Report NEP-FOR-2022-01-31

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
• Filip Stanek, 2021. "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers wp712, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
• Mestiri, Sami, 2021. "Modelling the volatility of Bitcoin returns using Nonparametric GARCH models," MPRA Paper 111116, University Library of Munich, Germany.
• Justin Dang & Aman Ullah, 2021. "Machine Learning Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting," Working Papers 202204, University of California at Riverside, Department of Economics, revised Jan 2022.