scholarly journals A Numerical Study for Robust Active Portfolio Management with Worst-Case Downside Risk Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Aifan Ling ◽  
Le Tang
Keyword(s):  
Portfolio Management ◽  
Numerical Study ◽  
Risk Measure ◽  
Tracking Error ◽  
Downside Risk ◽  
Worst Case ◽  
Active Portfolio Management ◽  
Multiple Weights ◽  
Real Market ◽  
Portfolio Selection Model

Recently, active portfolio management problems are paid close attention by many researchers due to the explosion of fund industries. We consider a numerical study of a robust active portfolio selection model with downside risk and multiple weights constraints in this paper. We compare the numerical performance of solutions with the classical mean-variance tracking error model and the naive1/Nportfolio strategy by real market data from China market and other markets. We find from the numerical results that the tested active models are more attractive and robust than the compared models.

Downside Risk Premium

2021 ◽  
pp. 138-147
Author(s):  
Kanellos Stylianou Toudas
Keyword(s):  
Portfolio Management ◽  
Risk Premium ◽  
Critical Reflection ◽  
Risk Measure ◽  
Linear Optimization ◽  
Downside Risk ◽  
Optimization Techniques ◽  
Portfolio Efficiency ◽  
Recent Developments ◽  
Safety First

The purpose of this chapter is to address the main developments and challenges on risk assessment and portfolio management. The former innovation in modern portfolio theory, Markowitz, has been succeeded from linear and non-linear optimization techniques that improve portfolio efficiency. Special emphasis is given on Roy's seminal work on “Safety First Criterion” which advocates that the safety of investments should be prioritized. Thus, an investment should be chosen in a way that it has the lowest probability of falling short of a required threshold of investors. This motivated Markowitz to advocate a downside risk measure based on semivariance. It captures the notion of risk as failure to meet some minimum target. It is influenced by returns below the target rate. It focuses on investors' concern with downside variability and loss reduction. This chapter offers a critical reflection of these recent developments and could be of interest for individual and institutional investors.

scholarly journals Taming Tail Risk: Regularized Multiple β Worst-Case CVaR Portfolio

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 922
Author(s):  
Kei Nakagawa ◽  
Katsuya Ito
Keyword(s):  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Downside Risk ◽  
Superior Performance ◽  
Conditional Value At Risk ◽  
Worst Case ◽  
Tail Risk ◽  
Promising Alternative ◽  
Investment Process

The importance of proper tail risk management is a crucial component of the investment process and conditional Value at Risk (CVaR) is often used as a tail risk measure. CVaR is the asymmetric risk measure that controls and manages the downside risk of a portfolio while symmetric risk measures such as variance consider both upside and downside risk. In fact, minimum CVaR portfolio is a promising alternative to traditional mean-variance optimization. However, there are three major challenges in the minimum CVaR portfolio. Firstly, when using CVaR as a risk measure, we need to determine the distribution of asset returns, but it is difficult to actually grasp the distribution; therefore, we need to invest in a situation where the distribution is uncertain. Secondly, the minimum CVaR portfolio is formulated with a single β and may output significantly different portfolios depending on the β. Finally, most portfolio allocation strategies do not account for transaction costs incurred by each rebalancing of the portfolio. In order to improve these challenges, we propose a Regularized Multiple β Worst-case CVaR (RM-WCVaR) portfolio. The characteristics of this portfolio are as follows: it makes CVaR robust with worst-case CVaR which is still an asymmetric risk measure, it is stable among multiple β, and against changes in weights over time. We perform experiments on well-known benchmarks to evaluate the proposed portfolio.RM-WCVaR demonstrates superior performance of having both higher risk-adjusted returns and lower maximum drawdown.

ACTIVE PORTFOLIO MANAGEMENT WITH CARDINALITY CONSTRAINTS: AN APPLICATION OF PARTICLE SWARM OPTIMIZATION

2009 ◽  
Vol 05 (03) ◽  
pp. 535-555 ◽  
Author(s):  
NIKOS S. THOMAIDIS ◽  
TIMOTHEOS ANGELIDIS ◽  
VASSILIOS VASSILIADIS ◽  
GEORGIOS DOUNIAS
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Tracking Error ◽  
Particle Swarm ◽  
Solution Space ◽  
Upper And Lower Bounds ◽  
Cardinality Constraints ◽  
Swarm Optimization ◽  
Computational Point ◽  
Active Portfolio Management

This paper considers the task of forming a portfolio of assets that outperforms a benchmark index, while imposing a constraint on the tracking error volatility. We examine three alternative formulations of active portfolio management. The first one is a typical setup in which the fund manager myopically maximizes excess return. The second formulation is an attempt to set a limit on the total risk exposure of the portfolio by adding a constraint that forces a priori the risk of the portfolio to be equal to the benchmark's. In this paper, we also propose a third formulation that directly maximizes the efficiency of active portfolios, while setting a limit on the maximum tracking error variance. In determining optimal active portfolios, we incorporate additional constraints on the optimization problem, such as a limit on the maximum number of assets included in the portfolio (i.e. the cardinality of the portfolio) as well as upper and lower bounds on asset weights. From a computational point of view, the incorporation of these complex, though realistic, constraints becomes a challenge for traditional numerical optimization methods, especially when one has to assemble a portfolio from a big universe of assets. To deal properly with the complexity and the "roughness" of the solution space, we use particle swarm optimization, a population-based evolutionary technique. As an empirical application of the methodology, we select portfolios of different cardinality that actively reproduce the performance of the FTSE/ATHEX 20 Index of the Athens Stock Exchange. Our empirical study reveals important results concerning the efficiency of common practices in active portfolio management and the incorporation of cardinality constraints.

Tracking Error and Active Portfolio Management

2003 ◽  
Vol 28 (2) ◽  
pp. 183-207 ◽  
Author(s):  
Nadima El-Hassan ◽  
Paul Kofman
Keyword(s):  
Portfolio Management ◽  
Tracking Error ◽  
Active Portfolio Management
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