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研究生: 蕭智⽂
Chih-Wen Hsiao
論文名稱: 運用異質變異數與精確估計模型方法分析複雜的金融時間序列數據— 以新冠疫情前後台灣股價指數期貨為例
Heterogeneity of complex financial time series data and an accurate estimation modeling approach - An example of Taiwan stock index futures before and during COVID-19
指導教授: 盧希鵬
Hsi-Peng Lu
口試委員: 盧希鵬
Hsi-Peng Lu
王譯賢
Yi-Hsien Wang
黃世楨
Shih-Jhen Huang
李彥賢
Yen-Hsien Lee
金志聿
Chih-Yu Chin
李玫郁
Mei-Yu Lee
學位類別: 博士
Doctor
系所名稱: 管理學院 - 管理研究所
Graduate Institute of Management
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 89
中文關鍵詞: ⼈工智慧異質變異數模型形式選擇複雜的⾦融數據
外文關鍵詞: Artificial Intelligence, Heteroscedasticity, Model Form Selection, Complex Financial Data
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  • 大數據分析和人工智慧已是當前的科技主流,然而從研究方法角度解析「累加性數字」的方法較少被提及,因此,本研究提供了一種利用異質變異數估計的數學方法,以達到為複雜金融數據建立更精確的數學模型之目的。透過特定或多種數學模型的建構,由AI判斷最佳的模型,包含 (1.)運用AI,針對特定線性或非線性模型,進行資料數量的決策(AI多線段法),以及 (2.) 運用AI,從多種數學模型當中抉擇出最佳模式。後者須同時考量期望值模型和變異數異質性模型,並能合併二者以達到研究人員所能做到之最佳數學模式。同時,以新冠肺炎前後期間,台灣股價指數期貨日收益率為例。研究發現人工智慧可建構更為符合數據之數學模型,並以數學式提供良好訊號,做決策依據使用。若使用AI多線段法,則可對應上該期間市場發生的事件。若使用AI判斷多種數學模型,則可在無人為干預或設定下,符合人工智慧特性,甚至做到人類無法計算的精確數學模型結果。即使是黑天鵝的新冠肺炎事件,在本研究之分析方法上,亦得到非常良好的結果。


    Big data analysis and artificial intelligence are the current mainstream of science and technology, but the method of analyzing additive numbers from the perspective of research methods is rarely mentioned. Therefore, this study provides a method of building a more accurate mathematical model for complex financial data by using heterogeneous variance estimation. This research uses the daily yields of Taiwan Stock Index Futures as the source of raw data and focuses on analyzing three distinct time frames: before COVID-19, during COVID-19, and the entire time period. There are two methods used, and both involve AI: an AI multi-line segment method followed up with mathematical models selected by AI. AI multi-line segment method uses either a specific linear or nonlinear model fitted with optimal data quantity determined by AI. Followed up by a mathematical model selection method where AI screens for the best model from a variety of mathematical models. The latter must consider both the expected value model and the variance heterogeneity model and combine the two to achieve the goal of a more accurate mathematical model. When the AI multi-line method is used, trends and turning points coincidentally corresponds to previous events of significant impact. On the flip side, having AI decide on the two mathematical models used, out of a pool of prospects, meet the characteristics of artificial intelligence without human intervention or setting. It is found that artificial intelligence is capable of selecting more suitable mathematical models based on the characteristics of the inputted raw data. As the results are derived from a mathematical formula, it is free of manipulation and advantageous for decision-making. In addition, the result also shows higher accuracy that cannot be calculated by humans. Even during a black swan event such as the COVID-19 pandemic, this method has achieved highly accurate results.

    目錄 中文摘要 I 英文摘要 II 致 謝 III 目 錄 IV 圖 目 錄 VI 表 目 錄 VII 第一章 緒 論 1 1.1 研究背景與動機 1 1.2 研究問題與目的 5 1.3 研究流程 6 第二章 文獻探討 8 2.1 模型建構相關文獻 8 2.2 傳染病爆發對經濟之影響 11 2.3 COVID-19疫情對經濟之影響 14 2.4 小結 17 第三章 研究設計 18 3.1 研究架構 18 3.2 樣本期間與樣本選取 20 3.3 AI人工智能多線段法 22 3.4 期望值模型 24 3.5 異質變異數模型 27 3.6 具有異質變異數的期望值模型 31 第四章 實證結果與分析 33 4.1 AI人工智能多線段法 34 4.2 期望值配適模型 43 4.3 異質變異數配適模型 50 4.4 具有異質變異數的期望值配適模型 57 第五章 結論與建議 63 5.1 研究結論 63 5.2 研究建議 65 5.3研究限制 66 參考文獻 67 附錄 73 圖目錄 圖1.1 研究流程圖 7 圖3.1 研究架構圖 19 圖3.2 AI人工智能多線段法的流程圖 23 圖4.1 線性迴歸估計線圖 35 圖4.2 AI人工智能多線段法的直線線段估計線圖 36 圖4.3 AI人工智能多線段法的直線線段估計線分段圖 38 圖4.4 AI人工智能多線段法的非線性線段估計線圖 39 圖4.5 個案1實際值與配適圖 46 圖4.6 個案2實際值與配適圖 47 圖4.7 個案3實際值與配適圖 48 圖4.8 個案1異質變異數的配適殘差結果圖 53 圖4.9 個案2異質變異數的配適殘差結果圖 54 圖4.10 個案3異質變異數的配適殘差結果圖 55 圖4.11 個案1具有異質變異數的期望值配適模型圖 60 圖4.12 個案2具有異質變異數的期望值配適模型圖 61 圖4.13 個案3具有異質變異數的期望值配適模型圖 62 表目錄 表4.1 敘述性統計 33 表4.2 新冠病毒開始發生期間AI人工智能多線段法直線估計式 37 表4.3 新冠病毒開始發生期間AI人工智能多線段法非線性估計式 41 表4.4 期望值曲線迴歸係數 45 表4.5 解釋變數的曲線係數 52

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