Forecasting risk measures using intraday and overnight information

Douglas G. Santos, Osvaldo Candido and Paula V. Tófoli

The North American Journal of Economics and Finance, 2022, vol. 60, issue C

Abstract: Volatility forecasts are important for a number of practical financial decisions, such as those related to risk management. When working with high-frequency data from markets that operate during a reduced time, an approach to deal with the overnight return volatility is needed. In this context, we use heterogeneous autoregressions (HAR) to model the variation associated with the intraday activity, with distinct realized measures as regressors, and, to model the overnight returns, we use augmented GARCH type models. Then, we combine the HAR and GARCH models to generate forecasts for the total daily return volatility. In an empirical study, for returns on six international stock indices, we analyze the separate modeling approach in terms of its out-of-sample forecasting performance of daily volatility, Value-at-Risk and Expected Shortfall relative to standard models from the literature. In particular, the overall results are favorable for the separate modeling approach in comparison with some HAR models based on realized variance measures for the whole day and the standard GARCH model.

Keywords: Volatility forecasting; Realized volatility; Overnight volatility; Value-at-Risk; Expected Shortfall (search for similar items in EconPapers)
JEL-codes: C22 C53 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:60:y:2022:i:c:s1062940822000250

DOI: 10.1016/j.najef.2022.101669

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