The Experts below are selected from a list of 23517 Experts worldwide ranked by ideXlab platform
Frank J. Fabozzi - One of the best experts on this subject based on the ideXlab platform.
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Bond Portfolio Optimization in the Presence of Duration Constraints
The Journal of Fixed Income, 2018Co-Authors: Romain Deguest, Frank J. Fabozzi, Lionel Martellini, Vincent MilhauAbstract:Although there exists an abundant literature on the benefits and limits of scientific diversification in the equity universe, little is known about the out-of-sample performance of Portfolio Optimization models in the fixed-income universe. In this article, the authors address two key challenges that are specific to bond Portfolio Optimization, namely, the presence of duration constraints and the presence of no-arbitrage restrictions on risk parameter estimates, for which no equivalent exists in the equity universe. In an application to sovereign bonds in the eurozone, they find that the use of Portfolio Optimization techniques based on robust estimators for risk parameters generates an improvement in investor welfare compared with the use of ad hoc bond benchmarks such as equally weighted or cap-weighted Portfolios. These results are robust with respect to changes in the number of constituents in the Portfolio and the rebalancing period, and in the presence of duration or weight constraints.
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60 Years of Portfolio Optimization: Practical challenges and current trends
European Journal of Operational Research, 2014Co-Authors: Petter N. Kolm, Reha Tütüncü, Frank J. FabozziAbstract:Abstract The concepts of Portfolio Optimization and diversification have been instrumental in the development and understanding of financial markets and financial decision making. In light of the 60 year anniversary of Harry Markowitz’s paper “Portfolio Selection,” we review some of the approaches developed to address the challenges encountered when using Portfolio Optimization in practice, including the inclusion of transaction costs, Portfolio management constraints, and the sensitivity to the estimates of expected returns and covariances. In addition, we selectively highlight some of the new trends and developments in the area such as diversification methods, risk-parity Portfolios, the mixing of different sources of alpha, and practical multi-period Portfolio Optimization.
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Robust Portfolio Optimization
The Journal of Portfolio Management, 2007Co-Authors: Frank J. Fabozzi, Petter N. Kolm, Dessislava A. Pachamanova, Sergio M. FocardiAbstract:As quantitative techniques have become commonplace in the investment industry, the mitigation of estimation and model risk in Portfolio management has grown in importance. Robust Optimization, which incorporates estimation error directly into the Portfolio Optimization process, is typically used with conventional robust statistical estimation methods. This perspective on the robust Optimization approach reviews useful practical extensions and discusses potential applications for robust Portfolio Optimization.
Indana Lazulfa - One of the best experts on this subject based on the ideXlab platform.
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A Firefly Algorithm for Portfolio Optimization
Journal of the Indonesian Mathematical Society, 2019Co-Authors: Indana LazulfaAbstract:Portfolio Optimization is the process of allocating capital among a universe of assets to achieve better risk – return trade-off. Portfolio Optimization is a solution for investors to get the return as large as possible and make the risk as small as possible. Due to the dynamic nature of financial markets, the Portfolio needs to be rebalanced to retain the desired risk-return characteristics. This study proposed multi objective Portfolio Optimization model with risk, return as the objective function. For multi objective Portfolio Optimization problems will be used mean-variance model as risk measures. All these Portfolio Optimization problems will be solved by Firefly Algorithm (FA).
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Portfolio Optimization With Buy-in Thresholds Constraint Using Simulated Annealing Algorithm
2017Co-Authors: Indana Lazulfa, Pujo Hari SaputroAbstract:Portfolio Optimization is a solution for investors to get the return as much as possible and also to minimize risk as small as possible. In this research, we use risk measures for Portfolio Optimization, namely mean-variance model. For single objective Portfolio Optimization problem, especially minimizing risk of Portfolio, we used mean-variance as risk measure with constraint such as buy-in thresholds. Buy-in thresholds set a lower limit on all assets that are part of Portfolio. All this Portfolio Optimization problems will be solved by simulated annealing algorithm. The performance of the tested metaheuristics was good enough to solve Portfolio Optimization.
Petter N. Kolm - One of the best experts on this subject based on the ideXlab platform.
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60 Years of Portfolio Optimization: Practical challenges and current trends
European Journal of Operational Research, 2014Co-Authors: Petter N. Kolm, Reha Tütüncü, Frank J. FabozziAbstract:Abstract The concepts of Portfolio Optimization and diversification have been instrumental in the development and understanding of financial markets and financial decision making. In light of the 60 year anniversary of Harry Markowitz’s paper “Portfolio Selection,” we review some of the approaches developed to address the challenges encountered when using Portfolio Optimization in practice, including the inclusion of transaction costs, Portfolio management constraints, and the sensitivity to the estimates of expected returns and covariances. In addition, we selectively highlight some of the new trends and developments in the area such as diversification methods, risk-parity Portfolios, the mixing of different sources of alpha, and practical multi-period Portfolio Optimization.
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Robust Portfolio Optimization
The Journal of Portfolio Management, 2007Co-Authors: Frank J. Fabozzi, Petter N. Kolm, Dessislava A. Pachamanova, Sergio M. FocardiAbstract:As quantitative techniques have become commonplace in the investment industry, the mitigation of estimation and model risk in Portfolio management has grown in importance. Robust Optimization, which incorporates estimation error directly into the Portfolio Optimization process, is typically used with conventional robust statistical estimation methods. This perspective on the robust Optimization approach reviews useful practical extensions and discusses potential applications for robust Portfolio Optimization.
Emanuela Rosazza Gianin - One of the best experts on this subject based on the ideXlab platform.
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Portfolio Optimization with Quasiconvex Risk Measures
Mathematics of Operations Research, 2015Co-Authors: Elisa Mastrogiacomo, Emanuela Rosazza GianinAbstract:In this paper, we focus on the Portfolio Optimization problem associated with a quasiconvex risk measure (satisfying some additional assumptions). For coherent/convex risk measures, the Portfolio Optimization problem has been already studied in the literature. Following the approach of Ruszczynski and Shapiro [Ruszczynski A, Shapiro A (2006) Optimization of convex risk functions. Math. Oper. Res. 31(3):433–452.], but by means of quasiconvex analysis and notions of subdifferentiability, we characterize optimal solutions of the Portfolio problem associated with quasiconvex risk measures. The shape of the efficient frontier in the mean-risk space and some particular cases are also investigated.
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Portfolio Optimization with Quasiconvex Risk Measures
SSRN Electronic Journal, 2013Co-Authors: Elisa Mastrogiacomo, Emanuela Rosazza GianinAbstract:In this paper, we focus on the Portfolio Optimization problem associated to a quasiconvex risk measure (satisfying some additional assumptions). For coherent/convex risk measures, the Portfolio Optimization problem has been already studied by Gaivoronski and Pflug (2005), Rockafellar and Uryasev (2000) and Ruszczynski and Shapiro (2006), among others. Following the approach of Ruszczynski and Shapiro (2006) but by means of quasiconvex analysis and notions of subdifferentiability, we characterize optimal solutions of the Portfolio problem associated to quasiconvex risk measures. The shape of the efficient frontier in the mean-risk space and some particular cases are also investigated.
Grazia M Speranza - One of the best experts on this subject based on the ideXlab platform.
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twenty years of linear programming based Portfolio Optimization
European Journal of Operational Research, 2014Co-Authors: Renata Mansini, Włodzimierz Ogryczak, Grazia M SperanzaAbstract:Markowitz formulated the Portfolio Optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the Portfolio Optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in Portfolio Optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when Portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable Portfolio Optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features.
