Two-step methods in VaR prediction and the importance of fat tails

Ergen, Ibrahim

doi:10.1080/14697688.2014.942230

Cited by 20 publications

(10 citation statements)

References 45 publications

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“…To obtain ES estimates based on this framework, McNeil and Frey (2000), Bhattacharyya et al (2008), and Ergen (2015) propose a parsimonious but effective two‐step procedure. We follow its basic idea.…”

Section: Methodsmentioning

confidence: 99%

“…In the nonparametric case, we apply a kernel density method (see Nadaraya, 1964) which does not impose a specific theoretical distribution but instead derives the distribution of losses by smoothing the empirical distribution with an appropriate kernel function and bandwidth parameter (see Chen, 2008; Scaillet, 2004). For all six methods, we estimate the full set of parameters (time‐series model and distribution model) by the two‐step procedure of McNeil and Frey (2000) and Bhattacharyya et al (2008), which, in parametric approaches, can reduce misspecification error (in the parameters of the time‐series model) related to an incorrect distributional choice (see Ergen, 2015) and, in nonparametric ones, is the de facto standard (see Gao & Song, 2008). To characterize past investment risk in commodity futures markets and analyze its behavior in turbulent phases, like, recessions and stock market downturns, we apply the estimators to our entire range of return data.…”

Section: Introductionmentioning

confidence: 99%

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Time‐varying dynamics of expected shortfall in commodity futures markets

Mehlitz

Auer

2021

Journal of Futures Markets

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Motivated by the growing interest of investors in commodities and by advances in risk measurement, we present a full‐scale analysis of expected shortfall (ES) in commodity futures markets. Besides illustrating the dynamics of historic ES, we evaluate whether popular estimators are suitable for forecasting future ES. By implementing a new backtest, we find that the performance of estimators hinges on market stability. Estimators tend to fail when markets are in turmoil and accurate forecasts are urgently needed. Even though a kernel method performs best on average, our results advise against the use of established estimators for risk (and margin) prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time‐varying dynamics of expected shortfall in commodity futures markets

Mehlitz

Auer

2021

Journal of Futures Markets

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“…Ibragimov (2009) shows that VaR-driven portfolio diversification is robust to heavy-tailed risk in the return distributions. Mancini and Trojani (2011) employ a semi-parametric VaR model with a bootstrap technique to deal with extreme returns, and Ergen (2014) proposes a GARCH model in VaR prediction with skewed t-distributed errors. Rossignolo et al (2012) show that models with heavy-tailed distribution are the most accurate technique to capture market risks.…”

Section: Introductionmentioning

confidence: 99%

Value at risk estimation by threshold stochastic volatility model

Huang

2015

Applied Economics

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This article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.

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“…It is also more suited for modelling the stylized facts such as "fat tails" in the empirical return distribution. Empirical evidence suggests that the EVT models provide better VaR forecasts than the stand-alone GARCH-type models (Bali, 2003;Dimitrakopoulos et al, 2010;Ergen, 2015;Gençay et al, 2003;Gençay and Selçuk, 2004;Ho et al, 2000;Karmakar and Paul, 2016;Karmakar and Shukla, 2015;Yi et al, 2014;Paul and Sharma, 2017). SEF 35,4 The EVT assumes that the observations are independent and identically distributed (iid).…”

Section: Introductionmentioning

confidence: 99%

Quantile forecasts using the Realized GARCH-EVT approach

Paul

Sharma

2018

SEF

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Purpose-This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models. Design/methodology/approach-One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model. Findings-In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model. Originality/value-It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors' knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

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Two-step methods in VaR prediction and the importance of fat tails

Cited by 20 publications

References 45 publications

Time‐varying dynamics of expected shortfall in commodity futures markets

Time‐varying dynamics of expected shortfall in commodity futures markets

Value at risk estimation by threshold stochastic volatility model

Quantile forecasts using the Realized GARCH-EVT approach

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