Tardella, Fabio
Overview
Works:  19 works in 20 publications in 1 language and 28 library holdings 

Roles:  Other, Author 
Publication Timeline
.
Most widely held works by
Fabio Tardella
Largest minimal inversioncomplete and paircomplete sets of permutations by
Eric Balandraud(
)
2 editions published between 2015 and 2017 in English and held by 3 WorldCat member libraries worldwide
2 editions published between 2015 and 2017 in English and held by 3 WorldCat member libraries worldwide
A general class of greedily solvable linear programs by
Maurice Queyranne(
Book
)
1 edition published in 1993 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 1993 in English and held by 3 WorldCat member libraries worldwide
Submodularity and related properties in continuous optimization by
Fabio Tardella(
)
1 edition published in 1996 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 1996 in English and held by 2 WorldCat member libraries worldwide
Equal Risk Bounding is better than Risk Parity for portfolio selection by
Francesco Cesarone(
)
1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide
Linear vs. quadratic portfolio selection models with hard realworld constraints by
Francesco Cesarone(
)
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
Scaling, proximity, and optimization of integrally convex functions by Satoko Moriguchi(
)
1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide
An optimizationdiversification approach to portfolio selection by
Francesco Cesarone(
)
1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide
Why Small Portfolios Are Preferable and How to Choose Them by
Francesco Cesarone(
)
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
One of the fundamental principles in portfolio selection models is minimization of risk through diversification of the investment. However, this principle does not necessarily translate into a request for investing in all the assets of the investment universe. Indeed, following a line of research started by Evans and Archer almost 50 years ago, we provide here further evidence that small portfolios are sufficient to achieve almost optimal insample risk reduction with respect to variance and to some other popular risk measures, and very good outofsample performances. While leading to similar results, our approach is significantly different from the classical one pioneered by Evans and Archer. Indeed, we describe models for choosing the portfolio of a prescribed size with the smallest possible risk, as opposed to the random portfolio choice investigated in most of the previous works. We find that the smallest risk portfolios generally require no more than 15 assets. Furthermore, it is almost always possible to find portfolios that are just 1% more risky than the smallest risk portfolios and contain no more than 10 assets. The preference for small optimal portfolios is also justified by recent theoretical results on the estimation errors for the parameters required by portfolio selection models.Our empirical analysis is based on some new and on some publicly available benchmark data sets often used in the literature
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
One of the fundamental principles in portfolio selection models is minimization of risk through diversification of the investment. However, this principle does not necessarily translate into a request for investing in all the assets of the investment universe. Indeed, following a line of research started by Evans and Archer almost 50 years ago, we provide here further evidence that small portfolios are sufficient to achieve almost optimal insample risk reduction with respect to variance and to some other popular risk measures, and very good outofsample performances. While leading to similar results, our approach is significantly different from the classical one pioneered by Evans and Archer. Indeed, we describe models for choosing the portfolio of a prescribed size with the smallest possible risk, as opposed to the random portfolio choice investigated in most of the previous works. We find that the smallest risk portfolios generally require no more than 15 assets. Furthermore, it is almost always possible to find portfolios that are just 1% more risky than the smallest risk portfolios and contain no more than 10 assets. The preference for small optimal portfolios is also justified by recent theoretical results on the estimation errors for the parameters required by portfolio selection models.Our empirical analysis is based on some new and on some publicly available benchmark data sets often used in the literature
A MultiGreedy Approach to Optimal Diversified Portfolio Selection by
Francesco Cesarone(
)
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
The classical approaches to optimal portfolio selection call for finding a feasible portfolio that optimizes a risk measure, or a gain measure, or a combination thereof by means of a utility function or of a performance measure. However, the optimization approach tends to amplify the estimation errors on the parameters required by the model, such as expected returns and covariances. For this reason, the risk parity model, a novel risk diversification approach to portfolio selection, has been recently theoretically developed and used in practice, mainly for the case of the volatility risk measure.Here we first provide new theoretical results for the risk parity approach for general risk measures. Then we propose a novel framework for portfolio selection that combines the diversification and the optimization approaches through the solution of a hard nonlinear mixed integer or pseudo Boolean problem. For the latter problem we propose an efficient and accurate MultiGreedy heuristic that extends the classical singlethreaded greedy approach to a multiplethreaded setting. Finally, we provide empirical results on realworld data showing that the diversified optimal portfolios are only slightly suboptimal insample with respect to optimal portfolios, and generally show improved outofsample performance with respect to their purely diversified or purely optimized counterparts
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
