Large Nonparametric Estimation Of Time Varying Characteristics Of Intertemporal Asset Pricing Models

Economic research of the last decade linking macroeconomic fundamentals to asset prices has revealed evidence that standard intertemporal asset pricing theory is not successful in explaining (unconditional) first moments of asset market characteristics such as the risk free intrest rate, stock return volatility, equity premium and the Sharpe ratio. Subsequent empirical research has pursued the question whether those characteristics of asset markets are time varying and, in particular, varying over the business cycle. Recently intertemporal asset pricing models have been employed to replicate those time varying characteristics. The analytical and numerical work of Kandel and Stambaugh (1990, 1991), Rouwenhorst (1995), Jermann (1998), Campbell and Cochrane (1998) and Veronesi (1999) are important steps into this direction. The aim of our contribution is (1) to relax some of the assumptions that previous work has imposed on underlying economic and financial variables, (2) to extend the solution technique of Marcet and Den Haan (1990) for those models by nonparametric expectations and (3) to propose a new estimation procedure based on the above solution technique.We choose, as in Rouwenhorst (1995), the Real Business Cycle model as starting point. To allow for nonparametric expectations in the parameterized expectations approach for numerically solving the intertemporal economic model we employ the Local Linear Maps (LLMs) of Ritter, Martinetz and Schulten (1992) to approximate conditional expectations in the Euler equation. In our estimation approach based on nonparametric expectations we are able to use full structural information and, consequently, Monte Carlo simulations show that our estimations are less biased than the widely applied GMM procedure of Hansen and Singleton (1982). Based on quarterly U.S. data we also empirically estimate structural parameters of the model and explore its time varying asset price chracteristics. We find an indication that the the baseline RBC model is able to capture this time variation for European as well as U.S. data although the high level of Sharpe ratio can only be matched, e.g., by including internal habit formation and market frictions as in Boldrin, Christiano and Fisher (1999). Therefore, our estimation scheme could be extended directly.