臺灣博碩士論文加值系統

本研究主要是比較誤差指數異質變異數模型(EC-EGARCH)、狀態轉換指數異質變異數模型(RS-EGARCH)以及將兩個原始模型結合遺傳基因演算法(hybrid model)在價格和波動性的預測能力。由於指數異質變異數模型(EGARCH)考慮了資訊不對稱的效果，所以本篇論文將使用指數異質變異數模型(EGARCH)作為條件變異數的方程式。
此篇論文結合了傳統的計量模型與遺傳基因演算法(GA)，希望藉此來檢視是否有助於改善在價格與波動的預測能力。這在以往文獻中，有Neely和Weller (2002)發現可提高對匯率波動的預測能力、Lai和Li(2006)發現可提高對每股盈餘的預測能力。
本研究所使用的資料為每年第四季十分鐘的史坦普爾500指數與史坦普爾500指數期貨，樣本期間則從1986年到2007年。在利用平均絕對誤差百分比(Mean Absolute Percentage Error, MAPE)、平均絕對誤差(Mean Absolute Error, MAE)、配對檢定(Paired t Test)及費雪真實檢定(Fisher Exact Test)等檢定法評估四種模型的預測績效之後，我們發現結合遺傳基因演算法的模型相對於傳統的計量模型，對價格和波動性都具有較好的預測能力。

This study is to investigate the effectiveness of various price and volatility forecast models including Error Correction EGARCH model (EC-EGARCH), Regime-Switching EGARCH model (RS-EGARCH), and hybrid models by combining Genetic Algorithm (GA) with EC-EGARCH (EC-EGARCHGA) and RS-EGARCH (RS-EGARCHGA). Since EGARCH rather than GARCH considers information asymmetry, EGARCH is used as conditional variance model in this study.
Hybrid models by combining EC-EGARCH and RS-EGARCH with Genetic Algorithm (GA) are used in this study to see whether one of artificial intelligence, GA, can help improve forecast ability in price level and volatility since it has been supported by Neely and Weller (2002) in predicting the volatility and Lai and Li (2006) in predicting earning per share.
In this thesis, 10-minute S&P 500 index and index futures are used and the sample period spreads from January 1986 to December 2007. Because yearly 10-minut interval data is hard to converge when EC-EGARCH or RS-EGARCH model is used, we only use the fourth quarter data in each year. Based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Paired t Test and Fisher Exact Test, we conclude that two hybrid models, EC-EGARCHGA and RS-EGARCHGA, perform better relative to their corresponding ordinary econometric models in price and volatility forecasts.

Abstract (Chinese) III
Abstract IV
Acknowledgement V
Content VI
List of Tables VIII
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Purpose of the Research 2
1.3 Structure of the Research 4
1.4 The Contributions of this Study 4
CHAPTER 2 Literature Review 6
2.1 Introduction of S&P 500 Index and Index Futures 6
2.2 Relative Studies about Error Correction model and GARCH model 8
2.3 Relative Studies about Regime-Switching Model 9
2.4 Relative Studies about Genetic Algorithm 12
CHAPTER 3 Data and Methodology 14
3.1 Unit Root Test 15
3.2 Cointegration Test 16
3.3 Length of Lag 17
3.4 Error Correction Model 18
3.5 Bivariate Exponential GARCH (EGARCH) Model 19
3.6 Markov Regime-Switching Model 23
3.7 EC-EGARCH and RS-EGARCH with Genetic Algorithm 26
3.8 Tests of Performance Among the Chosen Models 28
CHAPTER 4 Empirical Results 30
4.1 Data Collection 30
4.2 Stationary Test for S&P 500 Index and Index Futures Data 31
4.3 Cointegration Test 34
4.4 Length of Lag 37
4.5 The Bivariate EC-EGARCH Model 39
4.6 Predictive Power for S&P 500 Index and Index Futures 40
CHAPTER 5 Conclusions and Suggestions 43
5.1 Conclusions 43
5.2 Suggestions 45
References 46
Appendix 51

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