This paper derives and analyzes dynamic timing strategies of a fund manager with private information. Endogenous timing strategies generated by various information structures and skills, and associated fund styles, are identified. Endogenous fund returns are characterized in the public information of an uninformed observer. Timing components are identified. The paper provides foundations for regression analyses of fund returns and tests of market timing.

A new decomposition of the optimal portfolio, in dynamic models with von Neumann–Morgenstern preferences and Ito prices, is established. The formula rests on a change of numéraire that uses pure discount bonds as units of account. The dynamic hedging demand has two components. The first hedge insures against fluctuations in an optimally designed bond with a maturity date matching the investor's horizon. The second hedge immunizes against fluctuations in the market price of risk in the bond numéraire. Various applications are examined. New results concerning the behavior of extremely risk-averse individuals, the demand for bonds and its long-horizon limit, and the optimal portfolio in incomplete markets are derived.

This paper compares two recent Monte Carlo methods advocated for the computation of optimal portfolio rules. The candidate methods are the approach based on Monte Carlo with Malliavin Derivatives (MCMD) proposed by Detemple, Garcia and Rindisbacher [Detemple et al., 2003. A Monte-Carlo method for optimal portfolios. Journal of Finance 58, 401–406] and the approach based on Monte Carlo with regression (MCR) of Brandt, Goyal, Santa-Clara and Stroud [Brandt et al., 2003. A simulation approach to dynamic portfolio choice with an application to learning about return predictability. Working paper, Wharton School]. Our comparisons are carried out in the context of various intertemporal portfolio choice problems with two assets, a risky asset and a riskless asset, and different configurations of the state variables. The specifications studied include a linear model with a single state variable admitting an exact solution and a non-linear model with two state variables that requires a purely numerical resolution. The accuracies of the candidate methods are compared. We provide, in particular, efficiency plots displaying the speed–accuracy trade-off for various selections of the relevant simulation and discretization parameters. MCMD is shown to dominate in all the settings considered.

This paper studies equilibrium in a pure exchange economy with unobservable Markov switching growth regimes and beliefs-dependent risk aversion (BDRA). Risk aversion is stochastic and depends nonlinearly on consumption and beliefs. Equilibrium is obtained in closed form. The market price of risk, the interest rate, and the stock return volatility acquire new components tied to fluctuations in beliefs. A three-regime specification is estimated using the generalized method of moments (GMM). Model moments match their empirical counterparts for a variety of unconditional moments, including the equity premium, stock returns volatility, and the correlations between stock returns and consumption and dividends. Dynamic features of the data, such as the countercyclical behaviors of the equity premium and volatility, are also captured. Model volatility provides a good fit for realized volatility. A new factor, the information risk premium, is found to be a strong predictor of future excess returns. These results are obtained with an estimated risk aversion fluctuating between 1.44 and 1.93.