Bayesian tail‐risk forecasting using realized GARCH

DOI10.1002/asmb.2237zbMath1420.91525OpenAlexW2099911124MaRDI QIDQ4620201

Christian Contino, Richard H. Gerlach

Publication date: 8 February 2019

Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asmb.2237

zbMATH Keywords

GARCHhigh-frequency datarisk managementexpected shortfallrealized volatilityvalue at riskCVaR

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70)

Related Items (4)

A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures ⋮ Stochastic variational inference for GARCH models ⋮ Forecasting price of financial market crash via a new nonlinear potential GARCH model ⋮ Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

This page was built for publication: Bayesian tail‐risk forecasting using realized GARCH