國立虎尾科技大學 |

Sparse Optimization Methods and Statistical Modeling with Applications to Finance.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Sparse Optimization Methods and Statistical Modeling with Applications to Finance./
作者:	Ho, Michael.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:	Dissertation Abstracts International77-11B(E).
標題:	Mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9781339783994

Sparse Optimization Methods and Statistical Modeling with Applications to Finance.
Ho, Michael.

Sparse Optimization Methods and Statistical Modeling with Applications to Finance. - 1 online resource (139 pages)

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this dissertation we examine methods to address this problem through application of methods and techniques from sparse optimization and modeling. Two new techniques are developed with the aim of improving the performance of mean-variance portfolio optimization.

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

Mode of access: World Wide Web

ISBN: 9781339783994Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Sparse Optimization Methods and Statistical Modeling with Applications to Finance.
LDR:02889ntm a2200385Ki 4500 001 908775
005 20180416072029.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781339783994
035 $a (MiAaPQ)AAI10116996
035 $a (MiAaPQ)uci:13626
035 $a AAI10116996
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Ho, Michael. $3 1179059
245 1 0 $a Sparse Optimization Methods and Statistical Modeling with Applications to Finance.
264 0 $c 2016
300 $a 1 online resource (139 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Jack Xin.
502 $a Thesis (Ph.D.) $c University of California, Irvine $d 2016.
504 $a Includes bibliographical references
520 $a It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this dissertation we examine methods to address this problem through application of methods and techniques from sparse optimization and modeling. Two new techniques are developed with the aim of improving the performance of mean-variance portfolio optimization.
520 $a In the first technique a pairwise weighted elastic net penalized mean-variance criterion for portfolio design in proposed. Here we motivate the use of this penalty through a robust optimization interpretation. This interpretation is then employed to develop a bootstrap calibration technique for the pairwise elastic net. The benefit of the pairwise weighted elastic net and calibration is shown in portfolio performance results using recent U.S. stock market data.
520 $a In the second application robust Kalman filtering techniques are applied to return covariance estimation from high frequency financial price data. The methods developed address three factors which make covariance estimation from high frequency data difficult: 1) microstructure noise, 2) asynchronous trading, and 3) jumps. The performance of these robust Kalman filtering techniques are tested against simulated high frequency data and are compared with other existing covariance estimators. The results indicate that the robust Kalman filtering techniques substantially improve covariance estimation performance versus other approaches.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mathematics. $3 527692
650 4 $a Finance. $3 559073
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0405
690 $a 0508
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Irvine. $b Mathematics. $3 1179060
773 0 $t Dissertation Abstracts International $g 77-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10116996 $z click for full text (PQDT)