A Nodewise Regression Approach to Estimating Large Portfolios

Callot, Laurent; Caner, Mehmet; Önder, A. Özlem; Ulaşan, Esra

doi:10.1080/07350015.2019.1683018

Cited by 23 publications

(22 citation statements)

References 34 publications

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“…Next, we impose an additional assumption to control the sparsity of the precision matrix (see, Callot et al (2021)). Denote with S i := j such that k ij = 0 to be the set of nonzero parameter estimates of the precision matrix for the i−th row, k i , and denote with s i := |S i | to be its cardinality (the number of elements of the particular set).…”

Section: Remarkmentioning

confidence: 99%

“…In this Section we examine the mechanism of optimal portfolio choice under the assumption of network tail dependence. Related studies to tail dependence measures and assumptions are Escanciano and Hualde (2019), Zhang (2021) while Callot et al (2019) consider the portfolio choice problem. Firstly, we examine the relationship between tail connectivity and stock centrality, focusing on two aspects: (i) relation between stock centrality and portfolio risk;…”

Section: Portfolio Choice Under Tail Dependence In Graphsmentioning

confidence: 99%

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Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Katsouris¹

2021

Preprint

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We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise ∆CoVaR measures reflecting tail connections between assets. First, we derive the conditions under which the associated quadratic risk function has a closed-form solution. Second, we examine the relationship between portfolio risk and eigenvector centrality. Third, we show that portfolio risk is not necessarily increasing with respect to stock centrality. Forth, we demonstrate under certain conditions that asset centrality increases the optimal weight allocation of the asset to the portfolio. Overall, our empirical study indicates that a network topology which exhibits low connectivity is outperformed by high connectivity based on a Sharpe ratio test.

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Section: Remarkmentioning

confidence: 99%

Section: Portfolio Choice Under Tail Dependence In Graphsmentioning

confidence: 99%

Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Katsouris¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A further promising strategy is to directly estimate the inverse of the covariance matrix, rather than the covariance matrix itself. In particular, Callot et al (2019) present a sparse estimation technique that allows for consistent estimation of the GMVP portfolio weights and the Markowitz (1952) portfolio weights. By definition, the static approach aims for the long-run (or unconditional) vector of portfolio weights.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Global Minimum Variance Portfolio

Reh

Krüger

Liesenfeld

2022

Journal of Business & Economic Statistics

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We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the optimal GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns' conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations combined with targeting towards nonlinear shrinkage estimates of the long-run GMVP weights ensure scalability with respect to the number of assets. An empirical analysis of daily and monthly financial returns shows that the proposed models perform well in-and out-of-sample in comparison to existing approaches.

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“…On this basis, Fan et al (2012) and Li (2015) show that constraining portfolio norms amounts to constraining estimation risks. Further, Callot et al (2020) use a nodewise regression approach to estimate the inverse covariance matrix directly.…”

Section: Introductionmentioning

confidence: 99%

Risk Reduction and Efficiency Increase in Large Portfolios: Leverage and Shrinkage

2019

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We investigate the effects of constraining leverage and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller vs. larger universe of stocks, alternative portfolio formation objectives (Global Minimum Variance vs. exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously-chosen shrinkage method always outperforms an arbitrarily-determined leverage constraint. By extending the mathematical connection between leverage and shrinkage from static to dynamic, we provide a new theoretical explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the DCC-NL estimator results in risk reduction and efficiency increase in large portfolios as long as a small amount of leverage is allowed, whereas tightening the leverage constraint often hurts a DCC-NL portfolio.

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A Nodewise Regression Approach to Estimating Large Portfolios

Cited by 23 publications

References 34 publications

Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

Predicting the Global Minimum Variance Portfolio

Risk Reduction and Efficiency Increase in Large Portfolios: Leverage and Shrinkage

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