Artikel
Bayesian nonparametric measurement of factor betas and clustering with application to hedge fund returns
We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year) than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 1 ; Pages: 1-23 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
- Thema
-
nonparametric clustering
Bayesian
cluster
nonparametric alpha and beta
hedge fund performance
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Garay, Urbi
ter Horst, Enrique
Molina, German
Rodriguez, Abel
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2016
- DOI
-
doi:10.3390/econometrics4010013
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Garay, Urbi
- ter Horst, Enrique
- Molina, German
- Rodriguez, Abel
- MDPI
Entstanden
- 2016
Ähnliche Objekte (12)
Bayesian inference for hedge funds with stable distribution of returns
Adaptive Nonparametric Clustering
Adaptive nonparametric clustering
Risk, return, and volatility feedback: A Bayesian nonparametric analysis
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Multiscale clustering of nonparametric regression curves
Recovering delisting returns of hedge funds
Bayesian clustering for row effects models
Bayesian nonparametric inference for queueing systems
Bayesian inference for hedge funds with stable distribution of returns
Adaptive Nonparametric Clustering
Adaptive nonparametric clustering
Risk, return, and volatility feedback: A Bayesian nonparametric analysis
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Multiscale clustering of nonparametric regression curves
Recovering delisting returns of hedge funds
Bayesian clustering for row effects models
Bayesian nonparametric inference for queueing systems
Bayesian inference for hedge funds with stable distribution of returns
Adaptive Nonparametric Clustering
Adaptive nonparametric clustering
Risk, return, and volatility feedback: A Bayesian nonparametric analysis
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Multiscale clustering of nonparametric regression curves
Recovering delisting returns of hedge funds
Bayesian clustering for row effects models