Ranking the predictive performances of value-at-risk estimation methods

Name: Ranking the predictive performances of value-at-risk estimation methods
Author: Şener, Emrah, Baronyan, Sayat, Mengütürk, L. A.

İsim	Ranking the predictive performances of value-at-risk estimation methods
Yazar	Şener, Emrah, Baronyan, Sayat, Mengütürk, L. A.
Basım Tarihi:	2012
Basım Yeri	- Elsevier
Konu	Value at risk, Predictive ability test, EGARCH, CAViaR asymmetric
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0169-2070
Kayıt Numarası	aafe38d3-f739-4a1b-a64d-2cff3fe12f0f
Lokasyon	Business Administration
Tarih	2012
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	We introduce a ranking model and a complementary predictive ability test statistic to investigate the forecasting performances of different Value at Risk (VaR) methods, without specifying a fixed benchmark method. The period including the recent credit crisis offers a unique laboratory for the analysis of the relative successes of different VaR methods when used in both emerging and developed markets. The proposed ranking model aims to form a unified framework which penalizes not only the magnitudes of errors between realized and predicted losses, but also the autocorrelation between the errors. The model also penalizes excessive capital allocations. In this respect, the ranking model seeks for VaR methods which can capture the delicate balance between the minimum governmental regulations for financial sustainability, and cost-efficient risk management for economic vitality. As a complimentary statistical tool for the ranking model, we introduce a predictive ability test which does not require the selection of a benchmark method. This statistic, which compares all methods simultaneously, is an alternative to existing predictive ability tests, which compare forecasting methods two at a time. We test and rank twelve different popular VaR methods on the equity indices of eleven emerging and seven developed markets. According to the ranking model and the predictive ability test, our empirical findings suggest that asymmetric methods, such as CAViaR Asymmetric and EGARCH, generate the best performing VaR forecasts. This indicates that the performance of VaR methods does not depend entirely on whether they are parametric, non-parametric, semi-parametric or hybrid; but rather on whether they can model the asymmetry of the underlying data effectively or not.
DOI	10.1016/j.ijforecast.2011.10.002
Cilt	28

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Ranking the predictive performances of value-at-risk estimation methods

Yazar Şener, Emrah, Baronyan, Sayat, Mengütürk, L. A.

Basım Tarihi 2012

Basım Yeri - Elsevier

Konu Value at risk, Predictive ability test, EGARCH, CAViaR asymmetric

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0169-2070

Kayıt Numarası aafe38d3-f739-4a1b-a64d-2cff3fe12f0f

Lokasyon Business Administration

Tarih 2012

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin We introduce a ranking model and a complementary predictive ability test statistic to investigate the forecasting performances of different Value at Risk (VaR) methods, without specifying a fixed benchmark method. The period including the recent credit crisis offers a unique laboratory for the analysis of the relative successes of different VaR methods when used in both emerging and developed markets. The proposed ranking model aims to form a unified framework which penalizes not only the magnitudes of errors between realized and predicted losses, but also the autocorrelation between the errors. The model also penalizes excessive capital allocations. In this respect, the ranking model seeks for VaR methods which can capture the delicate balance between the minimum governmental regulations for financial sustainability, and cost-efficient risk management for economic vitality. As a complimentary statistical tool for the ranking model, we introduce a predictive ability test which does not require the selection of a benchmark method. This statistic, which compares all methods simultaneously, is an alternative to existing predictive ability tests, which compare forecasting methods two at a time. We test and rank twelve different popular VaR methods on the equity indices of eleven emerging and seven developed markets. According to the ranking model and the predictive ability test, our empirical findings suggest that asymmetric methods, such as CAViaR Asymmetric and EGARCH, generate the best performing VaR forecasts. This indicates that the performance of VaR methods does not depend entirely on whether they are parametric, non-parametric, semi-parametric or hybrid; but rather on whether they can model the asymmetry of the underlying data effectively or not.

DOI 10.1016/j.ijforecast.2011.10.002

Cilt 28