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研究生： 宋宜潔
Sung, Yi-Chieh
論文名稱： 透過選擇權隱含波動率曲面結構轉換偵測以選擇市場入場時機
Market Timing via Structural Transition Detection in the Options Implied Volatility Surface
指導教授： 黃仁暐
Huang, Jen-Wei
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 77
中文關鍵詞： 入場點偵測 、隱含波動率曲面結構轉換 、隱含波動率曲面 、選擇權隱含資訊
外文關鍵詞： Market Entry Point Identification, Structural Transitions in the Implied Volatility Surface, Implied Volatility Surface, Option-Implied Information
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    • 精確辨識有效的市場入場點，能用以掌握良好的風險報酬機會並限制下行風險。於適當時機點進場，能使投資人捕捉後續趨勢報酬的較大部分，同時降低對短期、反應式交易決策的依賴。雖然有部分研究專注於市場趨勢預測或市場狀態轉變偵測，這些研究通常產生較為密集的訊號，並不適合作為相對有效的入場點。為了更好的識別這些市場入場點，本文提出利用偵測隱含波動率曲面（Implied Volatility Surface, IVS）的結構轉換進行市場入場點辨識。本研究將隱含波動率曲面分解為具經濟意涵的「中央情緒（Central Sentiment）」與「邊際風險（Peripheral Risk）」兩大結構域，並萃取關鍵結構性因子，以區分市場方向性情緒與極端風險定價行為。在此基礎上，本文採用自適應、情境分群特性的動態門檻辨識隱含波動率曲面結構狀態轉換，進而產生相對稀疏且有效的市場入場訊號，明確聚焦於結構性轉折點，而非日常價格波動。實驗結果顯示，所提出之隱含波動率曲面結構化入場點辨識框架，在方向判斷準確度與下行風險控制方面，均顯著優於傳統以價格或波動率水準為基礎之基準策略，且在 2022 年空頭市場期間表現尤為突出。整體而言，本研究結果突顯結構化隱含波動率曲面分析於金融市場早期入場點辨識上的實務價值與應用潛力。

    Identifying effective market entry points is critical for capturing favorable risk-return opportunities while limiting downside exposure. Entering the market at appropriate entry points allows participants to capture a larger portion of subsequent trend returns, while reducing reliance on short-horizon, reactive trading decisions. Although several studies have addressed trend prediction or regime detection, they typically generate dense signals that are unable to isolate effective entry points. To more effectively identify such entry points, we propose a framework that leverages structural transitions of the Implied Volatility Surface (IVS). In this framework, the IVS is decomposed into economically interpretable Central Sentiment and Peripheral Risk domains, from which key structural factors are extracted to disentangle directional market sentiment from extreme risk pricing. Adaptive, regime-specific optimization is then applied to identify IVS regimes, enabling the generation of relatively sparse entry signals based on IVS state transitions and explicitly targeting effective entry points rather than day-to-day fluctuations. Experimental results demonstrate that the proposed framework achieves superior directional accuracy and downside risk control relative to conventional price-based and volatility-level benchmarks, with particularly strong performance during the 2022 bear market. These findings highlight the practical value of structurally informed IVS analysis for early-phase entry point detection in financial markets.

    中文摘要 ii Abstract iii Acknowledgment iv Table of Contents v List of Tables viii List of Figures xi 1 Introduction 1 2 Related Works 5 2.1 Trend Prediction and Directional Forecasting 5 2.2 Regime Detection 5 2.3 Event-Driven Market Signals 6 3 Background Knowledge related to Implied Volatility 7 3.1 Option-Implied Measures 7 3.2 Volatility-Based Risk Management 7 3.3 Volatility Shocks 8 4 Foundations of the Implied Volatility Surface 9 4.1 IVS Structural Indicators 9 4.2 Characterizing the Volatility Smile across Moneyness Domains 9 5 Methodology 11 5.1 Framework for Effective Entry Point Identification 11 5.2 IVS Matrix Construction 12 5.3 IVS Structural Indicators Extraction 14 5.3.1 Directional Expectation Indicators 14 5.3.2 Risk Stability Indicators 16 5.4 Effective Entry Point Identification 20 5.4.1 Regimes screening 20 5.4.2 Entry Point Identification 23 5.5 Regime-specific optimization 24 6 Experiments 26 6.1 Dataset 26 6.2 Compared Models 27 6.3 Strategy Execution and Risk Management 29 6.4 Evaluation Metrics 32 6.4.1 Signal-Level Evaluation 32 6.4.2 Strategy-Level Evaluation 33 6.5 The Performance of Bullish Entry Strategy 34 6.5.1 Directional Accuracy of Baseline Strategy 34 6.5.2 Full-Sample Trading Performance 35 6.6 Performance Stability during the 2022 Bear Market 43 6.7 Analysis of IVS Regime Combinations Across Stocks 48 6.8 Long-Short Signal Integration with Bearish Regimes 55 6.8.1 Integration of Short-Side Signals 55 6.8.2 Performance of Long-Short Strategy Across Stocks 56 7 Conclusions 60 References 61

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