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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp713, Nov.
- Filip Stanek, 2021, "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp712, Nov.
- Mestiri, Sami, 2021, "Modelling the volatility of Bitcoin returns using Nonparametric GARCH models," MPRA Paper, University Library of Munich, Germany, number 111116, Dec.
- Justin Dang & Aman Ullah, 2021, "Machine Learning Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting," Working Papers, University of California at Riverside, Department of Economics, number 202204, Jan, revised Jan 2022.