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博碩士論文 etd-0801122-173839 詳細資訊
Title page for etd-0801122-173839
論文名稱
Title
機器學習選擇權定價—以台指選擇權日內資料為例
Option Pricing Using Machine Learning with Intraday Data of TAIEX Option
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
49
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
2022-06-20
繳交日期
Date of Submission
2022-09-01
關鍵字
Keywords
選擇權定價、隱含波動度、機器學習、日內資料、XGBoost、CatBoost
Option pricing, Implied volatility, Machine Learning, Intraday data, XGBoost, CatBoost
統計
Statistics
本論文已被瀏覽 223 次,被下載 0
The thesis/dissertation has been browsed 223 times, has been downloaded 0 times.
中文摘要
近年來人工智慧 (Artificial Intelligence, AI) 在金融領域的應用愈來愈廣泛,其中應用在資產定價也是個熱門話題。本研究將以台灣加權指數為標的之選擇權日內資料用機器學習(Machine Learning, ML)做選擇權定價,以此方式和傳統的Black – Scholes選擇權定價模型做比較,觀察結果是否更加貼近實際的市場價格。本研究也在實證中做了訓練目標為選擇權價格與波動度的模型比較,和不同資料頻率的模型比較。實證結果顯示,用機器學習做選擇權定價能比BS模型更貼近實際價格,而訓練目標為波動度的表現優於選擇權價格,頻率越高的資料定價能力也越高。不過就預測未來半年價格的能力而言,機器學習的預測能力沒有比BS模型直接用前一期價格做還好。
Abstract
In recent years, the application of artificial intelligence in the field of finance has become more and more widespread. Among them, the application in asset pricing is also a hot topic. In this study, I use machine learning (ML) for option pricing with intraday data of Taiwan Capitalization Weighted Stock Index (TAIEX). This method is compared with the traditional Black-Scholes option pricing model to see if the results are closer to the actual market price. This study also empirically compares models with output of option price and volatility, and models with different data frequencies.The empirical results show that using machine learning for option pricing can be closer to the actual price than the BS model, and the training objective of volatility outperforms the option price, and the higher the frequency, the higher the information pricing ability. However, in terms of the ability to predict prices for the next six months, machine learning is no better than a BS model that uses the previous period's prices directly.
目次 Table of Contents
Contents
論文審定書i
摘要ii
Abstractiii
目錄iv
List of Figuresv
List of Tablesvi
1. Introduction 1
1.1 Background 1
1.2 Research Purpose 2
1.3 Thesis Organization 3
2. Literature Review 5
2.1 Option Pricing Model 5
2.2 XGBoost and CatBoost for Financial Applications 9
2.3 Option Pricing Using Machine Learning 11
3. Methodology 15
3.1 Research Design 15
3.2 Data 16
3.3 Model Description 17
3.4 Model Comparisons 25
4. Empirical Results 30
4.1 Descriptive Statistics of Data 30
4.2 Model Comparisons 31
5. Conclusions 38
References 40
List of Figures
FIGURE 1 RESEARCH FRAMEWORK 4
FIGURE 2 FLOW CHART 15
FIGURE 3 FIRST STAGE OF MODEL COMPARISON 26
FIGURE 4 SECOND STAGE OF MODEL COMPARISON 28
FIGURE 5 ROLLING SCHEME DIAGRAM 28
FIGURE 6 RECURSIVE SCHEME DIAGRAM 28
FIGURE 7 RECURSIVE SCHEME DIAGRAM 29

List of Table
TABLE 1 FEATURES SETS TABLE 27
TABLE 2 DESCRIPTIVE STATISTICS OF THE IMPORTANT VARIABLES 31
TABLE 3 COMPARISON OF DIFFERENT LABEL AND FEATURES 32
TABLE 4 FIRST SET OF FEATURES CUT BY ROLLING SCHEME 33
TABLE 5 FIRST SET OF FEATURES CUT BY RECURSIVE SCHEME 34
TABLE 6 FOURTH SET OF FEATURES CUT BY ROLLING SCHEME 34
TABLE 7 FOURTH SET OF FEATURES CUT BY RECURSIVE SCHEME 35
TABLE 8 COMPARISON OF DIFFERENT DATA FREQUENCIES
36
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