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

Ricardo Crisóstomo and Lorena Couso

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Abstract: 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 Breeden-Litzenberger formula yield biased predictions and are rejected in statistical tests.

New Economics Papers: this item is included in nep-for
Date: 2018-01, Revised 2018-05
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Journal Article: Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes (2018) Downloads
Working Paper: Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes (2017) Downloads
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