Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets

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Frontiers in VaR forecasting and backtesting (2016, methodology): “…about which model is more adequate to forecast the VaR depend on the particular quantile of the distribution of returns being forecast; see, for example, Giacomini and Komunjer (2005), Kuester et al. (2006), Gerlach et al. (2011), Gaglionone et al. (2011) and Chen, Gerlach, Lin and Lee (2012).”
Bayesian Tail Risk Interdependence Using Quantile Regression (2015, methodology): “More recently, Gerlach et al. (2011) and Chen et al. (2012a,b), deal with the problem of estimating the conditional dynamic VaR using a Bayesian CAViaR approach.”
A comprehensive review of Value at Risk methodologies (2014, methodology): “HS FHS RM Parametric approaches ETV CF CaViaR MC N-P N T SSD MN HOM Abad and Benito (2013) × × × × × × Gerlach et al. (2011) × × × × Sener et al. (2012) × × × × × × Ergun and Jun (2010) × × × × × Nozari et al. (2010) × × Polanski and Stoja (2010) × × × × × × Brownlees and Gallo (2010) × × × × Yu et…”
Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics (2020, methodology): “The favoured appraoch is based on quantile autoregression [43, 20, 35], like in the application to valueat-risk known as CAViaR [24].”