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博碩士論文 etd-0528123-091648 詳細資訊
Title page for etd-0528123-091648
Application of the XGBoost Algorithm in Option Trading Strategies with Volatility Risk Premium
Year, semester
Number of pages
Advisory Committee
Date of Exam
Date of Submission
Volatility Risk Premium, Machine Learning, TAIEX Options, TAIEX Options Volatility Index, Strangle Position
本論文已被瀏覽 74 次,被下載 2
The thesis/dissertation has been browsed 74 times, has been downloaded 2 times.
本研究依照無模型限制的方法來衡量臺指選擇權的波動度風險溢酬(Volatility Risk Premium, VRP),並結合臺指選擇權波動率指數(TAIWAN VIX)與三大法人相關指標,以 XGBoost 演算法來預測報酬點數、建構交易策略。考量到保證金帳戶之效率,選擇以趨近 Delta 中立的勒式部位作為交易對象。權衡收穫 VRP 效率與交易流動性,本交易策略於滿足策略條件下之每日一般交易時段開盤建倉、收盤平倉。在考慮保證金帳戶設定及資金持有成本下,在回測區間 2019 年 7 月到 2021 年 12 月中,達成年化報酬率 61.04% 及夏普比率(Sharpe Ratio) 3.4332 ,分別為加權指數的 2.8116 倍及 2.8130 倍。同時,賣出勒式部位時的報酬與風險表現皆較買進勒式部位時佳。最後,本研究驗證以週頻率來訓練模型在此交易策略之績效表現與以日頻率來訓練者並無顯著差異。
This study employs a model-free approach to measure the volatility risk premium (VRP) of Taiwan index options. It combines the Taiwan index option volatility index (TAIWAN VIX) with relevant indicators from the major institutional traders. The XGBoost algorithm is used to predict return points and develop trading strategies. To consider margin account efficiency, the strategy focuses on using a strangle position that is close to Delta neutral as the trading object. Balancing the VRP harvesting efficiency and trading liquidity, the strategy opens and closes positions during the regular trading session. Taking into consideration margin account settings and capital holding costs, the backtest period from July 2019 to December 2021 shows an annualized rate of return of 61.04% and a Sharpe ratio of 3.4332. These figures are 2.8116 times the weighted index and 2.8130 times respectively. Furthermore, selling a strangle position outperforms buying a strangle position in terms of return and risk performance. Finally, the study verifies that the performance of the trading strategy trained with weekly frequency is not significantly different from that trained with daily frequency.
目次 Table of Contents
摘 要ii
目 錄iv
圖 次vi
表 次vii
第一章 緒論1
第一節 研究動機1
第二節 研究目的3
第三節 研究架構3
第二章 文獻回顧5
第一節 波動度風險溢酬5
第二節 台灣指數選擇權交易策略7
第三節 機器學習在金融領域的應用9
第三章 研究方法11
第一節 研究流程11
第二節 研究資料12
第三節 特徵建構14
第四節 預測目標19
第五節 模型訓練22
第六節 模型介紹23
第七節 交易策略27
第四章 實證結果29
第一節 模型預測結果29
第二節 買進及賣出績效分析41
第三節 建構模型頻率分析46
第五章 結論49
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