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Conferences

Session IV-2 Portfolio Management (17/12/2009 à 16h30)

C. Pérignon

Quantitative Forecast Model for the Application of the Black-Litterman Approach

Auteurs : Franziska Becker (Institute of Finance, Technical University Carolo-Wilhelmina) ;Marc Gürtler (Institute of Finance, Technical University Carolo-Wilhelmina)

!!Email!! : franziska.becker@tu-bs.de

Intervenants : Franziska Becker (Institute of Finance, Technical University Carolo-Wilhelmina) ;Marc Gürtler (Institute of Finance, Technical University Carolo-Wilhelmina)

Rapporteurs : Luis Goncalves-Pinto

The estimation of expected security returns is one of the major tasks for the practical implementation of the Markowitz optimization. Against this background, in 1992 Black and Litterman developed an approach based on (theoretical established) expected equilibrium returns which also accounts for subjective investors’ views. In contrast to historical estimated returns, which lead to extreme asset weights within the Markowitz optimization, the Black-Litterman model generally results in balanced portfolio weights. However, the existence of investors’ views is crucial for the Black-Litterman model and with absent views no active portfoliomanagement is possible. Moreover problems with the implementation of the model arise, as analysts’ forecasts are typically not available in the way they are needed for the Black-Litterman-approach. In this context we present how (publicly available) analysts’ dividend forecasts can be used to determine an a-priori-estimation of the expected returns and how they can be integrated into the Black-Litterman model. For this purpose confidences of the investors’ views are determined from the number of analysts’ forecasts as well as from a Monte-Carlo simulation. After introducing our two methods of view generation, we examine the effects of the Black-Litterman approach on portfolio weights in an empirical study. Finally, the performance of the Black-Litterman model is compared to alternative portfolio allocation strategies in an out-of-sample study that has not been presented in literature before to the best of our knowledge.

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How Does Illiquidity Affect Delegated Portfolio Choice?

Auteurs : Luis Goncalves-Pinto (Marshall School of Business, University of Southern California)

!!Email!! : lgoncalv@usc.edu

Intervenants : Luis Goncalves-Pinto (Marshall School of Business, University of Southern California)

Rapporteurs : Denitsa Stefanova

In a continuous-time dynamic portfolio choice framework, I study the problem of an investor who exogenously decides to delegate the administration of his or her savings to a risk-averse money manager who trades multiple risky assets in a thin market. I consider a manager who is rewarded for increasing the value of assets under management, which is the product of both the manager’s portfolio allocation decisions, taken over the investment period, and the money flows into and out of the fund, as a result of the portfolio performance relative to an exogenous benchmark. The model proposed here shows that, whenever the manager can substitute between more illiquid and less illiquid risky assets, he or she is likely to choose to hold an initial portfolio that is skewed toward more illiquid assets, and to gradually shift toward less illiquid assets over the investment period. The model further shows that several misalignments of objectives between the investor and the manager can lead to large utility costs on the part of the investor, and that these costs decrease with asset illiquidity. Solving for the shadow costs of illiquidity, the model indicates that delegated rather than direct investing is likely to lead to larger price discounts.

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Dynamic Correlation Hedging in Copula Models for Portfolio Selection

Auteurs : Denitsa Stefanova (University of Amsterdam); Redouane Elkamhi (University of Iowa, Henry B. Tippie College of Business)

!!Email!! : dstefanova@feweb.vu.nl.

Intervenants : Denitsa Stefanova (University of Amsterdam)

Rapporteurs : Olivier Lecourtois

In this paper we address the issue of modeling extreme asset co-movements and their implications for the hedging demands of a dynamic portfolio. We propose a model that is able to accommodate an extremal dependence structure through the stationary distribution of the state variables underlying the asset price process, as well as through a dynamic conditional correlation specification, driven by latent and observable factors. With this we aim at replicating the stylized fact of increased dependence during extreme market downturns, rising market-wide volatility, or worsening macroeconomic conditions. The model we propose accounts for stylized properties of asset returns in terms of univariate tail behavior as well as varying forms of dependence in the extremes , while keeping a continuous time complete market setup for a tractable portfolio solution. The paper further concentrates on the portfolio implications of those stylized facts. We isolate the intertemporal hedging demands, including those for correlation risk due to stochastic changes in the factors. Thus, we are able to analyze separately the impact of tail dependence through the unconditional distribution of the underlying state variables and that of conditional correlation on the portfolio holdings. We find that both correlation hedging demands and intertemporal hedges due to increased tail dependence have distinct portfolio implications and cannot act as substitutes to each other. As well, there are substantial economic costs for disregarding both the dynamics of conditional correlation and the dependence in the extremes.

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