Abstract
We investigate an iterative, data-driven approximation to a problem where the investor seeks to maximize the expected return of her portfolio subject to a quantile constraint, given historical realizations of the stock returns. The approach, which was developed independently from Calafiore (SIAM J Optim 20:3427–3464 2010) but uses a similar idea, involves solving a series of linear programming problems and thus can be solved quickly for problems of large scale. We compare its performance to that of methods commonly used in the finance literature, such as fitting a Gaussian distribution to the returns (Keisler, Decision Anal 1:177–189 2004; Rachev et al. Advanced stochastic models, risk assessment and portfolio optimization: the ideal risk, uncertainty and performance measures, Wiley, New York 2008). We also analyze the resulting efficient frontier and extend our approach to the case where portfolio risk is measured by the inter-quartile range of its return. Our main contribution is in the detail of the implementation, i.e., the choice of the constraints to be generated in the master problem, as well as the numerical simulations and empirical tests, and the application to the inter-quartile range as a risk measure.
Similar content being viewed by others
References
Artzner P, Delbaen F, Eber JM, Health D (1999) Coherent measures of risk. Math Finance 9(3):203–228
Beasley J (2013) Portfolio optimization: models and solution approaches. Tutor Oper Res 1:201–221
Benati S, Rizzi R (2007) A mixed integer linear programming formulation of the optimal mean/value-at-risk portfolio problem. Eur J Oper Res 176:423–434
Benninga S, Wiener Z (1998) Value-at-risk (var). Math Educ Res 7(4)
Calafiore GC (2010) Random convex programs. SIAM J Optim 20(6):3427–3464
Calafiore GC (2013) Direct data-driven portfolio optimization with guaranteed shortfall probability. Automatica 49:370–380
Calafiore GC, Monastero B (2012) Data-driven asset allocation with guaranteed short-fall probability. IEEE American Control Conference, pp 3687–3692
Cetinkaya E (2014) Essays in robust and data-driven risk management. Ph.D. thesis, Lehigh University, Bethlehem
Colombo M (2007) Advances in interior point methods for large scale linear programming. Ph.D. thesis, Doctor of Philosophy University of Edinburgh
Cornuejols G, Tutuncu R (2007) Optimization methods in finance. Cambridge University Press, New York
Dentcheva D, Ruszczynski A (2006) Portfolio optimization with stochastic dominance constraints. J Bank Finance 30(2):433–451
El-Ghaoui L, Oks L, Oustry F (2000) Worst-case value-at-risk and robust asset allocation: a semidefinite programming approach. Tech. Rep. M00/59, University of California, Berkeley
Fabozzi F, Kolm P, Pachamanova D, Focardi S (2007) Robust portfolio optimization and management. Wiley, New York
Fenton LF (1969) The sum of lognormal probability distributions in scatter transmission systems. IRE Trans Commun Syst CS 8(3):57–67
Gaivoronski A, Pflug G (2005) Value-at-risk in portfolio optimization: properties and computational approach. J Risk 7(2):1–31
Goh J, Lim K, Sim M, Zhang W (2012) Portfolio value-at-risk optimization for asymmetrically distributed asset returns. Eur J Oper Res 221(2):397–406
Harlow WV (1991) Asset allocation in a downside risk framework. Financial Anal J 47(5):28–40
Keisler J (2004) Value of information in portfolio decision analysis. Decision Anal 1(3):177–189
Kim JH, Powell WB (2011) Quantile optimization for heavy-tailed distribution using asymmetric signum functions. Princeton University
Larsen N, Mausser H, Uryasev S (2002) Algorithms for optimization of value-at-risk. In: Pardalos P, Tsitsiringos VK (eds) Financial Engineering, E-Commerce and Supply Chain. Applied Optimization, vol 126. Springer, pp 19–46
Linsmeier TJ, Pearson ND (2000) Value at risk. Financial Anal J 56(2):47–67
