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Asset Pricing Theory

Paper Session

Friday, Jan. 6, 2017 3:15 PM – 5:15 PM

Sheraton Grand Chicago, Arkansas
Hosted By: Chinese Economic Association in North America
  • Chair: Karl Schmedders, University of Zurich

Examining the Sources of Excess Return Predictability: Stochastic Volatility or Persistent Investor Forecast Errors?

Kevin J. Lansing
,
Federal Reserve Bank of San Francisco
Stephen F. LeRoy
,
University of California-Santa Barbara
Jun Ma
,
University of Alabama

Abstract

This paper shows that realized excess returns on stocks relative to bonds in a consumption-based asset pricing model can be represented by an additive combination of the representative investor's percentage forecast errors. As a result, predictability of realized excess returns can arise from only two sources: (1) persistent stochastic volatility of the model's fundamental driving variables, or (2) persistent investor forecast errors, implying a departure from fully-rational expectations. This is a general result that holds for any stochastic discount factor, any consumption or dividend process, and any stream of bond coupon payments. From an empirical perspective, we investigate whether excess returns on stocks can be predicted using the previous period's excess returns, while controlling for persistent stochastic volatility in past returns and persistent stochastic volatility in the growth rates of consumption and dividends. We find evidence of predictability of excess returns from both of the above-named sources using both annual and quarterly data.

Discretizing Nonlinear, Non-Gaussian Markov Processes with Exact Conditional Moments

Leland E. Farmer
,
University of California-San Diego
Alexis Akira Toda
,
University of California-San Diego

Abstract

Approximating 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

Richard M. H. Suen
,
University of Leicester

Abstract

To be added.

Higher-Order Effects in Asset-Pricing Models With Long-Run Risks

Karl Schmedders
,
University of Zurich
Walt Pohl
,
University of Zurich
Ole Wilms
,
University of Zurich

Abstract

This 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.
Discussant(s)
Charles Ka Yui Leung
,
City University of Hong Kong
Kevin J. Lansing
,
Federal Reserve Bank of San Francisco
Zhiming Fu
,
Sichuan University
Alexis Akira Toda
,
University of California-San Diego
JEL Classifications
  • G1 - Asset Markets and Pricing