Real Estate Portfolio Management : Optimization under Risk Aversion

This paper deals with real estate portfolio optimization when investors are risk averse. In this framework, we determine several types of optimal times to sell a diversified real estate and analyze their properties. The optimization problem corresponds to the maximization of a concave utility function defined on the terminal value of the portfolio. We extend previous results (Baroni et al., 2007, and Barthélémy and Prigent, 2009), established for the quasi linear utility case, where investors are risk neutral. We consider four cases. In the first one, the investor knows the probability distribution of the real estate index. In the second one, the investor is perfectly informed about the real estate market dynamics. In the third case, the investor uses an intertemporal optimization approach which looks like an American option problem. Finally, the buy-and-hold strategy is considered. For these four cases we analyze numerically the solutions that we compare with those of the quasi linear case. We show that the introduction of risk aversion allows to better take account of the real estate market volatility. We also introduce the notion of compensating variation to better compare all these solutions.Real estate portfolio, Optimal holding period, Risk aversion, Real estate market volatility

Optimal Time to Sell in Real Estate Portfolio Management

This paper examines the properties of optimal times to sell a diversified real estate portfolio. The portfolio value is supposed to be the sum of the discounted free cash flows and the discounted terminal value (the discounted selling price). According to Baroni et al. (2007b), we assume that the terminal value corresponds to the real estate index. The optimization problem corresponds to the maximization of a quasi-linear utility function. We consider three cases. The first one assumes that the investor knows the probability distribution of the real estate index. However, at the initial time, he has to choose one deterministic optimal time to sell. The second one considers an investor who is perfectly informed about the market dynamics. Whatever the random event that generates the path, he knows the entire path from the beginning. Then, given the realization of the random variable, the path is deterministic for this investor. Therefore, at the initial time, he can determine the optimal time to sell for each path of the index. Finally, the last case is devoted to the analysis of the intertemporal optimization, based on the American option approach. We compute the optimal solution for each of these three cases and compare their properties. The comparison is also made with the buy-and-hold strategy.Real estate portfolio, Optimal holding period, American option.

Weak Convergence of Hedging Strategies of Contingent Claims

This paper presents results on the convergence for hedging strategies in the setting of incomplete financial markets. We examine the convergence of the so-called locally risk-minimizing strategy. It is proved that such a choice for the trading strategy, when perfect hedging of contingent claims is infeasible, is robust under weak convergence. Several fundamental examples, such as trinomial trees and stochastic volatility models, extracted from the financial modeling literature illustrate this property for both deterministic and random time intervals shrinking to zero.Weak convergence; Incomplete financial markets; Locally risk-minimizing strategy; Hedging strategy; Minimal martingale measure

Hedging global environment risks: An option based portfolio insurance

This paper introduces a financial hedging model for global environment risks. Our approach is based on portfolio insurance under hedging constraints. Investors are assumed to maximize their expected utilities defined on financial and environmental asset values. The optimal investment is determined for quite general utility functions and hedging constraints. In particular, our results suggest how to introduce derivative assets written on the environmental asset.utility maximization, hedging, environmental asset, martingale theory

A Risk Management Approach for Portfolio Insurance Strategies

Controlling and managing potential losses is one of the main objectives of the Risk Management. Following Ben Ameur and Prigent (2007) and Chen et al. (2008), and extending the first results by Hamidi et al. (2009) when adopting a risk management approach for defining insurance portfolio strategies, we analyze and illustrate a specific dynamic portfolio insurance strategy depending on the Value-at-Risk level of the covered portfolio on the French stock market. This dynamic approach is derived from the traditional and popular portfolio insurance strategy (Cf. Black and Jones, 1987 ; Black and Perold, 1992) : the so-called "Constant Proportion Portfolio Insurance" (CPPI). However, financial results produced by this strategy crucially depend upon the leverage - called the multiple - likely guaranteeing a predetermined floor value whatever the plausible market evolutions. In other words, the unconditional multiple is defined once and for all in the traditional setting. The aim of this article is to further examine an alternative to the standard CPPI method, based on the determination of a conditional multiple. In this time-varying framework, the multiple is conditionally determined in order to remain the risk exposure constant, even if it also depends upon market conditions. Furthermore, we propose to define the multiple as a function of an extended Dynamic AutoRegressive Quantile model of the Value-at-Risk (DARQ-VaR). Using a French daily stock database (CAC 40) and individual stocks in the period 1998-2008), we present the main performance and risk results of the proposed Dynamic Proportion Portfolio Insurance strategy, first on real market data and secondly on artificial bootstrapped and surrogate data. Our main conclusion strengthens the previous ones : the conditional Dynamic Strategy with Constant-risk exposure dominates most of the time the traditional Constant-asset exposure unconditional strategies.CPPI, Portfolio insurance, VaR, CAViaR, quantile regression, dynamic quantile model.

