Attribution of hedge fund returns using a Kalman filter

Thomson, Daniel; Vuuren, Gary van

doi:10.1080/00036846.2017.1349290

Cited by 5 publications

(9 citation statements)

References 22 publications

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“…The CAPM , β despite its use as a benchmark measurement of firm risk by both internal managers and shareholders, has been shown to be a poor predictor of firm share price movements (Thomson & van Vuuren, 2018). Managers and shareholders indicate biased assessments of the risks a firm faces when undergoing M&As.…”

Section: Results Of Implied Volatility Testmentioning

confidence: 99%

“…Most estimates of the CAPM β rely on linear regression, but its robustness has been questioned(Thomson & van Vuuren, 2018).The Kalman filter is a recursive procedure for computing the optimal estimator of the state vector at time 1, + t based on information available at time t (Kalman, 1960), which provides a linear estimation method for equations represented in a state space form. Output is generated from measurement and transition equations, which depend on the form of stochastic process that the time-varying α s and β s are assumed to follow.…”

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confidence: 99%

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Applied prospect theory: assessing the βs of M&A-intensive firms

Homan

Vuuren

2019

Investment Management and Financial Innovations

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Behavioral components of Kahneman and Tversky’s (1979) prospect theory (PT) were applied to derive an adjusted Capital Asset Pricing Model (CAPM) in the estimation of merger and acquisition-intensive firms’ expected returns. The premise was that the CAPM – rooted in expected utility theory – is violated by the behavioral biases identified in prospect theory. Kahneman and Tversky’s prospect theory (1979) has demonstrated that weaknesses abound in the viability of classical utility theory predictions. For mergers and acquisitions, firms appear to be isolated from and immune to human error, yet decisions which involve the undertaking of capital-intensive projects are delegated to senior management. These individuals are prone to cognitive biases and personalized risk appetites that may (and often do) compromize attitudes and behavior when it comes to pricing risky ventures. Having established that beta estimates using linear regression are inferior, the CAPM was implemented utilizing beta estimates obtained from the Kalman filter. The results obtained were assessed for their long-term market price predictive accuracy. The authors test the reliability of the CAPM as a predictor of price, observe the rationality of human behavior in capital markets, and attempt to model premiums to adjust CAPM returns to a level that more appropriately accounts for firm specific risk. The researchers show that market participants behave irrationally when assessing M&amp;A firms’ specific risk. Logistic regression coupled with the development of a risk premium was implemented to correct the original Kalman filter returns and was tested for improvements in predictive power.

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Section: Results Of Implied Volatility Testmentioning

confidence: 99%

mentioning

confidence: 99%

Applied prospect theory: assessing the βs of M&A-intensive firms

Homan

Vuuren

2019

Investment Management and Financial Innovations

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“…Researchers are concerned about the asymmetric behavior of hedge funds depending on the phase of the business cycle (Holmes and Faff 2008 ; Jawadi and Khanniche 2012 ; Namvar et al 2016 ; Stafylas et al 2018 ). The Kalman filter is a tool used in a large number of studies (Thomson and van Vuuren 2018 ; Lambert and Platania 2020 ). 10 Other dynamic econometric techniques have also been used.…”

Section: Background Literaturementioning

confidence: 99%

“…Most studies rely on the Kalman filter to compute time-varying risk measures, which then become state (unobserved) variables that obey a standard random walk model with drift or to a more complex model including macroeconomic and financial variables (Holmes and Faff 2008 ; Thomson and van Vuuren 2018 ; Lambert and Platania 2020 ). 16 A more natural way to compute a time-varying beta is to rely on the Multivariate Generalized Autoregressive Conditional Heteroskedasticity process (MGARCH: Bollerslev et al 1988 ; DCC-MGARCH: Engle and Colacito 2006 ; Engle 2016 ), which is specifically designed to build the conditional (time-varying) covariances that are the components of the time-varying beta measure.…”

Section: Data and Stylized Factsmentioning

confidence: 99%

“…These options span various forms of market timing by portfolio managers (Glosten and Jagannathan 1994 ; Agarwal and Naik 2004 ; Stafylas et al 2017 ). The exposure of hedge-fund returns to risk factors, especially the market risk premium, has been made time-varying relatively recently, particularly since the subprime crisis (Holmes and Faff 2008 ; Billio et al 2012 ; Jawadi and Khanniche 2012 ; Bali et al 2014 ; Namvar et al 2016 ; Stafylas et al 2017 ; Racicot and Théoret 2016 ; Thomson and van Vuuren 2018 ; Lambert and Platania 2016 , 2020 ; Racicot et al 2021 ). The literature indicates that hedge funds monitor their risk exposure in order to take more risk in high regimes (i.e., expansions) and less risk in low regimes (i.e., recessions).…”

Section: Introductionmentioning

confidence: 99%

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Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach

Racicot

Théoret

2022

Financ Innov

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The subprime crisis was quite damaging for hedge funds. Using the local projection method (Jordà 2004, 2005, 2009), we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks—especially volatility and illiquidity shocks—over the subprime crisis in order to investigate their market timing activities. In a robustness check, using TVAR (Balke 2000), we simulate the reaction of hedge fund strategies’ betas in extreme scenarios allowing moderate and strong adverse shocks. Our results show that the behavior of hedge fund strategies regarding the monitoring of systematic risk is highly nonlinear in extreme scenarios—especially during the subprime crisis. We find that countercyclical strategies have an investment technology which differs from procyclical ones. During crises, the former seek to capture non-traditional risk premia by deliberately increasing their systematic risk while the later focus more on minimizing risk. Our results suggest that the hedge fund strategies’ betas respond more to illiquidity uncertainty than to illiquidity risk during crises. We find that illiquidity and VIX shocks are the major drivers of systemic risk in the hedge fund industry.

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