東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Estimation and Detection of Network ...

Li, Shanshan.

FindBook

Google Book

Amazon

博客來

Estimation and Detection of Network Variation in Intraday Stock Market.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Estimation and Detection of Network Variation in Intraday Stock Market./
作者:	Li, Shanshan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10249760
ISBN:	9781369569162

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

Estimation and Detection of Network Variation in Intraday Stock Market. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 157 p.

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

Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.

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.

ISBN: 9781369569162Subjects--Topical Terms:

2122814
Applied mathematics.

Estimation and Detection of Network Variation in Intraday Stock Market.
LDR:03473nmm a2200325 4500 001 2154452
005 20180416072030.5
008 190424s2016 ||||||||||||||||| ||eng d
020 $a 9781369569162
035 $a (MiAaPQ)AAI10249760
035 $a (MiAaPQ)grad.sunysb:13099
035 $a AAI10249760
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Shanshan. $3 3186761
245 1 0 $a Estimation and Detection of Network Variation in Intraday Stock Market.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Advisers: Haipeng Xing; Jiaqiao Hu.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.
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.
590 $a School code: 0771.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Statistics. $3 517247
650 4 $a Finance. $3 542899
690 $a 0364
690 $a 0463
690 $a 0508
710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 1684041
773 0 $t Dissertation Abstracts International $g 78-08B(E).
790 $a 0771
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10249760