Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)

Vladimir Pyrlik, Pavel Elizarov and Aleksandra Leonova

CERGE-EI Working Papers from The Center for Economic Research and Graduate Education - Economics Institute, Prague

Abstract: We assess the performance of selected machine learning algorithms (lasso, random forest, gradient boosting, and long short-term memory) in forecasting the daily realized volatility of returns of selected top stocks in the Russian stock market in comparison with a heterogeneous autoregressive realized volatility benchmark in 2018-2020. We seek to improve the predictive power of the models by including various economic indicators that carry information about future volatility. We find that lasso delivers a good combination of easy implementation and forecast precision. The other algorithms require fine-tuning and frequent re-training, otherwise they are likely to fail to outperform the benchmark often enough. Only the basic lagged log-RV values are significant explanatory variables in terms of the benchmark in-sample quality. Many economic indicators of mixed frequencies improve the predictive power of lasso though, including calendar and overnight effects, financial spillovers from local and global markets, and various macroeconomics indicators.

Keywords: heterogeneous autoregressive model; machine learning; lasso; gradient boosting; random forest; long short-term memory; realized volatility; Russian stock market; mixed-frequency data (search for similar items in EconPapers)
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp, nep-ets, nep-fmk and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.cerge-ei.cz/pdf/wp/Wp713.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cer:papers:wp713

Access Statistics for this paper

More papers in CERGE-EI Working Papers from The Center for Economic Research and Graduate Education - Economics Institute, Prague Contact information at EDIRC.
Bibliographic data for series maintained by Lucie Vasiljevova ().