Modeling Autoregressive Conditional Skewness and Kurtosis with Multi-Quantile CAViaR

By Halbert White, Tae-Hwan Kim, Simone Manganelli. Social Science Research Network, 2008

128 citations recorded by the source corpus.

Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis defined in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns. JEL Classification: C13, C32

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6033f757c8e70635bf0c6f7bfad17f5f440242c4

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Citations in the corpus

VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2015, methodology): “We establish consistency and asymptotic normality for ˆ through methods analogous to those of White, Kim, and Manganelli (2008).”
Ex Ante Skewness and Expected Stock Returns (2013, background): “5 In a different application, Xing, Zhang, and Zhao (2010) find a positive relation between a skewness metric taken from option prices and the next month’s returns.”
Forecasting and Stress Testing with Quantile Vector Autoregression (2019, methodology): “The asymptotic distribution of the regression quantile estimator is provided by White et al. (2015), which we report here for convenience.”