Fund-of-Funds Construction by Statistical Multiple Testing Methods

Wolf, Michael; Wunderli, Dan

doi:10.1093/oxfordhb/9780199553433.013.0007

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Remark 2.3. Already Schäfer and Strimmer (2005) mentioned a common '(co)variance' shrinkage target and Wolf and Wunderli (2012) used a two-parameter shrinkage target for the estimation of the covariance matrix for fund-of-funds managers. However, no derivation and intuition behind the method was presented as well as no application study for portfolio selection is known.…”

Section: Constant-variance-covariance Shrinkage Estimatormentioning

confidence: 99%

Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage

Nard

2020

Journal of Financial Econometrics

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Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator, we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small- and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator, the application is not restricted to financial problems.

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Section: Constant-variance-covariance Shrinkage Estimatormentioning

confidence: 99%