WorldCat Identities

Tardella, Fabio

Overview
Works: 19 works in 20 publications in 1 language and 28 library holdings
Roles: Other, Author
Publication Timeline
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Most widely held works by Fabio Tardella
Largest minimal inversion-complete and pair-complete sets of permutations by Eric Balandraud( )

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

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

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

Linear vs. quadratic portfolio selection models with hard real-world constraints by Francesco Cesarone( )

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

An optimization-diversification approach to portfolio selection by Francesco Cesarone( )

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 in-sample risk reduction with respect to variance and to some other popular risk measures, and very good out-of-sample 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 Multi-Greedy 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 Multi-Greedy heuristic that extends the classical single-threaded greedy approach to a multiple-threaded setting. Finally, we provide empirical results on real-world data showing that the diversified optimal portfolios are only slightly suboptimal in-sample with respect to optimal portfolios, and generally show improved out-of-sample 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

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 real-world and on simulated data confirms accuracy and efficiency of our heuristic approach to the ERB model also in comparison with some state-of-the-art 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 risk-return 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 Semi-Absolute Deviation (LAMSAD) and the Limited Asset Conditional Value at-Risk (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 tri-objective problem at no additional computational cost.We compare the out-of-sample 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

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

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

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 real-world data. Expectiles are also used as risk measures in the classical risk-return 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

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

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

 
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Languages
English (19)