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A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework

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  • Chao Wang
  • Richard Gerlach
  • Qian Chen

Abstract

A new realized joint Value-at-Risk (VaR) and expected shortfall (ES) regression framework is proposed, through incorporating a measurement equation into the original joint VaR and ES regression model. The measurement equation models the contemporaneous dependence between the realized measures (e.g. Realized Variance and Realized Range) and the latent conditional quantile. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with the inherent micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte Carlo method is employed for estimation and forecasting, whose properties are assessed and compared with maximum likelihood estimator through simulation study. In a forecasting study, the proposed models are applied to 7 market indices and 2 individual assets, compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH, CARE and Taylor (2017) joint VaR and ES quantile regression models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.

Suggested Citation

  • Chao Wang & Richard Gerlach & Qian Chen, 2018. "A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework," Papers 1807.02422, arXiv.org.
  • Handle: RePEc:arx:papers:1807.02422
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    References listed on IDEAS

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