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Dynamic Asset Allocation Using Adaptive Particle Filters.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic Asset Allocation Using Adaptive Particle Filters./
Author:	Xu, Li.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2013,
Description:	66 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: A.
Contained By:	Dissertations Abstracts International82-04A.
Subject:	Business administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28122348
ISBN:	9798662528994

Dynamic Asset Allocation Using Adaptive Particle Filters.
Xu, Li.

Dynamic Asset Allocation Using Adaptive Particle Filters. - Ann Arbor : ProQuest Dissertations & Theses, 2013 - 66 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2013.

This item must not be sold to any third party vendors.

Asset allocation is one of the most important problems in practical investment management and also plays a central role in financial economics. This study is devoted to an econometric treatment of asset allocation problems. We propose a very general procedure for dynamic asset allocation by using adaptive particle filters. It represents an advance over the traditional asset allocation because it incorporates jumps in mean returns and volatilities when using historical data. An advantage of our procedure is that it is very general and independent from any specific utilities. The empirical study shows our procedure indeed outperforms traditionally methods.The theoretical contribution of our paper is that we propose a general methodology which can accommodate lots of asset allocation problems. The methodology is easy to implement and simple to apply many models which are popular in returns modelling and asset allocation. It also inherits all the advantages of many variants of particle filtering approach. In fact, we provide a powerful tool for the researchers in finance area to solve the problems mentioned above.The empirical contribution is that we first apply our approach to the double-jump model. Based on S& P 500 data from 1996 to 2011, we find variations in posterior distributions of parameters. Our algorithm can quickly adapt to the new information and update the values. Next, we apply our framework to Log and Power utility. In general, we observe the same pattern for both cases: higher cumulative excess returns and larger CER for SVCJ model than SV model with same framework, higher cumulative excess returns and larger CER for our sequential strategy than others with same model.

ISBN: 9798662528994Subjects--Topical Terms:

3168311
Business administration.
Subjects--Index Terms:

Asset allocation

Dynamic Asset Allocation Using Adaptive Particle Filters.
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245 1 0 $a Dynamic Asset Allocation Using Adaptive Particle Filters.
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300 $a 66 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-04, Section: A.
500 $a Advisor: Lai, T. L.; Luenberger, David G.;Primbs, James.
502 $a Thesis (Ph.D.)--Stanford University, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a Asset allocation is one of the most important problems in practical investment management and also plays a central role in financial economics. This study is devoted to an econometric treatment of asset allocation problems. We propose a very general procedure for dynamic asset allocation by using adaptive particle filters. It represents an advance over the traditional asset allocation because it incorporates jumps in mean returns and volatilities when using historical data. An advantage of our procedure is that it is very general and independent from any specific utilities. The empirical study shows our procedure indeed outperforms traditionally methods.The theoretical contribution of our paper is that we propose a general methodology which can accommodate lots of asset allocation problems. The methodology is easy to implement and simple to apply many models which are popular in returns modelling and asset allocation. It also inherits all the advantages of many variants of particle filtering approach. In fact, we provide a powerful tool for the researchers in finance area to solve the problems mentioned above.The empirical contribution is that we first apply our approach to the double-jump model. Based on S& P 500 data from 1996 to 2011, we find variations in posterior distributions of parameters. Our algorithm can quickly adapt to the new information and update the values. Next, we apply our framework to Log and Power utility. In general, we observe the same pattern for both cases: higher cumulative excess returns and larger CER for SVCJ model than SV model with same framework, higher cumulative excess returns and larger CER for our sequential strategy than others with same model.
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653 $a Investment management
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793 $a English
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