Forecasting risk via realized GARCH, incorporating the realized range

DOI10.1080/14697688.2015.1079641zbMath1469.62337OpenAlexW2295520480MaRDI QIDQ5001146

Chao Wang, Richard H. Gerlach

Publication date: 16 July 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2123/12235

zbMATH Keywords

volatility estimation for financial asset returns

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (9)

Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility ⋮ Review of statistical approaches for modeling high-frequency trading data ⋮ A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures ⋮ Efficient estimation of financial risk by regressing the quantiles of parametric distributions: an application to CARR models ⋮ How does the choice of Value-at-Risk estimator influence asset allocation decisions? ⋮ A measure of market volatility based on F-transform ⋮ Estimating value-at-risk and expected shortfall using the intraday low and range data ⋮ Extreme downside risk and market turbulence ⋮ Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

Uses Software

• CAViaR

Cites Work

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• Generalized autoregressive conditional heteroscedasticity
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• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Autoregressive Conditional Density Estimation
• Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models
• Evaluating Value-at-Risk Models via Quantile Regression
• Modeling and Forecasting Realized Volatility
• The Asymptotic Distribution of the Range of Sums of Independent Random Variables

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