Value at Risk Models in Finance

By Robert F. Engle, Simone Manganelli. Social Science Research Network, 2001

323 citations recorded by the source corpus.

The main objective of this paper is to survey and evaluate the performance of the most popular univariate VaR methodologies, paying particular attention to their underlying assumptions and to their logical flaws. In the process, we show that the Historical Simulation method and its variants can be considered as special cases of the CAViaR framework developed by Engle and Manganelli (1999). We also provide two original methodological contributions. The first one introduces the extreme value theory into the CAViaR model. The second one concerns the estimation of the expected shortfall (the expected loss, given that the return exceeded the VaR) using a regression technique. The performance of the models surveyed in the paper is evaluated using a Monte Carlo simulation. We generate data using GARCH processes with different distributions and compare the estimated quantiles to the true ones. The results show that CAViaR models perform best with heavy-tailed DGP. JEL Classification: C22, G22

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4bcebd5f65f856f255a62c28a3024bc8f0e82cb1

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Citations in the corpus

Extreme Value Theory for Finance: A Survey (2012, background): “On the one hand, Manganelli and Engle (2004) point out that the evaluation of the optimal cut-off based on this criterion yields a biased estimator and no theoretical inquiry has been done to quantify the resulting bias when, for instance, estimating VaR (the EVT approach to VaR calculation…”
Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets (2011, methodology): “As in Manganelli and Engle (2004) VaR estimation methods can be classified as parametric, with a full distributional and model form specified; nonparametric, with minimal or no distributional or model assumptions; and semiparametric, where some assumptions are made, either about the error distributi”
VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2015, unknown): “[19] Manganelli, S.”
Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution (2019, methodology): “Manganelli and Engle (2004) adapted the EVT-based method of McNeil and Frey (2000).”
Using extreme value theory to measure value-at-risk for daily electricity spot prices (2006, methodology): “As such, the GARCH-EVT approach might be regarded as semi-parametric (Manganelli & Engle, 2004).”
Forecast combinations for value at risk and expected shortfall (2020, methodology): “This limitation is addressed by Manganelli and Engle (2004), who estimate a CAViaR model for the 7.”
Wind Energy: Forecasting Challenges for Its Operational Management (2013, background): “This was observed by Manganelli and Engle (2004) when focusing on risk quantification approaches in finance, and more particularly on dynamic quantile regression models for very low and high levels.”