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系統識別號 U0026-2507201519240500
論文名稱(中文) 應用技術分析提升GARCH模型對於股票指數波動度之預測能力
論文名稱(英文) Using technical rules to enhance the predictability of the standard GARCH model for the volatility of stock indices
校院名稱 成功大學
系所名稱(中) 財務金融研究所
系所名稱(英) Graduate Institute of Finance
學年度 103
學期 2
出版年 104
研究生(中文) 苟顥書
研究生(英文) Hao-Shu Kou
學號 R86011076
學位類別 碩士
語文別 英文
論文頁數 34頁
口試委員 指導教授-顏盟峯
口試委員-劉裕宏
口試委員-王澤世
口試委員-許英麟
中文關鍵字 波動度預測  GARCH  技術分析  Step-SPA(k)  股票指數波動度  實現波動率 
英文關鍵字 Volatility forecasting  GARCH  Technical rules  Step-SPK(k)  Realized volatility  Volatility of stock index 
學科別分類
中文摘要 本研究之主要目的是在探討技術分析能否改進一般化自我迴歸條件異質變異數(GARCH(1,1))模型對股價指數波動度的預測能力。研究採用四大技術指標:濾嘴法則、支撐與壓力、通道突破、移動平均共1,107條技術規則,並分別應用於股價指數的實現波動率產生訊號,探討以技術分析訊號擴充後的GARCH(1,1)模型能否提升基準模型(GARCH(1,1))的波動度預測力;並以平均絕對誤差(MAE)與均方誤差(MSE)為評比基準,利用Hsu, Kuan, and Yen (2014)的Step-SPA(k)檢定法來降低資料窺探偏誤。本研究以2008年至2012年與1993年至2012年的道瓊工業平均指數資料為樣本,結果發現以技術規則分析實現波動率訊號對GARCH(1,1)模型預測股價指數波動率能力並無顯著提升之效果。
英文摘要 The main purpose of this study is to explore whether technical analysis can improve the predictability of Generalized Autoregressive Conditional Heteroscedasticity (GARCH(1,1)) for the volatility of stock index. Four technical analysis categories, filter rule, support and resistance, channel breakout, and moving average, were applied, creating 1,107 rules in total, and the rules were used individually on the realized volatility of stock index to obtain signals. We discuss if the GARCH(1,1)-augmented model with technical analysis signals can provide better predictability for stock index volatility than the benchmark model(GARCH(1,1)). Employing the mean absolute error (MAE) and mean squared error (MSE) as performance measure, we conduct Hsu, Hsu and Yen’s (2014) Step-SPA(k) test to control for the data snooping bias. Dow Jones Industrial Average data from 2008 to 2012 and 1993 to 1992 are used as the sample. Our results showed that analyzing realized volatility signals with technical rules does not lead to a significant improvement in the predictability of the GARCH(1,1) model for volatility of stock indices.
論文目次 摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
Chapter 1. Introduction 1
1.1 Research background 1
1.2 Research motivation and purpose 2
Chapter 2. Literature Review 3
2.1 Modeling volatility-GARCH 3
2.2 Data snooping problem 4
Chapter 3. Methodology 6
3.1 Data 6
3.2 Realized volatility 7
3.3 Technical Trading Rules 9
3.3.1 Filter Rules (FR) 10
3.3.2 Support and Resistance (SR) 12
3.3.3 Channel Breakout (CB) 13
3.3.4 Moving Average (MA) 14
3.4 Model Specifications 14
3.4.1 Benchmark Model 15
3.4.2 Competing Model 15
3.5 Forecasting method 16
3.6 Evaluating volatility forecasts - Step-SPA(k) Test 18
Chapter 4. Empirical Results 21
4.1 Necessary test in advance 21
4.2 Volatility forecasting result 22
Chapter 5. Conclusions and Suggestions 26
5.1 Conclusions 26
5.2 Further research suggestions 26
Appendix A. Parameters of Technical Trading Rules 28
Appendix B. Stationary Bootstrap 30
References 31
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