Techniques for Verifying the Accuracy of Risk Measurement Models

Kupiec, Paul H.

doi:10.3905/jod.1995.407942

Cited by 1,973 publications

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“…The unconditional coverage (UC) test of Christoffersen (1998); Kupiec (1995) tests whether the actual fraction of VaR-violations is equal to the predicted proportion of 1 − α.…”

Section: Discussionmentioning

confidence: 99%

“…We evaluate the quality of each forecasting method itself, and compare them with each other. To establish the quality we use two tests, being an unconditional coverage test as in Christoffersen (1998); Kupiec (1995) and the dynamic quantile test in the style of Engle and Manganelli (2004). 9 We rank the methods based on a loss function, and test whether loss differentials are significant in the style of Diebold and Mariano (1995); Giacomini and White (2006).…”

Section: Discussionmentioning

confidence: 99%

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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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“…The unconditional coverage (UC) test of Christoffersen (1998); Kupiec (1995) tests whether the actual fraction of VaR-violations is equal to the predicted proportion of 1 − α.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial Econometrics

View full text Add to dashboard Cite

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“…Considering the conditional and unconditional coverage tests (CC, UC), the under-rejection for the Laplace is significant in several cases, whereas the over-rejection for the Student's t setting is never significant. Kupiec [1995], the independence (ID) and Conditional Coverage (CC) test of Christoffersen [1998], and the Berkowitz [2001] To get an impression about the shape of the time-varying parameters, we plot σ 2 t , t , and ν t for the skewed Student's t model in Figure 2. We clearly see the increased volatility around the time of the financial crisis, as well as the higher volatility level during the European sovereign debt crisis (2010)(2011)(2012)(2013).…”

Section: Full Results For the Euro-dollar Ratementioning

confidence: 99%

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Lucas

Zhang

2014

SSRN Journal

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“…Agora, a rejeição ocorre apenas para 5% na posição comprada como pode ser constatado na tabela 2. Kupiec (1995) para a proporção de falhas com 5% de significância. Os p-valores iguais ou maiores que 0,05 indicam que a hipótese nula, de que a verdadeira proporção de falhas do modeloé igual ao nível pré-especificado para o VaR, não pode ser rejeitada ao nível de significância de 5% do teste.…”

Section: Aferição Do Var Intradiáriounclassified

O uso de dados de alta freqüência na estimação da volatilidade e do valor em risco para o IBOVESPA

Moreira¹,

Lemgruber²

2004

Rev. Bras. Econ.

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Este artigo investiga o uso de dados de alta freqüência na estimação das volatilidades diária e intradiária do IBOVESPA e no cálculo do valor em risco (VaR). Os modelos GARCH e EGARCH são usados em conjunto com métodos determinísticos de filtragem de sazonalidade para a previsão da volatilidade e do VaR intradiários. Uma comparação com o método não-paramétrico baseado no quantil empíricoé efetuada. No cálculo do VaR diário, dois métodos simples de previsão buscam captar a informação de volatilidade contida nos dados de alta freqüência. O primeiro utiliza o desvio padrão amostral com janela móvel e o segundo faz uso da técnica de alisamento exponencial. Alguns métodos tradicionais aplicados a dados diários são usados para comparação. No cálculo do VaR diário, os dois métodos baseados em dados intradiários apresentaram bom desempenho. No cálculo do VaR intradiário, os resultados mostram que a filtragem do padrão sazonalé indispensávelà obtenção de medidasúteis de volatilidades com o uso dos modelos GARCH e EGARCH. This paper investigates the use of high frequency data in the estimation of daily and intraday volatility, in order to compute value at risk (VaR) forecasts for the IBOVESPA. GARCH models and deterministic methods for the filtering of seasonal patterns are used in the computation of intraday volatility and VaR forecasts. A comparison with a non-parametric method based on the empirical quantile is done. For daily VaR two simple methods seek to extract the volatility information conveyed by the high frequency data. The first method is based on the sample standard deviation with a moving window, while the second is based on exponencially weighted moving average. Some traditional methods applied to daily data are used for benchmarking. Both methods tested

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Techniques for Verifying the Accuracy of Risk Measurement Models

Cited by 1,973 publications

References 0 publications

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

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

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

O uso de dados de alta freqüência na estimação da volatilidade e do valor em risco para o IBOVESPA

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