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.

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 29535, National Bureau of Economic Research, Inc.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
• Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, 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) 129944, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Linyi Yang & Jiazheng Li & Ruihai Dong & Yue Zhang & Barry Smyth, 2022. "NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting," Papers 2201.01770, arXiv.org.
• Damir Filipović & Amir Khalilzadeh, 2021. "Machine Learning for Predicting Stock Return Volatility," Swiss Finance Institute Research Paper Series 21-95, Swiss Finance Institute.