本研究可分為兩部分。第一部分，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)、遺傳基因演算誤差指數異質變異數混合模型(EC-EGARCHGA)及遺傳基因演算狀態轉換指數異質變異數混合模型(RS-EGARCHGA)在價格的預測能力。第二部分，利用模擬交易檢視投資人在扣除交易成本之後，是否能藉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)之預測模式在S&P500指數期貨及SPDR指數型基金賺取超額報酬。
誤差指數異質變異數模型，因同時考慮條件平均值與變異數，將會完整地改善在價格的預測能力。而狀態轉換指數異質變異數模型中，假設不同時點的狀態會依照馬可夫鏈的行為而變動，所以必須考慮異質變異數的情況。同時本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， Lai和Li(2006)發現可提高對每股盈餘的預測能力。
本研究以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。並利用模擬交易檢視投資人在扣除交易成本之後，是否能以S&P500指數期貨及SPDR指數型基金賺取報酬，最後以大樣本的Z檢定超額報酬是否顯著。

This study contains two parts. First of all, the study investigates the effectiveness of various price forecasting models including Error Correction EGARCH model (EC-EGARCH), regime-switching EGARCH model (RS-EGARCH), hybrid model by combining EC-EGARCH with Genetic Algorithms (EC-EGARCHGA) and hybrid model by using combining RS-EGARCH with Genetic Algorithms (RS-EGARCHGA). Second, we use forward simulation to examine whether investors can earn positive excess return net of transaction cost based on predictive value about S&P500 index futures and SPDR index fund by Error Correction EGARCH model (EC-EGARCH), regime-switching EGARCH model (RS-EGARCH).
Since EC-EGARCH model considers both conditional mean and variance at the same time, it can help us predict price more completely. As to the RS-EGARCH model, it relies on different coefficients in each regime to account for the possibility that the financial series may undergo a finite numbers of changes over the sample period. In addition, Genetic Algorithm (GA), is also used in this study to see whether artificial intelligence, GA, can help improve forecasting ability in price level since it has been supported by Lai and Li (2006) in predicting earning per share.
Based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Paired t Test and Fisher Exact Test, we can evaluate which chosen models, EC-EGARCH, RS-EGARCH, EC-EGARCHGA and RS-EGARCHGA, performs best in predicting price. Furthermore, we examine whether the investors can earn significant excess return by Z Test.

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