Structured portfolio analysis under SharpeOmega ratio

This paper deals with performance measurement of financial structured products. For this purpose, we introduce the SharpeOmega ratio, based on put as downside risk measure. This allows to take account of the asymmetry of the return probability distribution. We provide general results about the optimization of some standard structured portfolios with respect to the SharpeOmega ratio. We determine in particular the optimal combination of risk free, stock and call/put instruments with respect to this performance measure. We show that, contrary to Sharpe ratio maximization (Goetzmann et al., 2002), the payoff of the optimal structured portfolio is not necessarily increasing and concave. We also discuss about the interest of the asset management industry to reward high Sharpe Omega ratios

Eliciting Utility for (Non)Expected Utility Preferences Using Invariance Transformations

This paper presents a methodology to determine the preferences of an individual facing risk in the framework of (non)-expected utility theory. When individual preference satisfies a given invariance property, his utility function is solution of a functional equation associated to a specific transformation. Conversely, there exist transformations characterizing any given utility function and its invariance property. More precisely, invariance with respect to two transformations uniquely determines the individual utility function. We provide examples of such transformations for CARA or CRRA utility, but also with any other utility specification and discuss the example of DARA and IRRA specifications.Utility theory; risk aversion, elicitation of preferences.

Portfolio Optimization within Mixture of Distributions

The recent financial crisis has highlighted the necessity to introduce mixtures of probability distributions in order to improve the estimation of asset returns and in particular to better take account of risks. Since Pearson (1894), these mixtures have been intensively used in many scientific fields since they provide very convenient mathematical tools to examine various statistical data and to approximate many probability distributions. They are typically introduced to model the choice of probability distributions among a given parametric family. The coefficients of the mixture usually correspond to the relative frequencies of each possible parameter. In this framework, we examine the single-period portfolio choice model, which has been addressed in the partial equilibrium framework, by Brennan and Solanki (1981), Leland (1980) and Prigent (2006). We consider an investor who wants to maximize the expected utility of the value of his portfolio consisting of one risk-free asset and one risky asset. We provide and analyze the solution for log return with mixture distributions, in particular for the mixture Gaussian case. The optimal portfolio is characterized for arbitrary utility functions. Our results show that mixture of distributions can have significant implications on the portfolio management

Structured portfolio analysis under SharpeOmega ratio

This paper deals with performance measurement of financial structured products. For this purpose, we introduce the SharpeOmega ratio, based on put as downside risk measure. This allows to take account of the asymmetry of the return probability distribution. We provide general results about the optimization of some standard structured portfolios with respect to the SharpeOmega ratio. We determine in particular the optimal combination of risk free, stock and call/put instruments with respect to this performance measure. We show that, contrary to Sharpe ratio maximization (Goetzmann et al., 2002), the payoff of the optimal structured portfolio is not necessarily increasing and concave. We also discuss about the interest of the asset management industry to reward high Sharpe Omega ratios.Structured portfolio, Performance measure, SharpeOmega ratio.

A Risk Management Approach for Portfolio Insurance Strategies

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/CESFramDP2009.htmClassification JEL : G11, C13, C14, C22, C32.Documents de travail du Centre d'Economie de la Sorbonne 2009.34 - ISSN : 1955-611XControlling and managing potential losses is one of the main objectives of the Risk Management. Following Ben Ameur and Prigent (2007) and Chen et al. (2008), and extending the first results by Hamidi et al. (2009) when adopting a risk management approach for defining insurance portfolio strategies, we analyze and illustrate a specific dynamic portfolio insurance strategy depending on the Value-at-Risk level of the covered portfolio on the French stock market. This dynamic approach is derived from the traditional and popular portfolio insurance strategy (Cf. Black and Jones, 1987 ; Black and Perold, 1992) : the so-called "Constant Proportion Portfolio Insurance" (CPPI). However, financial results produced by this strategy crucially depend upon the leverage - called the multiple - likely guaranteeing a predetermined floor value whatever the plausible market evolutions. In other words, the unconditional multiple is defined once and for all in the traditional setting. The aim of this article is to further examine an alternative to the standard CPPI method, based on the determination of a conditional multiple. In this time-varying framework, the multiple is conditionally determined in order to remain the risk exposure constant, even if it also depends upon market conditions. Furthermore, we propose to define the multiple as a function of an extended Dynamic AutoRegressive Quantile model of the Value-at-Risk (DARQ-VaR). Using a French daily stock database (CAC 40) and individual stocks in the period 1998-2008), we present the main performance and risk results of the proposed Dynamic Proportion Portfolio Insurance strategy, first on real market data and secondly on artificial bootstrapped and surrogate data. Our main conclusion strengthens the previous ones : the conditional Dynamic Strategy with Constant-risk exposure dominates most of the time the traditional Constant-asset exposure unconditional strategies.Evaluer, contrôler et gérer les pertes potentielles est un des objectifs principaux de la gestion des risques. Suivant Ben Ameur et Prigent (2007) et Chen et alii (2008) et étendant les premiers résultats de Hamidi et alii (2009), nous adoptons une stratégie d'assurance de portefeuille conforme aux problématiques de la gestion des risques. Cette stratégie dynamique d'assurance de portefeuille dont le levier dépend d'un modèle dynamique autorégressif de quantile est illustrée sur le marché des actions françaises sur des données réelles et simulées. Nos conclusions renforcent les résultats précédents : les stratégies dynamiques à exposition constante au risque dominent la plus part du temps les stratégies inconditionnelles