The classical approaches to optimal portfolio selection call for finding a feasible portfolio that optimizes a risk measure, or a gain measure, or a combination thereof by means of a utility function or of a performance measure. However, the optimization approach tends to amplify the estimation errors on the parameters required by the model, such as expected returns and covariances. For this reason, the risk parity model, a novel risk diversification approach to portfolio selection, has been recently theoretically developed and used in practice, mainly for the case of the volatility risk measure.Here we first provide new theoretical results for the risk parity approach for general risk measures. Then we propose a novel framework for portfolio selection that combines the diversification and the optimization approaches through the solution of a hard nonlinear mixed integer or pseudo Boolean problem. For the latter problem we propose an efficient and accurate MultiGreedy heuristic that extends the classical singlethreaded greedy approach to a multiplethreaded setting. Finally, we provide empirical results on realworld data showing that the diversified optimal portfolios are only slightly suboptimal insample with respect to optimal portfolios, and generally show improved outofsample performance with respect to their purely diversified or purely optimized counterparts
Some extensions of the fundamental theorem of linear programming and applications by
Fabio Tardella(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Equal Risk Bounding Is Better than Risk Parity for Portfolio Selection by
Francesco Cesarone(
)
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
Risk Parity (RP), also called equally weighted risk contribution, is a recent approach to risk diversification for portfolio selection. RP is based on the principle that the fractions of the capital invested in each asset should be chosen so as to make the total risk contributions of all assets equal among them. We show here that the Risk Parity approach is theoretically dominated by an alternative similar approach that does not actually require equally weighted risk contribution of all assets but only an equal upper bound on all such risks. This alternative approach, called Equal Risk Bounding (ERB), requires the solution of a nonconvex quadratically constrained optimization problem. The ERB approach, while starting from different requirements, turns out to be strictly linked to the RP approach. Indeed, when short selling is allowed, we prove that an ERB portfolio is actually an RP portfolio with minimum variance. When short selling is not allowed, there is a unique RP portfolio and it contains all assets in the market. In this case, the ERB approach might lead to the RP portfolio or it might lead to portfolios with smaller variance that do not contain all assets, and where the risk contributions of each asset included in the portfolio is strictly smaller than in the RP portfolio. We define a new riskiness index for assets that allows to identify those assets that are more likely to be excluded from the ERB portfolio. With these tools we then provide an exact method for small size nonconvex ERB models and a very efficient and accurate heuristic for larger problems of this type. In the case of a common constant pairwise correlation among all assets, a closed form solution to the ERB model is obtained and used to perform a parametric analysis when varying the level of correlation. The practical advantages of the ERB approach over the RP strategy are illustrated with some numerical examples. Computational experience on realworld and on simulated data confirms accuracy and efficiency of our heuristic approach to the ERB model also in comparison with some stateoftheart local and global optimization codes
1 edition published in 2018 in English and held by 1 WorldCat member library worldwide
Risk Parity (RP), also called equally weighted risk contribution, is a recent approach to risk diversification for portfolio selection. RP is based on the principle that the fractions of the capital invested in each asset should be chosen so as to make the total risk contributions of all assets equal among them. We show here that the Risk Parity approach is theoretically dominated by an alternative similar approach that does not actually require equally weighted risk contribution of all assets but only an equal upper bound on all such risks. This alternative approach, called Equal Risk Bounding (ERB), requires the solution of a nonconvex quadratically constrained optimization problem. The ERB approach, while starting from different requirements, turns out to be strictly linked to the RP approach. Indeed, when short selling is allowed, we prove that an ERB portfolio is actually an RP portfolio with minimum variance. When short selling is not allowed, there is a unique RP portfolio and it contains all assets in the market. In this case, the ERB approach might lead to the RP portfolio or it might lead to portfolios with smaller variance that do not contain all assets, and where the risk contributions of each asset included in the portfolio is strictly smaller than in the RP portfolio. We define a new riskiness index for assets that allows to identify those assets that are more likely to be excluded from the ERB portfolio. With these tools we then provide an exact method for small size nonconvex ERB models and a very efficient and accurate heuristic for larger problems of this type. In the case of a common constant pairwise correlation among all assets, a closed form solution to the ERB model is obtained and used to perform a parametric analysis when varying the level of correlation. The practical advantages of the ERB approach over the RP strategy are illustrated with some numerical examples. Computational experience on realworld and on simulated data confirms accuracy and efficiency of our heuristic approach to the ERB model also in comparison with some stateoftheart local and global optimization codes
Linear vs. Quadratic Portfolio Selection Models in Practice by
Francesco Cesarone(
)
1 edition published in 2019 in English and held by 1 WorldCat member library worldwide
