Evaluating Interval Forecasts

Christoffersen, Peter

doi:10.2307/2527341

Cited by 2,095 publications

(1,784 citation statements)

References 16 publications

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“…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. We found that the bias-corrected n-EGARCH model is the only model never rejected by any of the three performance tests for all the specified probabilities and real return series investigated.…”

Section: Discussionsupporting

confidence: 66%

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: 66%

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

Mahsa

Sajjad

2017

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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

274

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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“…We use the tests proposed by Christoffersen (1998) and Engle and Manganelli (2004) to check the accuracy of each method in isolation. We check whether a particular method outperforms another by comparing the values of the asymmetric tick-loss function as in Giacomini and Komunjer (2005) based on the test of Diebold and Mariano (1995) in the framework of Giacomini and White (2006), and construct Model Confidence Sets as proposed by Hansen et al (2011) to assess the importance of a particular choice.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial Econometrics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in December 2015 AbstractWe examine the impact of temporal and portfolio aggregation on the quality of Valueat-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregation.

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Evaluating Interval Forecasts

Cited by 2,095 publications

References 16 publications

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

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

GARCH Modelling of Cryptocurrencies

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

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