Time-varying variance and skewness in realized volatility measures

Anne Opschoor and Andre Lucas

International Journal of Forecasting, 2023, vol. 39, issue 2, 827-840

Abstract: We propose new empirical models to capture the dynamics of the variance and skewness in realized volatility measures. We find that time-variation in variance and skewness of realized measures is a key empirical feature, even after accounting for well-known, stylized facts such as long-memory-type persistence and large incidental observations. Using a broad range of 89 US stocks across different sectors over 2001–2019, we show that these are not incidental phenomena of a few stocks but are widely shared. Accounting for dynamics in the variance and skewness of realized measures results in significantly better in-sample fit and out-of-sample unconditional density and quantile forecasts.

Keywords: Realized kernel; Heavy tails; Dynamic F distribution; Time-varying shape-parameters; Vol-of-vol; Score-driven dynamics (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:827-840

DOI: 10.1016/j.ijforecast.2022.02.009

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