Regularizing Bayesian predictive regressions
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Related Research Unit(s)
|Journal / Publication||Journal of Asset Management|
|Online published||28 Sept 2020|
|Publication status||Published - Dec 2020|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85091608684&origin=recordpage|
Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions and seemingly unrelated regressions (SUR). By exploiting a duality between penalties and priors, we reinterpret two classic macrofinance studies: equity premium predictability and macroforecastability of bond risk premia. We find those plausible prior specifications for predictability for excess S&P 500 returns exist, using predictors as book-to-market ratios, consumption–wealth ratio, and T-bill rates. We evaluate our forecasts using a market-timing strategy and show how ours outperforms buy-and-hold. We also predict multiple bond excess returns involving a high-dimensional set of macroeconomic fundamentals with a regularized SUR model. We find the predictions from latent factor models such as PCA are sensitive to prior specifications. Finally, we conclude with directions for future research.
- Bayesian predictive regression, Bond risk premia, Equity-premium predictability, Maximum a posteriori, Predictor selection, Prior sensitivity analysis
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).