Dynamic semiparametric models for expected shortfall (and value-at-risk)

DOI10.1016/j.jeconom.2018.10.008zbMath1452.62785arXiv1707.05108OpenAlexW2962769745MaRDI QIDQ2000869

Andrew J. Patton, Rui Chen, Johanna F. Ziegel

Publication date: 1 July 2019

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.05108

zbMATH Keywords

forecastingtailsrisk managementcrashesgeneralized autoregressive score

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70)

Related Items (19)

Risks in emerging markets equities: time-varying versus spatial risk analysis ⋮ Semiparametric modeling of multiple quantiles ⋮ Isotonic regression for elicitable functionals and their Bayes risk ⋮ Loss function-based change point detection in risk measures ⋮ Predicting the Global Minimum Variance Portfolio ⋮ A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures ⋮ Observation-driven filtering of time-varying parameters using moment conditions ⋮ Simple factor realized stochastic volatility models ⋮ PELVE: probability equivalent level of VaR and ES ⋮ A joint quantile and expected shortfall regression framework ⋮ Estimating value-at-risk and expected shortfall using the intraday low and range data ⋮ A data-driven framework for consistent financial valuation and risk measurement ⋮ Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics ⋮ Quantile-based portfolios: post-model-selection estimation with alternative specifications ⋮ Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches ⋮ Dynamic large financial networks \textit{via} conditional expected shortfalls ⋮ Dynamic expected shortfall: a spectral decomposition of tail risk across time horizons ⋮ Computation of expected shortfall by fast detection of worst scenarios ⋮ Backtesting expected shortfall and beyond

Uses Software

• CAViaR

Cites Work

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