Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes
Ricardo Crisóstomo and
Papers from arXiv.org
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)
Working Paper: Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1801.08007
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