A sensitivity analysis of the impact of cumulative prospect theory (CPT) parameters on a Mean/Risk efficient frontier is performed through a simulation procedure, assuming a Multivariate Variance Gamma distribution for log-returns. The optimal investment problem for an agent with CPT preferences is then investigated empirically, by considering different parameters’ combinations for the CPT utility function. Three different portfolios, one hedge fund and two equity portfolios are considered in this study, where the Modified Herfindahl index is used as a measure of portfolio diversification, while the Omega ratio and the Information ratio are used as measures of performance.

Consigli, G., Hitaj, A., Mastrogiacomo, E. (2019). Portfolio choice under cumulative prospect theory: sensitivity analysis and an empirical study. COMPUTATIONAL MANAGEMENT SCIENCE, 16(1-2), 129-154 [10.1007/s10287-018-0333-x].

Portfolio choice under cumulative prospect theory: sensitivity analysis and an empirical study

Hitaj, Asmerilda
;
Mastrogiacomo, Elisa
2019

Abstract

A sensitivity analysis of the impact of cumulative prospect theory (CPT) parameters on a Mean/Risk efficient frontier is performed through a simulation procedure, assuming a Multivariate Variance Gamma distribution for log-returns. The optimal investment problem for an agent with CPT preferences is then investigated empirically, by considering different parameters’ combinations for the CPT utility function. Three different portfolios, one hedge fund and two equity portfolios are considered in this study, where the Modified Herfindahl index is used as a measure of portfolio diversification, while the Omega ratio and the Information ratio are used as measures of performance.
Articolo in rivista - Articolo scientifico
Cumulative prospect theory Non-convex optimization Robustness and sensitivity analysis Hedge funds
English
13-ago-2018
2019
16
1-2
129
154
none
Consigli, G., Hitaj, A., Mastrogiacomo, E. (2019). Portfolio choice under cumulative prospect theory: sensitivity analysis and an empirical study. COMPUTATIONAL MANAGEMENT SCIENCE, 16(1-2), 129-154 [10.1007/s10287-018-0333-x].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/204577
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