Title: Correcting misspecification in stochastic discount factor models
Authors: Irina Zviadadze - HEC Paris (France) [presenting]
Raman Uppal - EDHEC Business School (United Kingdom)
Paolo Zaffaroni - Imperial College London (United Kingdom)
Abstract: It is shown how, given a misspecified stochastic discount factor (SDF), one can construct an admissible SDF, namely an SDF that prices assets correctly. We first extend the traditional Arbitrage Pricing Theory (APT) to capture misspecification from both pervasive (systematic) pricing errors and idiosyncratic pricing errors. The constructed admissible SDF, which uses the extended APT as its foundation, satisfies the already-known bound exactly. If the number of assets $N$ is large, the admissible SDF recovers the contribution of the missing pervasive factors completely without requiring one to identify the missing factors. Indeed, projecting the correction term of the SDF on the space spanned by the candidate missing factors, achieves an $R^2$ that converges to one as $N$ increases. Simulations demonstrate that the theory we develop is remarkably effective in correcting various sources of misspecification.