2018/01/31 by Ricardo Crisóstomo, Ricardo Crisostomo, Lorena Couso · 11 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Financial Markets and Investment Strategies #Financial Risk and Volatility Modeling #Heston model #Insurance, Mortality, Demography, Risk Management #Log-normal distribution #Nonparametric statistics #Probabilistic forecasting #Probabilistic logic #Series (stratigraphy) #Statistical model #Variance (accounting) #math.PR #q-fin.RM #q-fin.ST #stat.AP #stat.ME
paper · pdf · doi:10.1002/for.2521
published in Journal of Forecasting 37(5), 589-603 (Wiley) · Journal of Forecasting, 2018
openalex created_date 2017/10/06 · openalex publication_date 2018/04/16 · arxiv created 2018/05/07 · arxiv updated 2018/05/08 · openalex updated_date 2026/08/05
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 nonholistic 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, 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 GARCH‐based GJR‐FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model, or the nonparametric Breeden–Litzenberger formula yield biased predictions and are rejected in statistical tests.