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Report NEP-FOR-2022-01-17
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:
- Frank Schorfheide & Dongho Song, 2021, "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," NBER Working Papers, National Bureau of Economic Research, Inc, number 29535, Dec.
- Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022, ""An application of deep learning for exchange rate forecasting"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202201, Jan, revised Jan 2022.
- Qinkai Chen & Christian-Yann Robert, 2021, "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers, arXiv.org, number 2112.09015, Dec, revised Dec 2021.
- Pan, Jingwei, 2021, "Volatility and Dependence Models with Applications to U.S. Equity Markets," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 129944.
- Linyi Yang & Jiazheng Li & Ruihai Dong & Yue Zhang & Barry Smyth, 2022, "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting," Papers, arXiv.org, number 2201.01770, Jan.
- Damir Filipović & Amir Khalilzadeh, 2021, "Machine Learning for Predicting Stock Return Volatility," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-95, Dec.