Reference : Practical Weight-Constrained Conditioned Portfolio Optimization Using Risk Aversion I...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Business & economic sciences : Finance
Practical Weight-Constrained Conditioned Portfolio Optimization Using Risk Aversion Indicator Signals
Schiltz, Jang mailto [University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Luxembourg School of Finance (LSF) >]
Boissaux, Marc []
19th annual conference of the multinational finance society
Cracow, Poland
[en] Optimal Control ; Portfolio Optimization
[en] Within a traditional context of myopic discrete-time mean-variance portfolio optimisation, the problem
of conditioned optimisation, in which predictive information about returns contained in a signal is used
to inform the choice of portfolio weights, was rst expressed and solved in concrete terms by Ferson and
Siegel ([1]). An optimal control formulation of conditioned portfolio problems was proposed and justi ed
by Boissaux and Schiltz ([2]). This opens up the possibility of solving variants of the basic problem that
do not allow for closed-form solutions through the use of standard numerical algorithms used for the
discretisation of optimal control problems.
The present paper contributes to the empirical literature on this topic. Risk aversion (or, equivalently,
risk appetite) indicators, aiming to quantify di erent time-varying de nitions of investor attitudes toward
risk, are both provided by nancial service providers and discussed in the academic literature - see e.g.
Coudert and Gex ([3]). We compare the performance of strategies resulting from conditioned optimisation
and using several possible indicators for signalling purposes, to that obtained using standard approaches
to portfolio investment. In particular, we report on both ex ante improvements to the accessible e cient
frontier as measured through the typical associated metrics such as the Sharpe ratio, and ex post results
a ected, most notably, by speci cation errors regarding the relationship between signal and returns.
We then discuss di erent problem parameters, examine their impact on performance and check whether
signi cant ex post improvements may be achieved through optimal parameter selection.

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