Continuous-time mean-variance portfolio optimization in a jump-diffusion market

2010 ◽  
Vol 34 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Özge Sezgin Alp ◽  
Ralf Korn
Keyword(s):  
Portfolio Optimization ◽  
Continuous Time ◽  
Jump Diffusion ◽  
Mean Variance Portfolio ◽  
Mean Variance

PRACTICAL INVESTMENT CONSEQUENCES OF THE SCALARIZATION PARAMETER FORMULATION IN DYNAMIC MEAN–VARIANCE PORTFOLIO OPTIMIZATION

2021 ◽  
Vol 24 (05) ◽  
pp. 2150029
Author(s):  
PIETER M. VAN STADEN ◽  
DUY-MINH DANG ◽  
PETER A. FORSYTH
Keyword(s):  
Risk Aversion ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Jump Diffusion ◽  
Investment Strategies ◽  
Institutional Settings ◽  
Portfolio Optimization Problem ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Theoretical Risk

We consider the practical investment consequences of implementing the two most popular formulations of the scalarization (or risk-aversion) parameter in the time-consistent dynamic mean–variance (MV) portfolio optimization problem. Specifically, we compare results using a scalarization parameter assumed to be (i) constant and (ii) inversely proportional to the investor’s wealth. Since the link between the scalarization parameter formulation and risk preferences is known to be nontrivial (even in the case where a constant scalarization parameter is used), the comparison is viewed from the perspective of an investor who is otherwise agnostic regarding the philosophical motivations underlying the different formulations and their relation to theoretical risk-aversion considerations, and instead simply wishes to compare investment outcomes of the different strategies. In order to consider the investment problem in a realistic setting, we extend some known results to allow for the case where the risky asset follows a jump-diffusion process, and examine multiple sets of plausible investment constraints that are applied simultaneously. We show that the investment strategies obtained using a scalarization parameter that is inversely proportional to wealth, which enjoys widespread popularity in the literature applying MV optimization in institutional settings, can exhibit some undesirable and impractical characteristics.

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