Minimum-Variance Portfolio Composition

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

doi:10.3905/jpm.2011.37.2.031

Cited by 171 publications

(48 citation statements)

References 13 publications

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“…The success of this strategy violates modern portfolio theory because it takes only the portfolio variance into account. But many empirical studies show that portfolios that focus on minimizing the volatility generate superior out-of-sample results (see, Clarke et al (2011Clarke et al ( , 2006, Jagannathan and Ma (2003), Ledoit and Wolf (2004) among others). That is why it makes sense to provide a statistical test whether the current portfolio composition is different from the conventional GMVP taking into account both the uncertainty of the asset returns and the large dimensionality of the portfolio.…”

Section: Introductionmentioning

confidence: 99%

Tests for the Weights of the Global Minimum Variance Portfolio in a High-Dimensional Setting

Bodnar

Dmytriv²,

Parolya³

et al. 2019

IEEE Trans. Signal Process.

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In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a highdimensional setting, namely, when the number of assets p depends on the sample size n such that p n → c ∈ (0, 1) as n tends to infinity. In the case of a singular covariance matrix with rank equal to q we assume that q/n →c ∈ (0, 1) as n → ∞. The considered tests are based on the sample estimator and on the shrinkage estimator of the GMVP weights. We derive the asymptotic distributions of the test statistics under the null and alternative hypotheses. Moreover, we provide a simulation study where the power functions and the receiver operating characteristic curves of the proposed tests are compared with other existing approaches. We observe that the test based on the shrinkage estimator performs well even for values of c close to one.

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Section: Introductionmentioning

confidence: 99%

Tests for the Weights of the Global Minimum Variance Portfolio in a High-Dimensional Setting

Bodnar

Dmytriv²,

Parolya³

et al. 2019

IEEE Trans. Signal Process.

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“…Others also documented characteristics of the minimum variance investing approach: Baker, Bradley and Wurgler (2011) measured and compared the minimum variance portfolio performance with characteristics of stocks sorted by volatility. Clarke, de Silva and Thorley (2011) wrote a paper about the minimum variance portfolio composition.…”

Section: Introductionmentioning

confidence: 99%

Minimum Variance Portfolios in the German Stock Market

Bastin¹

2017

Prague Economic Papers

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The text demonstrates out-of-sample performances of minimum variance portfolios in the German stock market in the period 2002-2015. Because of two huge drawdowns on equity markets in the period 2000-2010, scholars and professionals have tried to find an alternative to the marketcap weighted investing; potentially the minimum variance investing approach. The paper presents the construction of minimum variance portfolios, the description of their compositions and empirical risk-return characteristics under various holding periods. As anticipated, minimum variance portfolios have lower risk vis-à-vis the CDAX index, but they have also higher returns. Finally, minimum variance portfolios have better risk-adjusted performance figures in comparison with equal-weighted alternatives.

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“…where w are the weights of the assets in the portfolio, is the associated covariance matrix, and 2 ,w is the portfolio variance (for more detail, refer to Clarke, De Silva, & Thorley, 2011;Haugen & Baker, 1991).…”

Section: Definition 3 Global Minimum Variance Portfoliomentioning

confidence: 99%

Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

Ames

Bagnarosa

Peters

et al. 2018

Journal of Forecasting

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With the exception of naive methods for portfolio selection, such as the equal weighted approaches, all other methods of portfolio allocation are more or less sensitive to the quality of the inputs considered in constructing the models and risk measures utilized in the allocation framework. The extensively used factor model proposed initially by Sharpe in 1963 has provided a robust backdrop for development of relevant, micro, macro, and context‐specific or asset specific explanatory variables to be incorporated in a statistical manner as inputs to forecasting models that can then be used to obtain risk measures upon which portfolio allocations are based. However, like all statistical models, a set of statistical assumptions accompany this factor model regression framework, one of which has recently been highlighted as seemingly nonvalidated in financial data. This is, of course, the assumption such factor models make on homoskedasticity or weak‐sense covariance stationarity of the returns processes being modeled. Such factor models, therefore, have typically failed to cope with an important and ubiquitous feature of financial assets data, which often demonstrates heteroskedasticity of the returns variances and covariances. We propose a novel generalized multifactor forecasting structure utilizing a covariance regression model, which allows us to incorporate the required heteroskedasticity effects while also admitting potential dependence in the idiosyncratic error terms. We argue that such a modeling approach allows for more explicit relationships to be interpreted between the driving factors and the conditional responses of the portfolio returns. We then compare the forecasting performances of our model with the multifactor model and the time series dynamic conditional correlation model through a currency portfolio application.

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Minimum-Variance Portfolio Composition

Cited by 171 publications

References 13 publications

Tests for the Weights of the Global Minimum Variance Portfolio in a High-Dimensional Setting

Tests for the Weights of the Global Minimum Variance Portfolio in a High-Dimensional Setting

Minimum Variance Portfolios in the German Stock Market

Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

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