Minimum-Variance Portfolios in the U.S. Equity Market

Clarke, Roger; Silva, Harindra de; Thorley, Steven

doi:10.3905/jpm.2006.661366

Cited by 350 publications

(135 citation statements)

References 14 publications

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“…Refs. [6,9,10,24,25,22,11,7,13,27,28,8]. Whereas all these papers confirm that the lowvol anomaly is strong and pervasive in stock markets, the origin of the effect is still debated.…”

Section: Introductionmentioning

confidence: 91%

Deconstructing the Low-Vol Anomaly

Beveratos¹,

Bouchaud²,

Ciliberti³

et al. 2017

JPM

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We study several aspects of the so-called low-vol and low-β anomalies, some already documented (such as the universality of the effect over different geographical zones), others hitherto not clearly discussed in the literature. Our most significant message is that the low-vol anomaly is the result of two independent effects. One is the striking negative correlation between past realized volatility and dividend yield. Second is the fact that ex-dividend returns themselves are weakly dependent on the volatility level, leading to better risk-adjusted returns for low-vol stocks. This effect is further amplified by compounding. We find that the low-vol strategy is not associated to short term reversals, nor does it qualify as a Risk-Premium strategy, since its overall skewness is slightly positive. For practical purposes, the strong dividend bias and the resulting correlation with other valuation metrics (such as Earnings to Price or Book to Price) does make the low-vol strategies to some extent redundant, at least for equities.

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“…Refs. [6,9,10,24,25,22,11,7,13,27,28,8]. Whereas all these papers confirm that the lowvol anomaly is strong and pervasive in stock markets, the origin of the effect is still debated.…”

Section: Introductionmentioning

confidence: 91%

Deconstructing the Low-Vol Anomaly

Beveratos¹,

Bouchaud²,

Ciliberti³

et al. 2017

JPM

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“…Haugen and Baker (1991) and Clarke et al (2006) investigated the MV portfolio and showed that the cumulative excess returns of MV portfolios in the U.S. stock market have been slightly higher than those of the market over the previous 42 years. Scherer (2011) showed that an MV portfolio tends to hold a low beta and low residual-risk stocks.…”

Section: The Minimum Variance Portfoliomentioning

confidence: 99%

Risk-Based Portfolios with Large Dynamic Covariance Matrices

Nakagawa

Imamura

Yoshida

2018

IJFS

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Abstract:In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios rather than on mean-variance portfolios. Risk-based portfolios are constructed based solely on covariance matrices, and include methods such as minimum variance (MV), risk parity (RP), and maximum diversification (MD). It is well known that the performance of a mean-variance portfolio depends on the accuracy of the estimations of the inputs. However, no studies have examined the relationship between the performance of risk-based portfolios and the estimated accuracy of covariance matrices. In this research, we compare the performance of risk-based portfolios for several estimation methods of covariance matrices in the Japanese stock market. In addition, we propose a highly accurate estimation method called cDCC-NLS, which incorporates nonlinear shrinkage into the cDCC-GARCH model. The results confirm that (1) the cDCC-NLS method shows the best estimation accuracy, (2) the RP and MD do not depend on the estimation accuracy of the covariance matrix, and (3) the MV does depend on the estimation accuracy of the covariance matrix.

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“…Under this seemingly stark assumption, the optimal portfolio is the minimum-variance portfolio. Using historical backtests, Haugen and Baker (1991) and Clarke, de Silva, and Thorley (2006) demonstrated that minimum-variance strategies improve upon their cap-weighted counterparts by supplying better returns with reduced volatility. Note that a minimum-variance portfolio is meanvariance optimal only if stocks are assumed to have the same expected returns; although the minimumvariance portfolio is unlikely to be mean-variance optimal, outperformance against standard capweighted indices is certainly possible without mean-variance optimality if the cap-weighted indices are not on the efficient frontier.…”

confidence: 99%

“…We calculated the shrinkage target and intensity as defined in Appendix A of Clarke, de Silva, and Thorley (2006); in their study of minimum-variance portfolios, they used a shrinkage target that had a smaller degree of freedom than that of Ledoit and Wolf (2004). 21.…”

Section: Notesmentioning

confidence: 99%