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A1668
Topic: Contributions on financial time series and risk premia Title: A comprehensive dynamic Bayesian model combination approach to forecasting equity premia Authors:  Joscha Beckmann - University of Duisburg-Essen (Germany)
Rainer Schuessler - Helmut Schmidt University Hamburg (Germany) [presenting]
Abstract: We introduce a novel dynamic Bayesian model combination approach for predicting aggregate stock returns. Our method involves combining predictive densities in a data-adaptive fashion and simultaneously features (i) uncertainty about relevant predictor variables, (ii) parameter instability, (iii) time-varying volatility, (iv) time-varying model weights and (v) multivariate information. We analyze the predictability of monthly S$\&$P 500 returns and disentangle which components of prediction models pay off in terms of statistical accuracy and economic utility. As a key feature of our approach, we formally address the (possibly) diminishing relevance of past information over time. The flexibility embedded in our approach enhances density forecasting accuracy and provides sizeable economic utility gains. We find predictability to be strongly tied to business cycle fluctuations and document disagreement between statistical and economic metrics of forecast performance.