRMV

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“…The empirical evidence of this distribution performance in estimating VaR is ambiguous. Some papers show that the ST distribution performs better than the normal distribution (see Abad and Benito (2013), Orhan and Köksal (2012) and Polanski and Stoja (2010)) while other papers report that the ST distribution overestimates the proportion of exceptions (see Angelidis et al (2007) and Guermat and Harris (2002)). …”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis

Romero

Muela²,

Granero³

et al. 2016

JOR

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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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Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns

Cited by 59 publications

References 10 publications

Backtesting VaR models:a two-stage procedure

Backtesting VaR models:a two-stage procedure

Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis

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

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