Financial Density Forecasts: A Comprehensive Comparison of Risk-Neutral and Historical Schemes
Journal of Forecasting. Doi.org/10.1002/for.2521
25 Pages Posted: 11 Sep 2017 Last revised: 3 Feb 2020
Date Written: December 12, 2017
We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies – small samples, limited models and non-holistic validations – by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new composite indicator, the Integrated Forecast Score (IFS), we show that risk-neutral densities outperform historical-based predictions in terms of information content. We find that the Variance Gamma model generates the highest out-of-sample likelihood of observed prices and the lowest predictive errors, whereas the ARCH-based GJR-FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model or the non-parametric Breeden-Litzenberger formula yield biased predictions and are rejected in statistical tests.
Keywords: Probabilistic forecasts, risk-neutral densities, ARCH models, ensemble predictions, model validation
JEL Classification: C14, G12, G13, C52, C53
Suggested Citation: Suggested Citation