Asset Pricing with Delayed Consumption Decisions

The attempt to match characteristics of asset pricing models such as the risk-free interest rate, equity premium and the Sharpe ratio for models with instantaneous consumption decisions in the context of stochastic growth models has not been very successful. Many recent versions of asset pricing models have, in order to match those financial characteristics better with the data, employed habit formation models where there is a delay in consumption decisions. Yet the results of those studies may depend on the solution techniques employed to solve the stochastic dynamic optimization model. In this paper a stochastic version of a dynamic programming method with adaptive grid scheme is applied to compute the above mentioned asset price characteristics with delayed consumption decisions, where the delayed consumption decision is treated as an additional state variable of the model. Since our method produces only negligible errors it is suitable to be used as solution technique for elaborate stochastic growth models with a delayed decision structure.stochastic growth, habit formation, stochastic DP, adaptive grid, asset pricing

Asset Pricing and Loss Aversion

Using standard preferences for asset pricing has not been very successful to match asset price characteristics such as the risk-free interest rate, equity premium and the Sharpe ratio to time series data. Behavioral finance has recently proposed more realistic preferences such as preferences with loss aversion to model asset pricing. Research has now started to explore the implications of behaviorally founded preferences for asset price characteristics. Yet the solution to those models is intricate and depends on the solution techniques employed. In this paper a stochastic version of a dynamic programming method with adaptive grid scheme is applied to compute the above mentioned asset price characteristics of a model with loss aversion in preferences. Since, as shown in Grüne and Semmler (2004), our method produces only negligible errors it is suitable to be used as solution technique for such models with more intricate decision structure.asset pricing, preferences with loss aversion, behavioral finance, equity premium, dynamic programming