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| Title: | 基於分位數迴歸森林在選擇權結算價 報酬率預測及交易策略應用
Based on Quantile Regression Forests for Predicting Option Settlement Price Returns and Trading Strategy Applications |
| Authors: | 陳旻寬
Chen, Min-Kuan |
| Contributors: | 廖四郎
Liao, Szu-Lang
陳旻寬
Chen, Min-Kuan |
| Keywords: | 分位數迴歸森林
分位數迴歸
選擇權投資組合
選擇權結算價報酬率預測
選擇權交易
Quantile Regression Forests
Quantile Regression
Options portfolios
Options settlement price return forecasting
Options trading |
| Date: | 2024 |
| Issue Date: | 2024-08-05 12:18:11 (UTC+8) |
| Abstract: | 選擇權透過投資組合以應對市場預期或避險需求，顯示出其獨特優勢。然而，過往的選擇權研究多聚焦於定價和避險策略，對於確定投資組合履約價的探討相對缺乏。為解決此問題，本研究採用基於隨機森林的分位數迴歸森林(Quantile Regression Forests, QRF)，預測選擇權結算價的報酬率分位數並建立信賴區間。本文採用了蝴蝶價差策略和兀鷹價差策略，這兩種適用於預期市場波動範圍內的選擇權投資組合策略。透過對臺灣加權股價指數選擇權(臺指選)的週選擇權進行實證分析和回測交易，比較了QRF與傳統分位數迴歸(Quantile Regression, QR)的效能。結果顯示，QRF在預測準確度、勝率及報酬率方面均顯著優於QR，並在統計上達到顯著的正報酬率。這些發現強調了分位數預測與選擇權交易策略相結合在市場不確定性中的盈利潛力，並突出了機器學習在捕捉金融市場特徵方面的有效性。未來研究將探索結合深度學習以提高預測準確性，進行資金管理以優化風險控制，並將這些策略擴展到更多金融產品及選擇權投資組合。
Options demonstrate unique advantages through portfolio adjustments to meet market expectations or hedging needs. However, past research on options has predominantly focused on pricing and hedging strategies, with less discussion on determining strike prices for portfolios. To address this issue, this study employs Quantile Regression Forests (QRF), based on random forest algorithms, to predict the quantile of option settlement price returns and establish confidence intervals. This paper utilizes strategies such as the butterfly spread and condor spread, which are suited to expected market volatility ranges. Empirical analysis and backtesting trades were conducted using weekly options on the Taiwan Capitalization Weighted Stock Index (TAIEX options), comparing the efficacy of QRF with traditional Quantile Regression (QR). The results show that QRF significantly outperforms QR in terms of prediction accuracy, win rate, and return, achieving statistically significant positive returns. These findings highlight the profit potential of combining quantile forecasting with options trading strategies amidst market uncertainties and underscore the effectiveness of machine learning in capturing financial market characteristics. Future research will explore integrating deep learning to enhance predictive accuracy, optimize risk control through capital management, and extend these strategies to a broader range of financial products and options portfolio strategies. |
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| Description: | 碩士
國立政治大學
金融學系
111352021 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111352021 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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