Several riskreturn portfolio models take into account practical limitations on the number of assets to include in the portfolio and on their weights. We present here a comparative study, both from the efficiency and from the performance viewpoint, of the Limited Asset Markowitz (LAM), the Limited Asset Mean SemiAbsolute Deviation (LAMSAD) and the Limited Asset Conditional Value atRisk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints.The mixed integer linear LAMSAD and LACVaR models are solved with a state of the art commercial code, while the mixed integer quadratic LAM model is solved both with a commercial code and with a more efficient new method, recently proposed by the authors. Rather unexpectedly, for medium to large sizes it is easier to solve the quadratic LAM model with the new method, than to solve the linear LACVaR and LAMSAD models with the commercial solver. Furthermore, the new method has the advantage of finding all the extreme points of a more general triobjective problem at no additional computational cost.We compare the outofsample performances of the three models and of the equally weighted portfolio. We show that there is no apparent dominance relation among the different approaches and, in contrast with previous studies, we find that the equally weighted portfolio does not seem to have any advantage over the three proposed models. Our empirical results are based on some new and old publicly available data sets often used in the literature
1 edition published in 2019 in English and held by 1 WorldCat member library worldwide
Several riskreturn portfolio models take into account practical limitations on the number of assets to include in the portfolio and on their weights. We present here a comparative study, both from the efficiency and from the performance viewpoint, of the Limited Asset Markowitz (LAM), the Limited Asset Mean SemiAbsolute Deviation (LAMSAD) and the Limited Asset Conditional Value atRisk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints.The mixed integer linear LAMSAD and LACVaR models are solved with a state of the art commercial code, while the mixed integer quadratic LAM model is solved both with a commercial code and with a more efficient new method, recently proposed by the authors. Rather unexpectedly, for medium to large sizes it is easier to solve the quadratic LAM model with the new method, than to solve the linear LACVaR and LAMSAD models with the commercial solver. Furthermore, the new method has the advantage of finding all the extreme points of a more general triobjective problem at no additional computational cost.We compare the outofsample performances of the three models and of the equally weighted portfolio. We show that there is no apparent dominance relation among the different approaches and, in contrast with previous studies, we find that the equally weighted portfolio does not seem to have any advantage over the three proposed models. Our empirical results are based on some new and old publicly available data sets often used in the literature
Carathéodory, helly and radon numbers for sublattice convexities by
Maurice Queyranne(
)
1 edition published in 2015 in English and held by 1 WorldCat member library worldwide
1 edition published in 2015 in English and held by 1 WorldCat member library worldwide
PIECEWISE CONCAVITY AND DISCRETE APPROACHES TO CONTINUOUS MINIMAX PROBLEMS by
Fabio TARDELLA(
)
1 edition published in 1999 in Undetermined and held by 1 WorldCat member library worldwide
1 edition published in 1999 in Undetermined and held by 1 WorldCat member library worldwide
On the existence of polyhedral convex envelopes by
Fabio Tardella(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Risk Parity with Expectiles by Fabio Bellini(
)
1 edition published in 2019 in English and held by 1 WorldCat member library worldwide
A recent popular approach to portfolio selection aims at diversifying risk by looking for the so called Risk Parity portfolios. These are defined by the condition that the risk contributions of all assets to the global risk of the portfolio are equal. The Risk Parity approach has been originally introduced for the volatility risk measure. In this paper we consider expectiles as risk measures, we refine results on their differentiability and additivity, and we show how to define Risk Parity portfolios when the expectiles are used as coherent risk measures. Furthermore, we propose several methods for practically finding Risk Parity portfolios with respect to expectiles and we compare their accuracy and efficiency on realworld data. Expectiles are also used as risk measures in the classical riskreturn approach to portfolio selection, where we present a new linear programming formulation
1 edition published in 2019 in English and held by 1 WorldCat member library worldwide
A recent popular approach to portfolio selection aims at diversifying risk by looking for the so called Risk Parity portfolios. These are defined by the condition that the risk contributions of all assets to the global risk of the portfolio are equal. The Risk Parity approach has been originally introduced for the volatility risk measure. In this paper we consider expectiles as risk measures, we refine results on their differentiability and additivity, and we show how to define Risk Parity portfolios when the expectiles are used as coherent risk measures. Furthermore, we propose several methods for practically finding Risk Parity portfolios with respect to expectiles and we compare their accuracy and efficiency on realworld data. Expectiles are also used as risk measures in the classical riskreturn approach to portfolio selection, where we present a new linear programming formulation
Sublattices of product spaces: hulls, representation and counting by
Maurice Queyranne(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Bimonotone linear inequalities and sublattices of Rn by
Maurice Queyranne(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Submodular function minimization in Zn and searching in monge arrays by
Maurice Queyranne(
Book
)
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
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Related Identities
 Cesarone, Francesco Other Author
 SpringerLink (Online service) Other
 Queyranne, Maurice Author
 Scozzari, Andrea Other
 Queyranne, Maurice Other Author
 Balandraud, Eric Author
 Spieksma, Frits C.R. (Frederik Cornelis Rafae͏̈l) 1964
 Moriguchi, Satoko Author
 Tamura, Akihisa Other
 Murota, Kazuo Other
Languages