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Report NEP-FOR-2026-09-21
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:
- Frédérqiue Bec & Heino Bohn Nielsen, 2026, "Nonlinear Forecast Error Variance Decompositions: Shapley Shares, Generalized Shapley Shares, and the Role of Structural Interactions," Thema Working Papers, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS, number 2026-09.
- Arkadiusz Lipiecki & Rafa{l} Weron, 2026, "Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?," Papers, arXiv.org, number 2609.00089, Aug.
- Haiqiang Chen & Li Chen & Yunlong Chen & Difang Huang & Bo Zhang, 2026, "Does Training on Future Data Pay? Look-Ahead Bias in Forecasting with Pretrained Models," Papers, arXiv.org, number 2609.20554, Sep.
- Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Zero-Shot Conditional Forecasting and the Information Content of Central Bank Paths," CESifo Working Paper Series, CESifo, number 12972.
- Parsa Yousefnezhad & Gholamreza Mansourfar & Mohammadreza Feizi Derakhshi, 2026, "Forecasting Economically Significant Bitcoin Moves: A Multi-Scale TCN with Profit-Optimized Thresholds," Papers, arXiv.org, number 2608.26174, Jul.
- Tomasz Weron & Katarzyna Maciejowska, 2026, "From electricity prices to profits: multidimensional probabilistic forecasting for BESS trading," Papers, arXiv.org, number 2608.26122, Jun.
- Ayla Jungbluth & Johannes Lederer & Simon Trimborn, 2026, "Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 26-070/III, Sep.
- Sean Wan & Dongping Liu & Luyao Zhang, 2026, "StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction," Papers, arXiv.org, number 2609.18949, Aug.
- Anastasios Megaritis & Dimitrios Bakas & Theodora Bermpei & Athanasios Triantafyllou, 2026, "Term Spread Volatility as a Leading Indicator of Economic Activity," Post-Print, HAL, number hal-05740995, Aug, DOI: 10.1002/for.70132.
- Nneka Umeorah & Tolulope Fadina, 2026, "A Temporal Multiplex Graph Neural Network for Systemic Risk Transmission in Global Banking," Papers, arXiv.org, number 2608.27295, Aug.
- Sayan Dhan & Selvaraju Natarajan, 2026, "AlphaRJM: Reward-Jump Memory for Stochastic Return-Guided Alpha Discovery," Papers, arXiv.org, number 2609.08581, Sep.
- Chuanzhen Wang & Alice Zhang & Wei Chen & Michael Brown, 2026, "Graph-Based Modeling of Financial Volatility Dynamics," Papers, arXiv.org, number 2608.26127, Jun.
- Luke Hartigan, 2026, "Estimating the Common Output Cycle in Australia," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-78, Sep.
- Joshua Le Grice, 2026, "Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation," Papers, arXiv.org, number 2608.27076, Aug.
- Irma Alonso-Alvarez & Iván Kataryniuk & David López-Salido & Javier J. Pérez, 2026, "Geopolitical Oil-Supply Scenarios and Macroeconomic Risks: A Bayesian Toolkit for Policy Analysis," Occasional Papers, Banco de España, number 2616, Sep, DOI: https://doi.org/10.53479/44325.
- Manuel Naviglio & Fabrizio Lillo, 2026, "Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models," Papers, arXiv.org, number 2609.06085, Sep.
- Baichuan Li & Mengxiao Wang, 2026, "Option-Implied Signals and Crash Risk: Predictability and Machine-Learning Evidence from U.S. Equity Options," Papers, arXiv.org, number 2608.26115, Jun.
- Alex Chen, 2026, "A Statistical-Finance Benchmark for Same-Day Directional Stock Prediction: Walk-Forward Evidence from SPY," Papers, arXiv.org, number 2608.26106, May.
- Michael Stanley Smith & Lin Deng, 2026, "Vector Vine Copula Models for Multivariate Longitudinal Data," Papers, arXiv.org, number 2609.19547, Sep.
- Hesandi Mallawarachchi & Senilka Madurapperumage & Nadil Kulathunge & Thilokya Angeesa & Nethsith Gunaweera & Sandeepa Weerasekara & Patalee Narasinghe & Nisansa de Silva & Sandareka Wickramanayake, 2026, "Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues," Papers, arXiv.org, number 2608.24894, Jun, revised Aug 2026.