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

Anne Opschoor*, André Lucas

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)622-633
Number of pages12
JournalInternational Journal of Forecasting
Volume37
Issue number2
Early online date7 Sept 2020
DOIs
Publication statusPublished - Apr 2021

Funding

☆ We appreciate the comments of participants at the 13th Meeting of the NESG (Amsterdam, May 2019) and seminar participants at the Accounting/Finance and Economics Seminar Series at Cork University Business School (September 2019) and the Centre for Financial Markets (CFM) Research Seminar Series at the Michael Smurfit Graduate Business School (September, 2019).

FundersFunder number
CFM
Centre for Financial Markets
Michael Smurfit Graduate Business School
Korea University Business School

    Keywords

    • Expected Shortfall
    • F distribution
    • Overnight volatility
    • Realized variance
    • Score-driven dynamics
    • Value-at-Risk

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