Portfolio balancing requires estimates of covariance between asset returns. Returns data have histories which greatly vary in length, since assets begin public trading at different times. This can lead to a huge amount of missing data—too much for the conventional imputation-based approach. Fortunately, a well-known factorization of the MVN likelihood under the prevailing historical missingness pattern leads to a simple algorithm of OLS regressions that is much more reliable. When there are more assets than returns, however, OLS becomes unstable. Gramacy et al. (2008) showed how classical shrinkage regression may be used instead, thus extending the state of the art to much bigger asset collections, with further accuracy and interpretation advantages. In this paper, we detail a fully Bayesian hierarchical formulation that extends the framework further by allowing for heavy-tailed errors, relaxing the historical missingness assumption, and accounting for estimation risk. We illustrate how this approach compares favorably to the classical one using synthetic data and an investment exercise with real returns. An accompanying R package is on CRAN.
Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing
Ester Pantaleo
2010-01-01
Abstract
Portfolio balancing requires estimates of covariance between asset returns. Returns data have histories which greatly vary in length, since assets begin public trading at different times. This can lead to a huge amount of missing data—too much for the conventional imputation-based approach. Fortunately, a well-known factorization of the MVN likelihood under the prevailing historical missingness pattern leads to a simple algorithm of OLS regressions that is much more reliable. When there are more assets than returns, however, OLS becomes unstable. Gramacy et al. (2008) showed how classical shrinkage regression may be used instead, thus extending the state of the art to much bigger asset collections, with further accuracy and interpretation advantages. In this paper, we detail a fully Bayesian hierarchical formulation that extends the framework further by allowing for heavy-tailed errors, relaxing the historical missingness assumption, and accounting for estimation risk. We illustrate how this approach compares favorably to the classical one using synthetic data and an investment exercise with real returns. An accompanying R package is on CRAN.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
