Dynamic Hedge Fund Style Analysis With Errors‐in‐variables

Bodson, Laurent; Coën, Alain; Hübner, Georges

doi:10.1111/j.1475-6803.2010.01268.x

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…The study employs a return-based style analysis (RBSA) developed by Sharpe (1988Sharpe ( , 1992 to assess the asset allocation strategies of managed and superannuation fund managers. Recent examples that employ RBSA include Domian and Reichenstein (2009), Bodson, Coen and Hubner (2010) and Xiong, Ibbotson, Idzorek and Chen (2010). While there are other approaches to examine the fund's investment style, such as the portfolio-composition-based approach and data envelopment analysis (Alexakis and Tsolas, 2011), the RBSA approach works better with multi-sector funds, which invest across a number of asset classes.…”

Section: Introductionmentioning

confidence: 99%

Return-based Style Analysis in Australian Funds

Faff

Gharghori²,

Ip³

et al. 2012

MFJ

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This study applies return-based style analysis to a sample of Australian managed and superannuation funds, seeking to compare their asset allocation strategies across different style groups. Style analysis is performed using a rolling window estimation technique. As expected, riskier fund classes are more exposed to the riskier benchmarks. Further, differences in institutional and legal settings lead the managers of managed and superannuation funds to invest differently, with the latter employing a more conservative investment strategy despite having longer investment horizons.

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Section: Introductionmentioning

confidence: 99%

Return-based Style Analysis in Australian Funds

Faff

Gharghori²,

Ip³

et al. 2012

MFJ

View full text Add to dashboard Cite

show abstract

“…The second component contains the contribution of alternative risk factors such as asset-based style factors which reflect the risk premia associated with different trading strategies (Fung and Hsieh 2002). In fact, a number of authors note that hedge funds' effective trading strategies are highly dynamic (Fung and Hsieh 1997;Brown and Goetzmann 2003;Bodson et al 2010). As a result, hedge funds often absorb 'trading-related' risks in financial markets such as liquidity and volatility risk.…”

Section: Hedge Funds As An Alternative Asset Classmentioning

confidence: 98%

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Bessler

Holler

Kurmann

2012

Financ Mark Portf Manag

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This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds' ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.

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“…When shift in manager style is assumed to occur continuously and gradually over time, such smoothing time variation can be captured by "time-varying parameter models" such as state-space models usually estimated by the Kalman filter method. Several studies including Swinkels and Van Der Sluis (2006), Bodson et al (2010), Darolles and Vaissie (2012), and Marques et al (2012) proposed such modeling methods to incorporate smoothing time variation in style analysis. Although these studies opened a path toward a more general framework for style analysis, their methodology is not fully general as their time-varying models can only be applicable to either the weak style analysis or semi-strong style analysis according to the terminology of DeRoon et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Testing for structural breaks in return-based style regression models

Kim

Stone²,

Kim

2020

Financ Mark Portf Manag

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It is important for investors to know not only the style of a fund manager in which they are interested, but also whether this style is constant or changing through time. The style of a fund manager can be estimated by the so-called style regression, and a great deal of research has been carried out to investigate the statistical properties of style regression methods. However, there has been no formal and statistically valid method to test for a change in manager style when the two typically imposed restrictions (sum-to-one and non-negativity) are jointly present in style analysis. In this study, we apply and extend the results of Andrews (Econometrica 61:821-856, 1993; Estimation when a parameter is on a boundary: theory and application, Yale University, 1997a; A simple counterexample to the bootstrap,

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Dynamic Hedge Fund Style Analysis With Errors‐in‐variables

Cited by 15 publications

References 37 publications

Return-based Style Analysis in Australian Funds

Return-based Style Analysis in Australian Funds

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Testing for structural breaks in return-based style regression models

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