Tracking Error and Active Portfolio Management

El‐Hassan, Nadima; Kofman, Paul

doi:10.1177/031289620302800204

Cited by 26 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Surprisingly, the TESD tends to be slightly increased when short-sales are allowed, which validates the empirical¯nding that unconstrained active portfolios \wander away" much from the benchmark. 2 The ex-post measurement of tracking error generally supports that the replication of FTSE/ATHEX 20 returns (at least within the limits posed on tracking error) is feasible by holding a subset of index member stocks. Interesting features of the active formulation are revealed when one examines the problem in the total risk-return space.…”

Section: Out-of-sample Resultsmentioning

confidence: 81%

Active Portfolio Management With Cardinality Constraints: An Application of Particle Swarm Optimization

Thomaidis

Angelidis

Vassiliadis

et al. 2009

New Math. and Nat. Computation

View full text Add to dashboard Cite

y v.vassiliadis@fme.aegean.gr z g.dounias@aegean.gr 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¯rst 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 e±ciency 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 * We are thankful to the two anonymous referees for their valuable comments and suggestions.New Mathematics and Natural Computation Vol. 5, No. 3 (2009) 535À555 # . c World Scienti¯c Publishing Company 535 New Math. and Nat. Computation 2009.05:535-555. Downloaded from www.worldscientific.com by UNIVERSITY OF QUEENSLAND on 08/15/15. For personal use only.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 di®erent 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 e±ciency of common practices in active portfolio management and the incorporation of cardinality constraints.

show abstract

Section: Out-of-sample Resultsmentioning

confidence: 81%

Active Portfolio Management With Cardinality Constraints: An Application of Particle Swarm Optimization

Thomaidis

Angelidis

Vassiliadis

et al. 2009

New Math. and Nat. Computation

View full text Add to dashboard Cite

show abstract

“…Analýzami vlivů vybraných faktorů se zabývali Walsh a kol. (1998), El-Hassan a Kofman (2003), Valecký (2008aValecký ( , 2008bValecký ( , 2008c, a další.…”

Section: úVodunclassified

Analýza a ověření kvality replikace benchmarku metodologií Tracking Error

Valecký¹

2011

ER-CEREI

View full text Add to dashboard Cite

The aim of the paper is to perform an analysis and compare the accuracy of a benchmark replication using various replication methods of Tracking Error methodology. On the historical data and under the existence of the proportional transactions costs, we verify the impact of stocks number in portfolio and transaction costs on the selected replication methods of passive and active asset management strategies, namely replication with daily and controlled restructuring and the method with penalization of transaction costs. We also determined which selected replication method is the most precise in the sense of benchmark replication and we give several general recommendations for benchmark replication strategy including the eligible proportion of replicating portfolio on the market capitalization with respect to the target replication accuracy. Firstly, the Tracking Error methodology and its application in asset management are presented and optimization problem of particular replication methods is formulated in the next part of the paper. In the application part, the replication accuracy is analyzed and the quality of benchmark replication is verified on the Czech index PX-GLOB during

show abstract

“…Various statistical techniques have been applied toward the objective of benchmark index replication ranging from time series clustering (Focardi and Fabozzi 2004) to cointegration (Dunis and Ho 2005). Many studies have also investigated the question of actively managing a portfolio that replicates the performance of a benchmark index subject to limits on the tracking error (Burmeister et al 2004;El-Hassan and Kofman 2003;Israelsen and Cogswell 2006). Corielli and Marcellino (2006) were among the first to introduce factor models in the analysis of the benchmark replicating problem.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Strategies to Track and Outperform a Benchmark

Glabadanidis

2020

JRFM

View full text Add to dashboard Cite

I investigate the question of how to construct a benchmark replicating portfolio consisting of a subset of the benchmark’s components. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns’ first and second moments. The first approach produces the standard hedge portfolio that has the maximum feasible correlation with the benchmark. The second approach produces weights that are proportional to a “signal-to-noise” ratio of factor beta to idiosyncratic volatility. Using a factor model of securities returns allows the use of a larger number of securities than the number of time periods used to estimate the parameters of the factor model. I also consider a second objective that maximizes expected returns subject to a target tracking error variance. The security selection criterion naturally extends to the product of the information ratio and the signal-to-noise ratio. The optimal tracking portfolio is either a one-fund or a two-fund portfolio rule consisting of the optimal hedging portfolio, the tangent portfolio or the global minimum variance portfolio, depending on what constraints are imposed on the objective function. I construct buy-and-hold replicating portfolios using the algorithms presented in the paper to track a widely followed stock index with very good results both in-sample and out-of-sample.

show abstract

Tracking Error and Active Portfolio Management

Cited by 26 publications

References 31 publications

Active Portfolio Management With Cardinality Constraints: An Application of Particle Swarm Optimization

Active Portfolio Management With Cardinality Constraints: An Application of Particle Swarm Optimization

Analýza a ověření kvality replikace benchmarku metodologií Tracking Error

Portfolio Strategies to Track and Outperform a Benchmark

Contact Info

Product

Resources

About