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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭湛東 | zh_TW |
| dc.contributor.advisor | Lawrence Hsiao | en |
| dc.contributor.author | 熊士詔 | zh_TW |
| dc.contributor.author | Hubert Hsiung | en |
| dc.date.accessioned | 2025-08-21T17:06:08Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-04 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99279 | - |
| dc.description.abstract | 本研究旨在探討機器學習技術於波動風險溢酬（Volatility Risk Premium, VRP）交易策略中的應用，透過結合行為金融與總體經濟變數，以提升策略獲利表現與穩定性。本研究應用了兩種選擇權策略：Delta-hedged short straddle與Delta-hedged short put策略，並分別以「Raw VRP」與「Excess VRP」（扣除標普500報酬後之VRP）為預測目標，評估多種機器學習模型之預測效能與交易表現，包括Linear Regression、Random Forest與Gradient Boost模型。本研究納入市場情緒、意見分歧、動能因子、總體經濟指標與波動率等多元變數，並透過傳統Feature Selection方法及SHAP（SHapley Additive exPlanations）解釋性方法，分析模型在不同維度下之穩健性與敏感度。實證結果顯示，於兩類策略中，Delta-hedged short put策略相對具有較高之報酬潛力與風險調整後績效；而以Excess VRP為目標變數之模型，整體而言具備更高之穩定性與預測能力。研究結果顯示，行為與總體經濟變數可有效強化VRP策略之預測性，並提供實務上進行動態部位調整之依據；同時，本研究亦揭示在市場環境變遷下維持模型穩健性所面臨之挑戰。綜上所述，本論文提出一套結合可解釋性機器學習方法與Delta-hedged選擇權策略之整合性架構，對於風險溢酬策略之研究與實務應用具一定貢獻。 | zh_TW |
| dc.description.abstract | This thesis examines the application of machine learning models to predict and optimize the profitability of volatility risk premium (VRP) strategies through adaptive delta-hedging techniques informed by behavioral and macroeconomic indicators. Specifically, we construct and evaluate delta-hedged short straddle and put strategies on the S&P 500 index. By comparing two target formulations — raw VRP and excess VRP over the underlying index — we assess the predictive performance and trading efficacy of linear regression, random forest, and gradient boosting models. Our methodology encompasses comprehensive feature engineering, incorporating sentiment, disagreement, momentum, macroeconomic surprises, and volatility structure signals. We implement both traditional and SHAP-based feature selection techniques to evaluate the sensitivity of model performance to input dimensionality. Among the two strategy types, the put-only strategy displays higher return potential and stronger Sharpe ratios. At the same time, excess VRP emerges as a more stable and predictive target variable than raw VRP. The findings demonstrate the value of incorporating behavioral finance and macroeconomic insights into quantitative models, highlighting the practical challenges of maintaining model robustness in shifting environments. This study contributes to the growing literature on machine learning applications in asset pricing by proposing a framework that combines delta-hedged options trading with interpretable predictive modeling. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T17:06:08Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T17:06:08Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
Acknowledgement ii 中文摘要 iii Abstract iv Table of Contents v List of Figures viii List of Tables x 1 Introduction 1 1.1 Motivations 1 1.2 Contributions 2 1.3 Paper Outline 2 2 Literature Review 4 2.1 Volatility Risk Premium 4 2.2 Behavioral Effects on VRP 5 2.3 Macroeconomics Effects on VRP 6 2.4 Machine Learning in Finance 7 3 Data and Methodology 9 3.1 Behavioral & Economic Features 9 3.1.1 VIX 9 3.1.2 Trading Volume 9 3.1.3 Relative Strength Index (RSI) 10 3.1.4 Put-Call Skew 10 3.1.5 Momentum (1M, 3M, 6M, 12M) 10 3.1.6 Price Gap Ratio 11 3.1.7 Intraday Range 11 3.1.8 Unemployment Rate 12 3.1.9 CPI MoM 12 3.1.10 GDP Forecast/Surprise/Dispersion 12 3.1.11 Bull-Bear Spread 13 3.2 Strategy Construction 13 3.2.1 Delta-Hedged Short Straddle 14 3.2.2 Delta-Hedged Short Put 14 3.2.3 Dynamic Position Sizing 15 3.2.4 Time Range 15 3.2.5 NAV and Performance Tracking 16 3.3 Target Variable: VRP Profitability 16 3.3.1 Method 1: Absolute Strategy Returns 17 3.3.2 Method 2: Excess Returns over the S&P500 17 3.4 Feature Engineering 18 3.5 Model Selection 19 3.5.1 Ridge Regression 19 3.5.2 Random Forest 19 3.5.3 Gradient Boosting 20 3.5.4 Rationale for Model Selection 20 3.6 Feature Selection 21 3.6.1 Tree-Based Models: Random Forest and Gradient Boosting 21 3.6.2 Ridge Regression 22 4 Empirical Results 23 4.1 Overview of Experimental Results 23 4.2 Ridge Regression Results 26 4.2.1 Straddle Strategy 26 4.2.2 Put Strategy 31 4.3 Random Forest Results 34 4.3.1 Straddle Strategy 34 4.3.2 Put Strategy 40 4.4 Gradient Boosting Results 46 4.4.1 Straddle Strategy 46 4.4.2 Put Strategy 52 4.5 Model Comparison and Interpretation 58 4.5.1 Predictive Accuracy 58 4.5.2 Market Performance 59 4.5.3 Unexpected Strength of Ridge Regression for Put Strategies 60 4.5.4 Feature Interpretability and Strategy Alignment 61 4.5.5 Practical Takeaways 62 5 Conclusion 63 5.1 Summary of Findings 63 5.2 Practical Implications 64 5.3 Future Research Directions 65 5.4 Concluding Remarks 66 References 68 | - |
| dc.language.iso | en | - |
| dc.subject | 波動風險溢酬 | zh_TW |
| dc.subject | 選擇權策略 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 行為金融 | zh_TW |
| dc.subject | 總體經濟變數 | zh_TW |
| dc.subject | Machine Learning | en |
| dc.subject | Volatility Risk Premium | en |
| dc.subject | Option Strategies | en |
| dc.subject | Macroeconomic Variables | en |
| dc.subject | Behavioral Finance | en |
| dc.title | 機器學習在波動風險溢酬策略中的應用探討 | zh_TW |
| dc.title | A Machine Learning Approach to Volatility Risk Premium Strategies | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林嘉薇;朱玉芳 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Wei Lin;Yu-Fang Chu | en |
| dc.subject.keyword | 波動風險溢酬,選擇權策略,機器學習,行為金融,總體經濟變數, | zh_TW |
| dc.subject.keyword | Volatility Risk Premium,Option Strategies,Machine Learning,Behavioral Finance,Macroeconomic Variables, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202503618 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-07 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| dc.date.embargo-lift | 2025-08-22 | - |
| 顯示於系所單位： | 財務金融學系 | |
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