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博碩士論文 etd-0703116-174342 詳細資訊
Title page for etd-0703116-174342
論文名稱
Title
印尼股票市場之統計分析
Statistical Analysis of Indonesia Stock Market
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
44
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
2016-07-11
繳交日期
Date of Submission
2016-08-25
關鍵字
Keywords
赤池訊息準則、波動、自我迴歸條件異質性模型、廣義自我迴歸條件異質性模型、叢聚分析法
GARCH, Clustering, Volatility, ARCH, AIC
統計
Statistics
本論文已被瀏覽 5714 次,被下載 22
The thesis/dissertation has been browsed 5714 times, has been downloaded 22 times.
中文摘要
在本篇論文中,我們分析印尼LQ45指數中的股票。對每支股票的對數報酬率建立廣義自我迴歸條件異質性模型,使用赤池訊息準則,選擇較佳的模型去預測波動,並透過損失函數,評價預測波動的結果。此外,利用K 平均數叢聚分析法,分析每一季的股票叢聚情形。
Abstract
Liberalization and economic integration become topics of discussion and research in
recent years. Indonesia is one of the countries that actively participates in the achievement
of liberalization and economic integration, especially in the ASEAN region. Indonesia stock
market has a high degree of volatility which can be used to produce high investment returns,
which is one of the reasons to attract foreign investors to enter Indonesia stockmarket.
Volatility plays an important role for market participants to control and reduce their market
risk of financial assets


In this study we establish the volatility models for the stocks listed in the Indonesia stock
market index LQ45. The models we considered include the Autoregressive Conditional Heteroskedasticity
(ARCH) proposed by Engle (1982), Generalized Autoregrassive Conditional
Heteroskedasticity (GARCH) by Bollerslev (1986), the Stochastic Volatility Model (SVM) by
Jacquier, Polson and Rossi (1994), and Autoregressive Moving Average (ARMA) by Box, Jankins, and Reinsel (1994). We use the daily log returns to establish the models and select the best
one via Akaike information criterion (AIC).Moreover, we use it to predict the future volatility.
In the end, we also apply machine learning application such as the K-means method to figure
out how itsmovement of the clusters volatility in Indonesia stocks.
目次 Table of Contents
Chinese Abstract i
Abstract ii
Table of Contents iv
List of Tables v
List of Figures vi
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Importance of This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Data Description 3
2.1 The Underlying Index and SelectionMethodology . . . . . . . . . . . . . . . . . . 3
2.2 Index Evaluation and Stock Replacement . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Index CalculationMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Models andMethods 5
3.1 Testing for ARCH Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 VolatilityModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2.1 The Standard GARCHModel (SGARCH) . . . . . . . . . . . . . . . . . . . . . 7
3.2.2 The Integrated GARCHModel (IGARCH) . . . . . . . . . . . . . . . . . . . . 8
3.2.3 The Exponential GARCHModel (EGARCH) . . . . . . . . . . . . . . . . . . . 8
3.2.4 The Glosten-Jagannathan-Runkle GARCHModel (GJRGARCH) . . . . . . . 8
3.2.5 The Asymmetric Power ARCHModel (APARCH) . . . . . . . . . . . . . . . . 9
3.2.6 The Realized GARCHModel (RGARCH) . . . . . . . . . . . . . . . . . . . . . 9
3.3 Prediction Accuracy of The VolatilityModels . . . . . . . . . . . . . . . . . . . . . . 10
3.4 K-means Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Analysis Procedure 12
4.1 VolatilityModeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2 K-means Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3 Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Empirical Results 14
5.1 Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.1.1 Check Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.1.2 Testing ARCH Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.1.3 Model Fitting and Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.1.4 Prediction and Selected AppropriateModel . . . . . . . . . . . . . . . . . . 17
5.2 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.2.1 Volatility Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5.2.2 Selecting Cluster Solution Using The K-means . . . . . . . . . . . . . . . . 19
5.2.3 Hierarchical Agglomerative . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6 Conclusion 22
References 24
Appendix 26
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