High dimensional covariance matrix estimation using a factor model

Fan, Jianqing; Fan, Yingying; Lv, Jinchi

doi:10.1016/j.jeconom.2008.09.017

Cited by 589 publications

(455 citation statements)

References 45 publications

(53 reference statements)

Supporting

Mentioning

447

Contrasting

Unclassified

Order By: Relevance

“…Multifactor models represent stock correlations using a small number of factors and thus significantly reduce the number of estimated parameters. Fan, Fan, and Lv (2008) showed that applying a factor structure to covariance matrix estimation is particularly advantageous when the covariance matrix is used for portfolio optimization.…”

Section: Cfapubsorg First Quarter 2018mentioning

confidence: 99%

Taxes, Shorting, and Active Management

Sialm

SosnerNathan²

2018

Financial Analysts Journal

View full text Add to dashboard Cite

In our study, we analyzed the consequences of short selling for quantitative investment strategies held by individual investors in taxable accounts. The conventional wisdom is that the tax burden of an investment strategy increases with its turnover, because high-turnover strategies exhibit a higher propensity to realize capital gains. 1 Furthermore, short selling is often perceived to be particularly tax-inefficient because the realized capital gains on short positions are generally taxed at the higher short-term capital gains tax rate, regardless of the holding period. In this article, we show that, contrary to conventional wisdom, investment strategies that take advantage of short selling can generate relatively low tax burdens. Indeed, the addition of short positions probably enhances rather than diminishes the tax efficiency of quantitative investment strategies.One well-accepted method for managing the tax burden for individuals is to optimally time the realization of capital gains and losses, as first discussed in Constantinides (1983Constantinides ( , 1984. Investors can reduce their tax burden by accelerating capital losses and deferring capital gains. Deferring capital gains is beneficial under current law for several reasons: Long-term capital gains are taxed at a lower rate, the payment of taxes can be deferred, and the taxation of capital gains can be completely avoided when the assets pass through an estate (owing to the step-up of the cost basis at death) or are gifted to a charity. In contrast, it is advantageous to accelerate the realization of capital losses, which can be used to offset current or future capital gains in an overall portfolio. Realizing short-term losses is particularly beneficial because the short-term capital gains taxWe examine the consequences of short selling in the context of quantitative investment strategies held by individual investors in taxable accounts. Short positions not only allow investors to benefit from the anticipated underperformance of securities but also create tax benefits because they enhance opportunities to time capital gains realizations. Relaxing short-selling constraints results in tax benefits because a portfolio's long positions tend to realize net long-term capital gains taxed at relatively low rates, whereas short positions tend to realize net short-term capital losses, which can offset short-term capital gains from other strategies in the investor's portfolio. Our results show that investment strategies that take advantage of short selling can generate superior after-tax performance by significantly reducing the tax burden. Disclosures:The views expressed in this article are those of the authors and do not necessarily reflect the views of AQR Capital Management, LLC. Further information can be found at the end of this article.We are grateful for comments and suggestions by Stephen J. Brown (executive editor), Anthony Comprelli, CFA, George Constantinides, Jacques Friedman, Luis GarciaFeijóo, CFA, CIPM, Bryan Johnson, CFA, Hoon Kim, CFA, Mark Mc...

show abstract

Section: Cfapubsorg First Quarter 2018mentioning

confidence: 99%

Taxes, Shorting, and Active Management

Sialm

SosnerNathan²

2018

Financial Analysts Journal

View full text Add to dashboard Cite

show abstract

“…[7], [16]). In the financial domain, Fan et al [10] involve 3-factor model in the estimation of a covariance matrix. They find the factor model improves the estimation of the precision matrix, but affects errors in the estimation of the covariance matrix less.…”

Section: Introductionmentioning

confidence: 99%

Robust Portfolio Risk Minimization Using the Graphical Lasso

Millington

Niranjan

2017

Neural Information Processing

View full text Add to dashboard Cite

Abstract. We apply the statistical technique of graphical lasso for inverse covariance estimation of asset price returns in Markowitz portfolio optimisation. Graphical lasso induces sparsity in the inverse covariance matrix, thereby capturing conditional independences between different assets. We show empirical results that not only the resulting minimum risk portfolio is robust, in that the variation in expected returns is reduced when a fraction of the data is assumed missing, but also enables the construction of a financial network in which groups of assets belonging to the same financial sector are linked.

show abstract

“…There are several papers recently proposed in the literature to tackle this problem as, for example, Bickel and Levina (2008), Levina et al (2008), Fan et al (2008), Wang and Zou (2010), Bai and Shi (2011), Fan et al (2011), Fan et al (2012), Fan et al (2012a), Hautsch et al (2009), Fan et al (2013), and Lunde et al (2013) among many others. Our goal is to build an econometric methodology which will be used to construct dynamic models to forecast large covariance matrices estimated elsewhere.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice

2014

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ANDERS B. KOCKAarhus University, CREATES, Denmark. MARCELO C. MEDEIROSDepartment of Economics, Pontifical Catholic University of Rio de Janeiro, Brazil.Abstract. In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong theoretical performance guarantees on the forecast capability of our procedure. To be precise, we show that we can forecast future realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics for the realized covariance matrices of the 30 Dow Jones stocks and find that these dynamics are not stable as the data is aggregated from the daily to the weekly and monthly frequency. The theoretical performance guarantees on our forecasts are illustrated on the Dow Jones index. In particular, we can beat our benchmark by a wide margin at the longer forecast horizons. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts.

show abstract

High dimensional covariance matrix estimation using a factor model

Cited by 589 publications

References 45 publications

Taxes, Shorting, and Active Management

Taxes, Shorting, and Active Management

Robust Portfolio Risk Minimization Using the Graphical Lasso

Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice

Contact Info

Product

Resources

About