國立虎尾科技大學 |

Bayesian Filtering Estimation of Statistical Dynamic Factor Model.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Bayesian Filtering Estimation of Statistical Dynamic Factor Model./
作者:	Mu, Yu.
面頁冊數:	1 online resource (104 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Applied mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355663921

Bayesian Filtering Estimation of Statistical Dynamic Factor Model.
Mu, Yu.

Bayesian Filtering Estimation of Statistical Dynamic Factor Model. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2017.

Includes bibliographical references

Statistical factor analysis has been widely used in many areas of investment science such as risk management, portfolio selection, trading strategies, etc. This dissertation mainly investigates the estimation of dynamic factor model in the Bayesian framework, using the techniques of particle filter with online parameter learning such as marginalized particle filter and particle learning. We also compare our results with the offline conventional method such as Kalman filter combined with EM algorithm in root mean squared error criterion.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355663921Subjects--Topical Terms:

1069907
Applied mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Bayesian Filtering Estimation of Statistical Dynamic Factor Model.
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245 1 0 $a Bayesian Filtering Estimation of Statistical Dynamic Factor Model.
264 0 $c 2017
300 $a 1 online resource (104 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Advisers: Robert J. Frey; Raphael Douady.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2017.
504 $a Includes bibliographical references
520 $a Statistical factor analysis has been widely used in many areas of investment science such as risk management, portfolio selection, trading strategies, etc. This dissertation mainly investigates the estimation of dynamic factor model in the Bayesian framework, using the techniques of particle filter with online parameter learning such as marginalized particle filter and particle learning. We also compare our results with the offline conventional method such as Kalman filter combined with EM algorithm in root mean squared error criterion.
520 $a In the real data analysis, regime switching or structure break in the factor structure will make the estimation of static model difficult and lead to the problem of model misspecification. For solving this issue, we construct a regime switching dynamic factor model and compare its performance with conventional method using EM algorithm in the context of statistical arbitrage trading strategy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Applied mathematics. $3 1069907
650 4 $a Statistics. $3 556824
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0364
690 $a 0463
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 1179106
773 0 $t Dissertation Abstracts International $g 79-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10687134 $z click for full text (PQDT)