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A Monte-Carlo Method for Optimal Portfolios

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  • Jérôme B. Detemple

    ()

  • René Garcia
  • Marcel Rindisbacher

    ()

Abstract

This paper provides (i) new results on the structure of optimal portfolios, (ii) economic insights on the behavior of the hedging components and (iii) an analysis of simulation-based numerical methods. The core of our approach relies on closed form solutions for Melliavin derivatives of diffusion processes which simplify their numerical simulation and facilitate the computation and simulation of the hedging components of optimal portfolios. One of our procedures relies on a variance-stabilizing transformation of the underlying process which eliminates stochastic integrals from the representation of Malliavin derivatives and ensures the existence of an exact weak approximation scheme. This improves the performance of Monte-Carlo methods in the numerical implementation of portfolio rules derived on the basis of probabilistic arguments. Our approach is flexible and can be used even when the dimensionality of the set of underlying state variables is large. We implement the procedure for a class of bivariate and trivariate models in which the uncertainty is described by diffusion processes for the market price of risk (MPR), the interest rate (IR) and other relevant factors. After calibrating the models to the data we document the behavior of the portfolio demand and the hedging components relative to the parameters of the model such as risk aversion, investment horizon, speeds of mean-reversion, IR and MPR levels and volatilities. We show that the hedging terms are important and cannot be ignored for asset allocation purposes. Risk aversion and investment horizon emerge as the most relevant factors: they have a substantial impact on the size of the optimal portfolio and on its economic properties for realistic values of the models' parameters. Cet article établit des résultats nouveaux sur (i) la structure des portefeuilles optimaux, (ii) le comportement des termes de couverture et (iii) les méthodes numériques de simulation en la matière. Le fondement de notre approche repose sur l'obtention de formules explicites pour les dérivées de Malliavin de processus de diffusion, formules qui simplifient leur simulation numérique et facilitent le calcul des composantes de couverture des portefeuilles optimaux. Une de nos procédures utilise une transformation des processus sous-jacents qui élimine les intégrales stochastiques de la représentation des dérivées de Malliavin et assure l'existence d'une approximation faible exacte. Cette transformation améliore alors la performance des méthodes de Monte-Carlo lors de l'implémentation numérique des politiques de portefeuille dérivées par des méthodes probabilistes. Notre approche est flexible et peut être utilisée même lorsque la dimension de l'espace des variables d'états sous-jacentes est large. Cette méthode est appliquée dans le cadre de modèles bivariés et trivariés dans lesquels l'incertitude est décrite par des mouvements de diffusion pour le prix de marché du risque, le taux d'intérêt et les autres facteurs d'importance. Après avoir calibré le modèle aux données nous examinons le comportement du portefeuille optimal et des composantes de couverture par rapport aux paramètres tels que l'aversion au risque, l'horizon d'investissement, le taux d'intérêt et le prix de risque du marché. Nous démontrons l'importance des demandes de couverture. L'aversion au risque et l'horizon d'investissement émergent comme des facteurs déterminants qui ont un impact substantiel sur la taille du portefeuille optimal et sur ses propriétés économiques.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2000s-05.

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Date of creation: 01 Jan 2000
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Handle: RePEc:cir:cirwor:2000s-05

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Keywords: Optimal portfolios; hedging demands; Malliavin derivatives; explicit solutions; multiple state variables; IR-hedge; MPR-hedge; Monte Carlo simulation; Doss transformation; portfolio behavior; Portefeuilles optimaux; demandes de couverture; dérivées de Malliavin; solutions explicites; variables d'état multiples; couverture de taux d'intérêt; couverture de prix du risque de marché; simulation de Monte Carlo; transformation de Doss; comportement des portefeuilles;

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  1. Andrew W. Lo, . "Maximum Likelihood Estimation of Generalized Ito Processes with Discretely Sampled Data," Rodney L. White Center for Financial Research Working Papers 15-86, Wharton School Rodney L. White Center for Financial Research.
  2. Daniel B. Nelson & Dean P. Foster, 1994. "Asypmtotic Filtering Theory for Univariate Arch Models," NBER Technical Working Papers 0129, National Bureau of Economic Research, Inc.
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