Conditional value-at-risk: Aspects of modeling and estimation

By Victor Chernozhukov, Len Umantsev. 2001

247 citations recorded by the source corpus.

Abstract. This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeling is cast in terms of the quantile regression function – the inverse of the conditional distribution function. A basic specification analysis relates its functional forms to the benchmark models of returns and asset pricing. We stress important aspects of measuring the extremal and intermediate conditional risk. An empirical application characterizes the key economic determinants of various levels of conditional risk.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5ae694d2a0e05d44eef3fcdf5e33d928d2f6f3f7

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Citations in the corpus

Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (2009, unknown): “This empirical example closely matches the Monte-Carlo experiment in the previous section 16 See Christoffersen, Hahn, and Inoue (1999), Diebold, Schuermann, and Stroughair (2000), Cher- nozhukov and Umantsev (2001), and Engle and Manganelli (2004). with heavy-tailed t(3) disturbances.”
NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY (2016, background): “In insurance and empirical finance, where regulators and portfolio managers are interested in high levels of risk, with a in the vicinity of 1 (Chernozhukov and Umantsev (2001); Tsay (2010)), an important concern for inference is that conventional asymptotic theory does not apply sufficiently far…”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, methodology): “Chernozhukov and Umantsev, 2001). Koenker and Basset (1978) generalize the common linear regression framework by shifting the focus from the conditional mean to conditional quantiles.”
Extremal quantile regression: An overview (2016, methodology): “We revisit the problem of forecasting the conditional value-at-risk of a ﬁnancial institution posed by Chernozhukov and Umantsev (2001) with more recent methodology.”
Granger Causality in Risk and Detection of Extreme Risk Spillover Between Financial Markets (2009, methodology): “, Chernozhukov and Umantsev (2001), Engle and Manganelli (2004) and Jorion (2000)). Examples are historical simulation methods, Hansen’s (1994) autoregressive conditional densitymodel, Morgan’s (1996) RiskMetrics, and Engle and Manganelli’s (2004) conditional autoregressive VaR (CAViaR) models.”
Value at Risk Models in Finance (2001, unknown): “…have been proposed to estimate Value at Risk, such as applications of Extreme Value Theory (see, for example, Danielsson and deVries (1998) or Gourieroux and Jasak (1998)) and applications of regression quantile technique such as in Chernozhukov and Umantsev (2000) and Engle and Manganelli (1999).”
Quantile Regression Estimator for GARCH Models (2013, methodology): “…by using different estimated quantiles and investigated the factors affecting national economic growth in different countries; Taylor (1999), Chernozhukov & Umantsev (2001), and Engle & Manganelli (2004), who used quantile regression to study the value at risk (VaR) of asset returns; Peng &…”
Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction (2013, unknown): “The limiting properties of the latter are √ n(λ̃n − λ∗) d → N(0,ΣGMM ), (14)”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, methodology): “The use of quantile regression in financial econometrics is not new (Koenker & Zhao, 1996, Chernozhukov & Umantsev, 2001, Engle & Manganelli, 2004, Cenesizoglu & Timmerman, 2008), but to the best of our knowledge, it has not yet been applied in combination with realized volatility and related…”
A joint quantile and expected shortfall regression framework (2017, unknown): “…the ES (jointly with the VaR) by generalizing existing applications of quantile regression on VaR, such as e.g. in Koenker and Xiao (2006), Engle and Manganelli (2004), Chernozhukov and Umantsev (2001), Žikeš and Baruník (2016), Halbleib and Pohlmeier (2012), Komunjer (2013) and Xiao et al. (2015).”