Journal article

Authors list: Sengupta, RN; Seth, R; Winker, P

Publication year: 2023

Pages: 199-233

Journal: Sankhya. Series B: Applied and Interdisciplinary Statistics

Volume number: 85

Issue number: SUPPL 1

ISSN: 0976-8386

eISSN: 0976-8394

DOI Link: https://doi.org/10.1007/s13571-022-00285-2

Publisher: Springer

Abstract: 
Portfolio optimization problems are rather easy to solve if one assumes normality of the (joint) distribution of returns with given parameters and a linear objective function, subject to some risk constraints. Higher degrees of complexities arise when taking into account (i) non- linear asset classes, (ii) non-normal distributions, (iii) constraints on lot size, portfolio shares etc., and (iv) uncertainty of parameter estimates. In this paper a Reliability-Based Design Optimization (RBDO) framework is developed to tackle the last two issues, namely, to solve portfolio optimization problems, taking into account the constraints on lot sizes, portfolio shares, etc., and the uncertainty of parameter estimates (i.e., return and risk). Thereby, standard methods and heuristics, available in the literature for handling reliability-based constraints, are adopted. The Block-Bootstrap method is used to obtain estimates of the uncertainty of parameter estimates. For an application to stocks which are components of the DAX, efficient portfolios are obtained taking into account the trade-off between optimal values of the objective function and the reliability of results.

Harvard Citation style: Sengupta, R., Seth, R. and Winker, P. (2023) Reliability in Portfolio Optimization using Uncertain Estimates, Sankhya. Series B: Applied and Interdisciplinary Statistics, 85(SUPPL 1), pp. 199-233. https://doi.org/10.1007/s13571-022-00285-2

APA Citation style: Sengupta, R., Seth, R., & Winker, P. (2023). Reliability in Portfolio Optimization using Uncertain Estimates. Sankhya. Series B: Applied and Interdisciplinary Statistics. 85(SUPPL 1), 199-233. https://doi.org/10.1007/s13571-022-00285-2