Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution

DOI10.1080/07350015.2017.1281815zbMATH Open1548.62599MaRDI QIDQ6634847

James W. Taylor

Publication date: 8 November 2024

Published in: Journal of Business and Economic Statistics (Search for Journal in Brave)

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

• Unnamed Item
• Making and Evaluating Point Forecasts
• Quasi-maximum likelihood estimation for conditional quantiles
• Higher order elicitability and Osband's principle
• VAR for VaR: measuring tail dependence using multivariate regression quantiles
• Bayesian value-at-risk and expected shortfall forecasting via the asymmetric Laplace distribution
• Extreme-quantile tracking for financial time series
• Coherent Measures of Risk
• Goodness of Fit and Related Inference Processes for Quantile Regression
• Model Selection via Bayesian Information Criterion for Quantile Regression Models
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Tests of Conditional Predictive Ability

Related Items (7)

Inference for joint quantile and expected shortfall regression ⋮ Better the devil you know: improved forecasts from imperfect models ⋮ Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models ⋮ Realized Quantiles^* ⋮ Modeling and Forecasting Macroeconomic Downside Risk ⋮ Confidence Intervals for Conditional Tail Risk Measures in ARMA–GARCH Models ⋮ Estimation and backtesting of risk measures with emphasis on distortion risk measures

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