This paper provides (i) simulation-based approaches for the computation of asset allocation rules, (ii) economic insights on the behavior of the hedging components and (iii) a comparison of numerical methods. For general utility functions with wealth-dependent risk aversion and diffusion state variable processes, hedging demands are obtained as conditional expectations of random variables depending on the parameters of the model, which can then be estimated using standard simulation methods. We propose a modified simulation approach which relies on a simple transformation of the underlying state variables and improves the performance of Monte-Carlo estimators of portfolio rules. Our approach is flexible and applies to (i) arbitrary utility functions, (ii) any finite number of state variables, (iii) general diffusion processes for state variables and (iv) any finite number of assets. The procedure is implemented for a class of multivariate diffusions for the market price of risk (MPR), the interest rate (IR) and other factors such as dividends and volatility. After calibrating the models to the data we document the portfolio behavior. Intertemporal hedging demands are found to (i) significantly increase the demand for stocks and (ii) exhibit low volatility.
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