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Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting

Anne Opschoor and Andre Lucas ()

International Journal of Forecasting, 2021, vol. 37, issue 2, 622-633

Abstract: We present a new model to decompose total daily return volatility into high-frequency-based open-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to obtain robust volatility dynamics. Applying our new model to a 2001–2018 sample of individual stocks and stock indices, we find substantial in-sample variation of the daytime-to-total volatility ratio over time. We apply the model to out-of-sample forecasting, evaluated in terms of Value-at-Risk and Expected Shortfall. Models with a non-constant volatility ratio typically perform best, particularly in terms of Value-at-Risk. Our new model performs especially well during turbulent times. All results are generally stronger for individual stocks than for index returns.

Keywords: Overnight volatility; Realized variance; F distribution; Score-driven dynamics; Value-at-Risk; Expected Shortfall (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.ijforecast.2020.07.009

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Handle: RePEc:eee:intfor:v:37:y:2021:i:2:p:622-633