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Financial decision-making under dist...

Kacperczyk, Marcin.

Financial decision-making under distribution uncertainty.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Financial decision-making under distribution uncertainty.
作者:	Kacperczyk, Marcin.
面頁冊數:	114 p.
附註:	Co-Chairs: Paul Damien; Tyler Shumway.
附註:	Source: Dissertation Abstracts International, Volume: 65-06, Section: A, page: 2304.
Contained By:	Dissertation Abstracts International65-06A.
標題:	Economics, Finance.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3138188
ISBN:	0496853589

Financial decision-making under distribution uncertainty.
Kacperczyk, Marcin.

Financial decision-making under distribution uncertainty. - 114 p.

Co-Chairs: Paul Damien; Tyler Shumway.

Thesis (Ph.D.)--University of Michigan, 2004.

Investors do not know the distribution of future returns, but much of the finance literature assumes they do. This dissertation presents a novel approach which allows one to incorporate uncertainty about the type of return distribution (distribution uncertainty) and applies it to three different financial problems: asset allocation, return predictability, and option pricing. To analyze decisions under distribution uncertainty, I develop a new family of Bayesian nonparametric and semiparametric models. In such models, the nonparametric component is modeled using the class of Dirichlet processes, while the parametric component is modeled using mean-variance regression. I show that, in such a semiparametric framework, the predictive distribution for any decision variable can be obtained using a scale mixture of uniform distributions approach. Importantly, such predictive distribution accounts for both parameter- and distribution uncertainty. I find that, above and beyond parameter uncertainty, distribution uncertainty is highly time-varying. Compared to investors facing a standard parameter uncertainty, investors considered in this dissertation can benefit from accounting for distribution uncertainty when deciding about optimal asset allocation, or predicting option prices. Distribution uncertainty, however, does not help to improve a well-known weak evidence on short-term predictability from the dividend yield.

ISBN: 0496853589Subjects--Topical Terms:

212585
Economics, Finance.

Financial decision-making under distribution uncertainty.
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520 # $a Investors do not know the distribution of future returns, but much of the finance literature assumes they do. This dissertation presents a novel approach which allows one to incorporate uncertainty about the type of return distribution (distribution uncertainty) and applies it to three different financial problems: asset allocation, return predictability, and option pricing. To analyze decisions under distribution uncertainty, I develop a new family of Bayesian nonparametric and semiparametric models. In such models, the nonparametric component is modeled using the class of Dirichlet processes, while the parametric component is modeled using mean-variance regression. I show that, in such a semiparametric framework, the predictive distribution for any decision variable can be obtained using a scale mixture of uniform distributions approach. Importantly, such predictive distribution accounts for both parameter- and distribution uncertainty. I find that, above and beyond parameter uncertainty, distribution uncertainty is highly time-varying. Compared to investors facing a standard parameter uncertainty, investors considered in this dissertation can benefit from accounting for distribution uncertainty when deciding about optimal asset allocation, or predicting option prices. Distribution uncertainty, however, does not help to improve a well-known weak evidence on short-term predictability from the dividend yield.
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