Fund-of-Funds Construction by Statistical Multiple Testing Methods

Wolf, Michael; Wunderli, Dan

doi:10.2139/ssrn.1477859

Cited by 4 publications

(2 citation statements)

References 12 publications

(2 reference statements)

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“…Most of the research on FOF focuses on the selection of funds. Wolf and Wunderlin used multiple testing methods to select the fund, and found that the resulting FOF portfolios have attractive return properties compared to the 1/N portfolio [1]. Studies have shown that by selecting appropriate funds (skilful fund manager) and time can yield better returns than natural competitor.…”

Section: Introductionmentioning

confidence: 99%

Timing Ability in Fund of Mutual Funds Placements: Empirical Evidence from China

He¹

2022

Advances in Economics, Business and Management Research

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This paper examines Market timing ability in China's fund of mutual funds placements. We study the Treynor-Mauzy-Busse Model, which evaluate the market return timing and volatility timing at the same time. We use China's fund of funds data and compare empirical results of Treynor-Mauzy Model, Henriksson-Merton Model, Busse Model, and Treynor-Mauzy-Busse model. We find that most China's fund of funds have significant volatility timing ability, but there is not evidence to prove that they have the ability of market timing. The empirical results are consistent with those of previous models.

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Section: Introductionmentioning

confidence: 99%

Timing Ability in Fund of Mutual Funds Placements: Empirical Evidence from China

He¹

2022

Advances in Economics, Business and Management Research

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“…Therefore, the generic shrinkage target seems less appropriate for a covariance matrix of a universe of stock returns. The target (3.5) was first suggested by Ledoit (1995, Appendix B.1), then was also proposed by Schäfer and Strimmer (2005) in a different context related to genomics, and has been used by Wolf and Wunderli (2012) to estimate the covariance matrix of a universe of hedge fund returns.…”

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confidence: 99%

The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation

Ledoit

Wolf

2020

Journal of Financial Econometrics

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Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz’s portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance matrix certainly will not do. In this article, we review our work in this area, going back 15+ years. We have promoted various shrinkage estimators, which can be classified into linear and nonlinear. Linear shrinkage is simpler to understand, to derive, and to implement. But nonlinear shrinkage can deliver another level of performance improvement, especially if overlaid with stylized facts such as time-varying co-volatility or factor models.

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Bootstrap Analysis for Asian REIT’s Portfolios

Caicedo-Llano

Kurtbegu²

2014

Handbook of Asian Finance

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Fund-of-Funds Construction by Statistical Multiple Testing Methods

Cited by 4 publications

References 12 publications

Timing Ability in Fund of Mutual Funds Placements: Empirical Evidence from China

Timing Ability in Fund of Mutual Funds Placements: Empirical Evidence from China

The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation

Bootstrap Analysis for Asian REIT’s Portfolios

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