國立虎尾科技大學 |

Estimation and Detection of Network Variation in Intraday Stock Market.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Estimation and Detection of Network Variation in Intraday Stock Market./
Author:	Li, Shanshan.
Description:	1 online resource (157 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Applied mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9781369569162

Estimation and Detection of Network Variation in Intraday Stock Market.
Li, Shanshan.

Estimation and Detection of Network Variation in Intraday Stock Market. - 1 online resource (157 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

The last few years have witnessed an exponential growth in the collection and analysis of financial market data. Investigating the interactions between the dynamics of the financial system and extracting useful information from these multivariate data streams can help us in improving our understanding of the underlying backbone in the financial market. These massive noisy data sets require the application of suitable and efficient dependency measurements for their analysis in a real-time environment. And that is why network analysis has emerged recently, which is a plausible representation helps interpret the hidden interconnection between the elements in large datasets. However, most frequently used methods in this area have certain limitations, such as the computational complexity or the assumption of a temporally invariant network. This thesis has two major purposes: firstly, to construct time-varying networks by presenting two new approaches to dynamically measure symmetric and asymmetric interactions; and secondly, to detect the structural breaks in the high dimensional time series of the financial market.

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

Mode of access: World Wide Web

ISBN: 9781369569162Subjects--Topical Terms:

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

554714
Electronic books.

Estimation and Detection of Network Variation in Intraday Stock Market.
LDR:03810ntm a2200373Ki 4500 001 908813
005 20180416072030.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781369569162
035 $a (MiAaPQ)AAI10249760
035 $a (MiAaPQ)grad.sunysb:13099
035 $a AAI10249760
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Li, Shanshan. $3 1179131
245 1 0 $a Estimation and Detection of Network Variation in Intraday Stock Market.
264 0 $c 2016
300 $a 1 online resource (157 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: 78-08(E), Section: B.
500 $a Advisers: Haipeng Xing; Jiaqiao Hu.
502 $a Thesis (Ph.D.) $c State University of New York at Stony Brook $d 2016.
504 $a Includes bibliographical references
520 $a The last few years have witnessed an exponential growth in the collection and analysis of financial market data. Investigating the interactions between the dynamics of the financial system and extracting useful information from these multivariate data streams can help us in improving our understanding of the underlying backbone in the financial market. These massive noisy data sets require the application of suitable and efficient dependency measurements for their analysis in a real-time environment. And that is why network analysis has emerged recently, which is a plausible representation helps interpret the hidden interconnection between the elements in large datasets. However, most frequently used methods in this area have certain limitations, such as the computational complexity or the assumption of a temporally invariant network. This thesis has two major purposes: firstly, to construct time-varying networks by presenting two new approaches to dynamically measure symmetric and asymmetric interactions; and secondly, to detect the structural breaks in the high dimensional time series of the financial market.
520 $a Building on previous work, we propose two computationally efficient approaches based on partial correlation network and vector autoregressive adjacency network. Since both of these estimators are under the high-dimension-low-sample-size setting, we develop a penalized kernel smoothing method for the problem of selecting non-zero elements of the time-varying matrix. The network structures of multivariate financial time series are established for the first time for such estimators and displayed in a graphical representation. Furthermore, we consider the problem of efficient financial surveillance aimed at prompt detection of structural breaks in the market. Assuming the model evolves in a piece-wise constant fashion, we study four types of detection rules, including statistical process control chart, generalized likelihood ratio detection rule, a detection method based on an extension of Shiryaev's Bayesian single change point model and a sequential detection rule for multiple change points. The efficiency of the proposed methods is demonstrated on both simulation studies and the empirical analysis focusing primarily on the intraday stock market. Our findings shed a new light on uncovering the hidden interactions between the financial dynamics and present new insight into market structure and market stability.
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 Finance. $3 559073
655 7 $a Electronic books. $2 local $3 554714
690 $a 0364
690 $a 0463
690 $a 0508
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 78-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10249760 $z click for full text (PQDT)