Ambiguity, Learning, and Asset Returns

We propose a novel generalized recursive smooth ambiguity model which allows a three-way separation among risk aversion, ambiguity aversion, and intertemporal substitution. We apply this utility to a consumption-based asset pricing model in which consumption and dividends follow hidden Markov regime-switching processes. Our calibrated model can match the mean equity premium, the mean riskfree rate, and the volatility of the equity premium observed in the data. In addition, our model can generate a variety of dynamic asset pricing phenomena, including the procyclical variation of price-dividend ratios, the countercyclical variation of equity premia and equity volatility, and the mean reversion of excess returns. The key intuition is that an ambiguity averse agent behaves pessimistically by attaching more weight to the pricing kernel in bad times when his continuation values are low.Ambiguity aversion; learning; asset pricing puzzles; model uncertainty; robustness; pessimism

Dynamic asset allocation with ambiguous return predictability

We study an investor's optimal consumption and portfolio choice problem when he is confronted with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor's aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor's stock allocation to decrease sharply given a small doubt of return predictability, even though the expected return according to the VAR model is large. Over much of the parameter space, the robust strategy is very close to the Bayesian strategy with Epstein-Zin preferences and risk aversion chosen to match the same average portfolio holdings. This is true in particular when the IID model is unlikely and the dividend yield is low, as in recent years. However, differences in strategies can be substantial if the IID model is unlikely and the dividend yield is high

A Model of Optimal Capital Sucture with Stochastic Interest Rates

This paper develops a model of optimal capital structure with stochastic interest rate which is assumed to follow a mean-reverting process. Closed-form solutions are obtained for both the value of the firm and the value of its risky debt. The paper finds that the current level and the long-run mean of the interest rate process play distinctive roles in our integrated model. The current level of the interest rate is critical in the pricing of risky bonds, while the long-run mean plays a key role in the determination of a firm’s optimal capital sucture such as the optimal coupon rate and leverage ratio. Our findings demonsate that a model of optimal capital sucture with a constant interest rate cannot price risky bonds and determine the optimal capital sucture simultaneously in a satisfactory manner. Furthermore, our numerical results indicate that the correlation between the stochastic interest rate and the asset return of a firm has little impact on the firm’s optimal capital structure

Managerial Responses to Incentives: Control of Firm Risk, Derivative Pricing Implications, and Outside Wealth Management

We model a firm’s value process controlled by a manager maximizing expected utility from restricted shares and employee stock options. The manager also dynamically controls allocation of his outside wealth. We explore interactions between those controls as he partially hedges his exposure to firm risk. Conditioning on his optimal behavior, control of firm risk increases the expected time to exercise for his employee stock options. It also reduces the percentage gap between his certainty equivalent and the firm’s fair value for his compensation, but that gap remains substantial. Managerial control also causes traded options to exhibit an implied volatility smile

Ambiguity, learning, and asset returns

We develop a consumption-based asset-pricing model in which the representative agent is ambiguous about the hidden state in consumption growth. He learns about the hidden state under ambiguity by observing past consumption data. His preferences are represented by the smooth ambiguity model axiomatized by Klibanoff et al. (2005, 2006). Unlike the standard Bayesian theory, this utility model implies that the posterior of the hidden state and the conditional distribution of the consumption process given a state cannot be reduced to a predictive distribution. By calibrating the ambiguity aversion parameter, the subjective discount factor, and the risk aversion parameter (with the latter two values between zero and one), our model can match the first moments of the equity premium and riskfree rate found in the data. In addition, our model can generate a variety of dynamic asset pricing phenomena, including the procyclical variation of price-dividend ratios, the countercyclical variation of equity premia and equity volatility, and the mean reversion and long horizon predictability of excess returns