Forecasting the Stock Return Distribution Using Macro-Finance Variables
نویسنده
چکیده
This paper proposes a new method to forecast S&P 500 return distribution by combining quantile regression models using macro-finance variables with volatility-based models including various standard EGARCH and stochastic volatility specifications. 30 density forecasting models are compared and combined in an out-of-sample forecasting exercise. Using macro-finance variables is found to help substantially in prediction; the best forecasts are obtained when all 30 models are combined. The proposed density forecasts are shown to be useful to an investor with a CRRA utility function in making optimal portfolio choice. Using the proposed density forecasts yields a certainty equivalent return that is up to 0.35% per month higher than can be obtained with the EGARCH model with a fat-tailed specification. JEL Classification: C32, C53, E32, E37.
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