Backtesting Value-at-Risk: A Duration-Based Approach

Christoffersen, Peter; Pelletier, Denis

doi:10.1093/jjfinec/nbh004

Cited by 279 publications

(110 citation statements)

References 25 publications

(6 reference statements)

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“…In the third experiment, neither the internal model nor the auxiliary model conform with the DGP, which is assumed to be a MS-GARCH model described by equations (18) and (19). We assume that the bank computes its VaR through Historical Simulation and we use a GARCH(1,1) model as the auxiliary model.…”

Section: Experiments 3: Invalid Internal and Auxiliary Modelsmentioning

confidence: 99%

“…When the UC and IND hypotheses are simultaneously valid, VaR forecasts are said to have a correct Conditional Coverage (CC), and the VaR violation process is a martingale di¤erence, with -[I t (B) 0 B F t0 ] = 0. For a test of the CC hypothesis, see Christo¤ersen (1998), Christo¤ersen and Pelletier (2004), Engle and Manganelli (2004), and Berkowitz, Christo¤ersen and Pelletier (2011). 4 Other standard backtesting methods also disregard the magnitude of the losses beyond the VaR …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Risk Map: A new tool for validating risk models

Colletaz

Hurlin

Pérignon

2013

Journal of Banking & Finance

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This paper presents a new method to validate risk models: the Risk Map. This method jointly accounts for the number and the magnitude of extreme losses and graphically summarizes all information about the performance of a risk model. It relies on the concept of a super exception, which is dened as a situation in which the loss exceeds both the standard Value-at-Risk (VaR) and a VaR dened at an extremely low coverage probability. We then formally test whether the sequences of exceptions and super exceptions are rejected by standard model validation tests. We show that the Risk Map can be used to validate market, credit, operational, or systemic risk estimates (VaR, stressed VaR, expected shortfall, and CoVaR) or to assess the performance of the margin system of a clearing house.JEL classication: G21, G28, G32

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Section: Experiments 3: Invalid Internal and Auxiliary Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Risk Map: A new tool for validating risk models

Colletaz

Hurlin

Pérignon

2013

Journal of Banking & Finance

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“…As it is known that a main drawback of both tests is that they have a questionable statistical power when applied to finite samples (see Christoffersen and Pelletier (2004), Berkowitz et al (2008), Ziggel et al (2013) etc). Namely, both tests are developed using asymptotic arguments, which can create difficulties when applied to finite samples.…”

Section: Validation Of the Backtesting Resultsmentioning

confidence: 99%

Quantifying extreme market risk in the selected Western Balkan countries

Radivojević¹,

Ćurčić²,

Marčetić³

2018

Industrija

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“…Following Engle and Manganelli (2004), a constant, the VaR forecasts, and first four lagged values of Hit t (α) are included as covariates. The other two backtesting measures are based on the duration between VaR violations to test the clustering of violations (Christoffersen and Pelletier 2004). As the fifth measure, we use the Weibull test, where the null hypothesis is exponential distribution and the alternative hypothesis is the Weibull distribution, to test the dependence of the duration.…”

Section: Forecastingmentioning

confidence: 99%

“…Finally, to test the conditional dependence of the duration, we consider the exponential autoregressive conditional duration (EACD) of Engle and Russell (1998). The implementation details for the duration based tests are found in Christoffersen and Pelletier (2004) (see also Dufour 2006).…”

Section: Forecastingmentioning

confidence: 99%

Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles

Kobayashi

2015

Comput Stat

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This paper proposes a unified framework to analyse the skewness, tail heaviness, quantiles and expectiles of the return distribution based on a stochastic volatility model using a new parametrisation of the skew exponential power (SEP) distribution. The SEP distribution can express a wide range of distribution shapes through two shape parameters and one skewness parameter. Since the asymmetric Laplace and skew normal distributions are included as special cases, the proposed model is related to quantile regression and expectile regression. The efficient and simple Markov chain Monte Carlo estimation methods are also described. The proposed model is demonstrated using the simulated data and real data on daily return of foreign exchange rate.Keywords Bayesian expectile regression · Bayesian quantile regression · Double two-piece family · Foreign exchange return · Markov chain Monte Carlo

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Backtesting Value-at-Risk: A Duration-Based Approach

Cited by 279 publications

References 25 publications

The Risk Map: A new tool for validating risk models

The Risk Map: A new tool for validating risk models

Quantifying extreme market risk in the selected Western Balkan countries

Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles

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