Asset Pricing Theory
Friday, Jan. 6, 2017 3:15 PM – 5:15 PM
- Chair: Karl Schmedders, University of Zurich
Discretizing Nonlinear, Non-Gaussian Markov Processes with Exact Conditional Moments
AbstractApproximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing models with various underlying stochastic processes for the fundamentals, including a rare disasters model. Our method outperforms the solution accuracy of existing methods by orders of magnitude, while drastically simplifying the solution algorithm. The performance of our method is robust to parameters such as the number of grid points and the persistence of the process.
Solving Asset Pricing Models Using Laplace Transform Technique
AbstractTo be added.
Higher-Order Effects in Asset-Pricing Models With Long-Run Risks
AbstractThis paper presents an analysis of higher-order dynamics in asset pricing models with long-run risk. The numerical errors introduced by the ubiquitous Campbell-Shiller log-linearization approach are economically significant for many plausible choices of parameters and exogenous processes. The resulting errors in the model moments can exceed 75 percent and may lead to qualitatively wrong model predictions. For example, a common belief about long-run risk models, based on the log-linearization, is that conditional risk premia for long-run consumption risk are constant. The correct solution reveals that, on the contrary, risk premia show considerable time variation and are procyclical.
- G1 - Asset Markets and Pricing