Provider: Ingenta Connect
Database: Ingenta Connect
Content: application/x-research-info-systems
TY - ABST
AU - Masih, Rumi
AU - Masih, A. Mansur M.
AU - Mie, Kilian
TI - Model uncertainty and asset return predictability: an application of Bayesian model averaging
JO - Applied Economics
PY - 2010-06-01T00:00:00///
VL - 42
IS - 15
SP - 1963
EP - 1972
N2 - We investigate model uncertainty associated with predictive regressions employed in asset return forecasting research. We use simple combination and Bayesian model averaging (BMA) techniques to compare the performance of these forecasting approaches in short-vs. long-run horizons of S&P500 monthly excess returns. Simple averaging involves an equally-weighted averaging of the forecasts from alternative combinations of factors used in the predictive regressions, whereas BMA involves computing the predictive probability that each model is the true model and uses these predictive probabilities as weights in combing the forecasts from different models. From a given set of multiple factors, we evaluate all possible pricing models to the extent, which they describe the data as dictated by the posterior model probabilities. We find that, while simple averaging compares quite favorably to forecasts derived from a random walk model with drift (using a 10-year out-of-sample iterative period), BMA outperforms simple averaging in longer compared to shorter forecast horizons. Moreover, we find further evidence of the latter when the predictive Bayesian model includes shorter, rather than longer lags of the predictive factors. An interesting outcome of this study tends to illustrate the power of BMA in suppressing model uncertainty through model as well as parameter shrinkage, especially when applied to longer predictive horizons.
UR - http://www.ingentaconnect.com/content/routledg/raef/2010/00000042/00000015/art00009
M3 - doi:10.1080/00036840701736214
UR - https://doi.org/10.1080/00036840701736214
ER -