首页|Combining density forecast accuracy tests: an application to agricultural, energy, and metal commodities

Combining density forecast accuracy tests: an application to agricultural, energy, and metal commodities

扫码查看
原文链接
  • NETL
  • NSTL
  • Oxford Univ Press
This study develops a new methodology for combining density forecast accuracy tests and assessing the relevance of psychological indicators in predicting commodity returns. Density forecasts provide a complete description of the uncertainty associated with a prediction and are highly requested by policymakers, central bankers, and financial operators to define policy actions, manage financial risks, and assess portfolio selection. The proposed methodology combines different tests and derives the p-value of the resulting test statistic by Monte Carlo simulations. To assess the power of the proposed methodology, we implement a set of experiments for several data-generating processes. Based on an empirical forecasting exercise applied to agricultural, energy, and metal commodities, we find that sentiment variables and psychological factors improve the density forecasts of commodity futures returns, especially for agricultural commodities. Additionally, combinations of sentiment variables are more powerful in predicting returns than considering them separately.

ARMAX-EGARCH-t modelbehavioural financecommodity futuresdensity forecasts

Bernardina Algieri、Arturo Leccadito、Danilo Sicoli、Diana Tunaru

展开 >

Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, Rende, CS 87036, Italy##Department of Economic and Technological Change, Zentrum fur Entwicklungsforschung (ZEF), Universitat Bonn, Genscherallee 3, Bonn 53113, Germany

Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, Rende, CS 87036, Italy##LFIN/UDAM, Universite catholique de Louvain, Voie du Roman Pays 34, Louvain-la-Neuve 1348, Belgium

Independent Researcher, Via Tommasone 54, Pandino, CR 26025, Italy

University of Kent, Kent Business School, Canterbury CT2 7FS, UK

展开 >

2025

10.1093/jrsssc/qlae069

Journal of the royal statistical society, Series C. Applied statistics

Journal of the royal statistical society, Series C. Applied statistics

ISSN：0035-9254
年,卷(期)：2025.74(Pt.3)
  • 59