Accurate value-at-risk forecasting based on the normal-GARCH model

Hartz, Christoph; Mittnik, Stefan; Paolella, Marc S.

doi:10.1016/j.csda.2006.09.017

Cited by 80 publications

(60 citation statements)

References 33 publications

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“…Our findings support those found in the study by Hartz et al (2006). They obtain similar results with the other data, finding that the bias-correction method based on the n-GARCH model performs better than the usual n-GARCH model.…”

Section: Discussionsupporting

confidence: 92%

“…They obtain similar results with the other data, finding that the bias-correction method based on the n-GARCH model performs better than the usual n-GARCH model. This is also confirmed by the csl scoring rule for both GARCH models which has not been considered by Hartz et al (2006). Our empirical study shows that the bias-correction method based on the n-EGARCH model instead of n-GARCH leads to improvements in correctly forecasting oneday-ahead VaR for long and short positions of almost all real return series investigated based on the three performance tests of Christoffersen (1998), while the independence of the VaR violations is unaffected by this method.…”

Section: Discussionsupporting

confidence: 66%

“…In this paper, we extend the study by Hartz et al (2006) to take account of asymmetry in conditional variance and correct the VaR for both long and short positions.…”

Section: Discussionmentioning

confidence: 92%

“…Because, assuming the true data generating process is constant over time, the bias caused by the use of the inappropriate but simple n-GARCH model will display certain regularities and thus can be corrected based on a set of past VaR bootstrap distributions (Hartz et al, 2006). This is the assumption used in the bias-correction method described in Section 2.2.…”

Section: Var Forecast Distributionmentioning

confidence: 99%

“…Hartz‚ Mittnik and Paolella (2006) have developed a resampling method based on the bootstrap and bias-correction for improving the VaR forecasting ability of the n-GARCH (normal GARCH) model. Their proposed method has improved the VaR forecasts of the n-GARCH model.…”

Section: Introductionmentioning

confidence: 99%

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Improving Value-at-Risk Estimation from the Normal EGARCH Model

Mahsa

Sajjad

2017

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Returns in financial assets display consistent excess kurtosis and skewness, implying the presence of large fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method to accurate for the tendency of the model tomiscalculate the VaR. Empirical results indicate that the bias-correction method can improve the n-GARCH and n-EGARCH VaR forecasts so much that the acquired VaR predictions are different from the proposed probability. Additionally, allowing asymmetry in the conditional variance using the EGARCH model with normal distribution instead of GARCH improves the performance of the bias-correction method in forecasting the VaR for almost all considered indices. Moreover, the bias-corrected n-EGARCH model performs better than the simple t-EGARCH model. Thus, it seems that this model can take account of both the asymmetry in the conditional variance and leptokurtosis in returns distribution. However, we find that the superiority of the bias-corrected n-EGARCH model over the t-EGARCH model is not completely confirmed for short positions based on the censored likelihood scoring rule. IntroductionSince the Basle Committee (1995; began allowing banks to implement internal VaR models for calculating capital requirements, various methods have been proposed to achieve this purpose. However, the theoretical and computational complexity of these methods has also been raised.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 66%

“…In this paper, we extend the study by Hartz et al (2006) to take account of asymmetry in conditional variance and correct the VaR for both long and short positions.…”

Section: Discussionmentioning

confidence: 92%

Section: Var Forecast Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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