Title: Factor models for conditional asset pricing
Authors: Paolo Zaffaroni - Imperial College London (United Kingdom) [presenting]
Abstract: A methodology is developed for inference on no-arbitrage conditional asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small. We show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors' risk premia and for the stochastic discount factor, spanned by the latent risk factors. In our set up naturally, almost every feature of the asset pricing model is allowed to be time-varying including loadings, idiosyncratic risk and the number of risk factors. Several Monte Carlo experiments corroborate our theoretical findings. An empirical application based on individual asset returns data demonstrates the power of the methodology, allowing us to tease out the empirical content of the time-variation stemming from asset pricing theory.