Evaluating Value-at-Risk Models with Desk-Level Data
By Jeremy Berkowitz, Peter F. Christoffersen, Denis Pelletier. Management Sciences, 2011
369 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/29cc8ba2c1c6f5aed3169db2fd41906450304e3a
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In Economics and Econometrics
- M. Nieto
- Jakub Nowotarski
- Esther Ruiz
- Rafal Weron
- Jozef Baruník
- Richard Gerlach
- Maximo Torero
- Carlos Martins-Filho
In Finance
- M. Nieto
- Esther Ruiz
- Jozef Baruník
- Rafal Weron
- Jakub Nowotarski
- Richard Gerlach
- Maximo Torero
- Carlos Martins-Filho
Citations in the corpus
- Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets (2011, background): “The DQ test is well known to be more powerful than the CC test; see Berkowitz, Christoffersen, and Pelletier (2010).”
- Frontiers in VaR forecasting and backtesting (2016, methodology): “They propose the following random coeffi cient model that can be used to construct a Wald test for the null hypothesis that a given VaR model is correctly specified Rt = β0(Ut) + β1(Ut)V aRt, (16) where Ut are iid U(0,1) variables and βi(Ut), i = 0, 1 are assumed to be comonotonic in Ut.”
- Systemic risk spillovers in the European banking and sovereign network (2016, neutral): “This is evaluated according to a backtest criterion (see Berkowitz, Christoffersen, and Pelletier (2011)).”
- Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, methodology): “As in the case of returns, we now assess the absolute in-sample performance of the conditional quantile models using the Berkowitz et al. (2011) test.”
- Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall (2016, methodology): “s exponential distribution. The null hypothesis of exponential spacings (constant hazard model) is tested against a Weibull alternative (in which the hazard function may be increasing or decreasing). Berkowitz et al. (2011) provide a comprehensive overview of tests of conditional coverage. They advo”
- TENET: Tail-Event driven NETwork risk (2016, methodology): “We apply the CaViaR test proposed by Berkowitz et al. (2011). While the p-values of ĈoVaR TENET in overall period is 0.63, ĈoVaR L is only 0.37. Also in crisis period (from 15 September 2008 to 26 February 2010) ĈoVaR TENET performs better than ĈoVaR L , see Table 9 for more details. Further, we exa”
- A review of backtesting for value at risk (2018, methodology): “Then, the null hypothesis that the risk model is correctly specified corresponds to p = α (Christoffersen and Pelletier, 2004).”