Lobo M, Fazel M, Boyd S (2006) Portfolio optimization with linear and fixed transaction costs. Ann Oper Res 152(5)
Markovitz HM (1952) Portfolio selection. J Finance 7(1):77–91
Markovitz HM (1959) Portfolio selection. Wiley, New York
Naumov AV, Kibzun AI (1992) Quantile optimization techniques with application to chance constrained problem for water-supply system design. Tech. Rep. 92–5, Department of Industrial and Operations Engineering at University of Michigan and Department of Applied Mathematics Moscow Aviation Institute, Ann Arbor. Mi 48109 and Moskiw, 127080, Russia
Oyama T (2007) Determinants of stock prices: the case of zimbabwe. A Working Paper of the International Monetary Fund
Pankov AR, Platonov EN, Semenikhin KV (2002) Minimax optimization of investment portfolio by quantile criterion. Autom Remote control 64(7):1122–1137
Pfaff B (2013) Financial risk modeling and portfolio optimization with R. Wiley, New York
Rachev S, Stoyanov S, Fabozzi F (2008) Advanced stochastic models, risk assessment and portfolio optimization: the ideal risk, uncertainty and performance measures. Wiley, New York
Rockafellar RT, Uryasev S (2000) Optimization of conditional value at risk. J Risk 2(3):21–41
Rodriguez GJL (1999) Portfolio optimization with quantile-based risk measures. Ph.D. thesis, Massachusetts Institute of Technology, Massachusetts
Roy A (1952) Safety first and the holding of assets. Econometrica 20(3):431–449
Ruszczynski A, Vanderbei R (2003) Frontiers of stochastically nondominated portfolios. Econometrica 71(4):1287–1297
Sharpe WF (1966) Mutual fund performance. J Bus 39:119–138
Sharpe WF (1971) Mean absolute deviation characteristic lines for securities and portfolios. Manag Sci 18(2):B1–B13
Sortino FA, Price LN (1994) Performance measurement in downside risk framework. J Invest 3:59–64
Szegö G (2002) Measures of risk. J Bank Finance 26:1253–1272
Uryasev S (ed) (2000) Probabilistic constrained optimization: methodology and applications. Springer, New York
Wozabal D (2012) Value-at-risk optimization using the difference of convex algorithm. OR Spectrum 34:681–683
Yitzhaki S (1982) Stochastic dominance, mean variance, and gini’s mean difference. Am Econ Assoc 72(1):178–185
Zymler S, Kuhn D, Rustem B (2013) Worst-case value-at-risk of nonlinear portfolios. Manag Sci 59(1):172–188
Acknowledgments
We would like to thank to the audience of our talk at the 21st International Symposium on Mathematical Programming (ISMP 2012) in Berlin, Germany, and two anonymous reviewers for their insightful comments that have substantially improved the clarity of the paper as well as its positioning with respect to existing literature.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was done while the author was a doctoral student at Lehigh University and was not funded by Amazon.com.
Appendix 1
Appendix 1
Denote \(e ^{R_i^t}\) the return of stock \(i\) during time period \(t\). Then return of stock \(i\) from time 1 to time \(T\) is \(e^ {\sum _{t=1}^T R_i^t}\). Therefore, the portfolio return over \(T\) period can be formulated as:
Then, the first and the second moments of the portfolio return are calculated as:
We define the vector \(b \in \mathcal {R}^{n} \) such that
and the matrix A \( \in \mathcal {R}^{n x n} \) such that
The Log-Normal approximation of the portfolio return is represented as \(e ^{Y}\) where \( Y \sim N(\mu ^*, \sigma ^*)\). Then the following equations hold:
The solution of this system of equations is as follows:
Then, the expected return maximization problem with quantile constraint is written as:
which is equivalent to
Rights and permissions
About this article
Cite this article
Çetinkaya, E., Thiele, A. Data-driven portfolio management with quantile constraints. OR Spectrum 37, 761–786 (2015). https://doi.org/10.1007/s00291-015-0396-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00291-015-0396-9