Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes

Ricardo Crisóstomo and Lorena Couso

CNMV Working Papers from CNMV- Spanish Securities Markets Commission - Research and Statistics Department

Abstract: We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the main methodological 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. Through the development of a new 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. (search for similar items in EconPapers)
JEL-codes: C14 C52 C53 G12 G13 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes (2018)
Working Paper: Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes (2018)
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