Conditional Asset Pricing - Predicting Time Varying Beta-Factors with Group Method of Data Handling Methods

Abstract

Allowing for time-varying risk premia yields sophisticated asset pricing models, but the search for adequate model specifications is more challenging. We introduce, to our knowledge, previously in conditional asset pricing not used Group Method of Data Handling (GMDH) that rests on sorting out requiring statsitical models for complex problems of unknown structure but does not require a model to predict conditional variation in betas. We find that lagged instruments used to proxy for expected returns in conditional asset pricing provide a challenge not only for the unconditional CAPM but also the Fama-French-model. Thereby non-linear GMDH-algorithms challenge traditional models of conditional asset pricing as we find a highly non-linear influence of lagged instruments on both conditional alphas and betas. Therefore, predetermining a structure for functional relationships between conditional alphas as well as betas and lagged instruments may lead to a significant misspecification of asset pricing models.