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博碩士論文 etd-0622109-142227 詳細資訊
Title page for etd-0622109-142227
論文名稱
Title
動態條件相關係數模型之估計值受時間聚集變動的影響及授信決策之應用
AN INVESTIGATION ON THE DYNAMIC CONDITIONAL CORRELATION MODELS FOR AN EMPIRICAL ESTIMATIONS OF THE TEMPORAL AGGREGATION AND ITS APPLICATION ON THE CREDITING POLICY
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
65
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2009-06-19
繳交日期
Date of Submission
2009-06-22
關鍵字
Keywords
效率前緣、信用風險、時間聚合、高頻資料、動態相關係數模型
DCC, crediting policy, efficient frontier, high frequency, mean-variance
統計
Statistics
本論文已被瀏覽 5760 次,被下載 1171
The thesis/dissertation has been browsed 5760 times, has been downloaded 1171 times.
中文摘要
Engle(2002)所提出的DCC模型在多變數財務時間序列上,已經是最受歡迎的模型之ㄧ,但是時間聚合對DCC模型估計值的影響卻尚未有研究成果。本文利用台灣股票市場中的八大類股指數,從2004年1月2日到2006年12月31日所建構的五分鐘、十分鐘、十五分鐘、三十分鐘、九十分鐘及二百七十分鐘報酬來檢視時間聚合變動對DCC模型估計值的影響。在標準化所有報酬後,實證結果顯示八大類股間的動態相關係數都是正數,而且隨著時間變動而非固定不變,電子、營建類股和其他的類股有較高的相關係數,塑膠類股和其他類股卻有較低的相關係數,更重要的是在低頻率時,八大類股有較高的條件相關係數,換句話說,時間聚合變動會增加條件相關係數。
在台灣,商業銀行的企業放款絕大多數屬於信用風險較難有效掌控的中小企業。為落實國內金融業風險管理理念,主管機關要求國內銀行檢討授信政策,避免風險過度集中,其中更發函明訂各金融機構,應依行業別、集團企業別及同一人之授信等分別訂定風險承擔限額並定期檢討。本文針對銀行的中小企業貸款戶進行產業分類,依各產業樣本群之授信記錄,逐月計算銀行資金貸放之報酬率及其標準差,據此建立銀行放款組合之效率前緣,及利用DCC模型所估計的動態相關係數,求出最小風險值下的最適授信政策。期望藉由本文模式之探討,能提供金融機構作為銀行制定授信政策之依據。
Abstract
The Dynamic Conditional Correlation (DCC) model proposed by Engle (2002) has become one of the most popular models for the analysis of multivariate financial time series. Yet, the impact of temporal aggregation on the DCC estimates has not yet been rigorously investigated. This thesis examines the changes of DCC estimates when the intraday returns are aggregated from 5-minutes to 270-minutes returns using Taiwanese eight industry index returns from Jan. 2, 2004 to Dec. 31, 2006. Our empirical analysis finds that dynamic correlation coefficients between the 8 industry index returns are all positive and time-varying. Further, Electronic and Building indices seem to have high correlation with other industry indices whereas plastics has a lower correlation with others. What is more important, all return series have higher conditional correlation for lower frequencies. In other words, temporary aggregation will increase the conditional correlation.
This thesis also seeks to categorize the loan accounts of small- and medium-scale corporations according to their respective business sectors and calculate the monthly returns and standard deviation of the bank loans according to the groups of sample of credit records from each sector, with the purpose of establishing the efficient frontier of the loan combinations of the banks and estimation the dynamic conditional correlation to discover the optimal crediting policy. It is expected that the discussion using the model presented in the thesis may provide the basis for financial institutions as they establish their respective crediting policies.
目次 Table of Contents
Abstract………………………………………………………….vII

1. Introduction…………………………………………..............1

2. Literature Review…………………………………………….4
2.1 Time varying correlation…………………………………4
2.2 Temporal aggregation…………………………………….6

3. DCC and Markowitz model…………..……………………....9
3.1 DCC model……………………………………………….9
3.2 Markowitz model………………………………………..13
3.3 The calculation of returns and standard deviations for asset
loans of banks……………………………………..…….14

4. The application of DCC model to the 8 industry indices of Taiwan stock market…………………………………………………..15.
4.1 Data and summary statistics of the 8 industry indices…...15
4.2 Empirical results………………………………………….16

5. The application of Markowitz and DCC model to make the optimal crediting policy………………………………………………..27
5.1 Data and summary statistics of the 5 business sectors….. .27
5.2 Empirical Results…………………………………………28

6. Conclusions……………………………………………………..35

References………………………………………………………….36

Appendix A: Volatility pattern of various frequencies for 8 industrial indices…………………………………….…………41
Appendix B: Correlation path of various frequencies for 8 industrial indices……….……………………………………….46
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