Computational methods in dynamic asset allocation

Généralement, les modèles d'allocation d'actifs sont sujets aux limitations imposées par les méthodes classiques d'optimisation. Dans cette thèse, notre démarche contourne cette contrainte en faisant bon usage de la disponibilité de puissance de calcul, que l'on peut, de nos jours, qualifier de presque ‘infinie' et que nous mettons à l'œuvre dans des heuristiques d'optimisation numériques. Ainsi, il est possible de résoudre des problèmes d'optimisation qui découlent de spécifications de modèles d'allocation plus réalistes. Les applications empiriques portent sur: i) des portefeuilles Omega optimaux ; ii) la réplication d'un indice de hedge funds avec des instruments liquides ; iii) construction d'un portefeuille de devises via une stratégie d'allocation d'actifs et iv) une analyse de l'effet des options sur la performance des portefeuilles d'actions

A heuristic model on the role of plasticity in adaptive evolution: Plasticity increases adaptation, population viability and genetic variation

An ongoing new synthesis in evolutionary theory is expanding our view of the sources of heritable variation beyond point mutations of fixed phenotypic effects to include environmentally sensitive changes in gene regulation. This expansion of the paradigm is necessary given ample evidence for a heritable ability to alter gene expression in response to environmental cues. In consequence, single genotypes are often capable of adaptively expressing different phenotypes in different environments, i.e. are adaptively plastic. We present an individual-based heuristic model to compare the adaptive dynamics of populations composed of plastic or non-plastic genotypes under a wide range of scenarios where we modify environmental variation, mutation rate and costs of plasticity. The model shows that adaptive plasticity contributes to the maintenance of genetic variation within populations, reduces bottlenecks when facing rapid environmental changes and confers an overall faster rate of adaptation. In fluctuating environments, plasticity is favoured by selection and maintained in the population. However, if the environment stabilizes and costs of plasticity are high, plasticity is reduced by selection, leading to genetic assimilation, which could result in species diversification. More broadly, our model shows that adaptive plasticity is a common consequence of selection under environmental heterogeneity, and hence a potentially common phenomenon in nature. Thus, taking adaptive plasticity into account substantially extends our view of adaptive evolution. © 2013 The Author(s) Published by the Royal Society. All rights reserved.Peer Reviewe

Replicating Hedge Fund Indices with Optimization Heuristics

Hedge funds offer desirable risk-return profiles; but we also find high management fees, lack of transparency and worse, very limited liquidity (they are often closed to new investors and disinvestment fees can be prohibitive). This creates an incentive to replicate the attractive features of hedge funds using liquid assets. We investigate this replication problem using monthly data of CS Tremont for the period of 1999 to 2009. Our model uses historical observations and combines tracking accuracy, excess return, and portfolio correlation with the index and the market. Performance is evaluated considering empirical distributions of excess return, final wealth and correlations of the portfolio with the index and the market. The distributions are compiled from a set of portfolio trajectories computed by a resampling procedure. The nonconvex optimization problem arising from our model specification is solved with a heuristic optimization technique. Our preliminary results are encouraging as we can track the indices accurately and enhance performance (e.g. have lower correlation with equity markets).Hedge Funds, Hedge Fund Replication, Asset Allocation, Portfolio Optimization, Optimization Heuristics, Drawdown

Constructing Long/Short Portfolios with the Omega ratio

We construct portfolios with an alternative selection criterion, the Omega function, which can be expressed as the ratio of two partial moments of the returns distribution. Finding Omega-optimal portfolios, in particular under realistic constraints like cardinality restrictions, requires to solve non-convex optimisation problems. Since standard (gradient-based) optimisation methods fail here, we suggest to use a heuristic technique (Threshold Accepting). The main purpose of the paper is to investigate the empirical performance of the selected portfolios, especially the effects of allowing short positions. Many studies on portfolio optimisation assume that short sales are not allowed. This is despite the fact that theoretically, short positions can improve the risk-return characteristics of a portfolio, and practically, institutional investors can and do sell stocks short.We investigate whether removing the non-negativity constraint really improves out-of-sample portfolio performance under realistic assumptions, that is when optimal weights need to be estimated from the data, different transaction costs apply to long and short positions or short selling is restricted to specific assets.Optimisation heuristics, Threshold Accepting, Portfolio Optimisation