本研究探討美國公債期貨市場中的風險評估與市場行為，涵蓋兩大主題：「價格跳躍在波動率預測中的重要性」與「交易員情緒指數對市場報酬與崩盤風險的影響」。
第一部分分析不同利率環境下價格跳躍對波動率預測的影響，並透過統計與波動性時機策略兩種方法進行樣本外評估。結果顯示，納入跳躍項的模型在預測效能上顯著優於基準模型，特別是在利率急升期間，且經跳躍檢定、交易成本與再平衡測試後具穩健性。
第二部分利用CFTC的COT資料建構三類交易人的情緒指數，探討其對市場報酬與崩盤風險的影響。實證顯示，情緒指標在不同利率環境下對報酬與風險的影響具有異質性，極端情緒尤具預測意涵。

This study examines risk and behavior in the U.S. Treasury futures market, focusing on two themes: the role of price jumps in volatility forecasting and the impact of trader sentiment on returns and crash risk.
The first part evaluates how price jumps affect forecast accuracy under different rate environments, using statistical and volatility timing methods. Models with jump components outperform benchmarks, especially during sharp rate hikes, and results are robust to jump tests, transaction costs, and rebalancing.
The second part constructs sentiment indices for three trader types using CFTC’s COT data. Findings show that sentiment has heterogeneous effects on returns and crash risk across rate environments, with extreme sentiment offering strong predictive value.

TABLE OF CONTENTS
PART I	1
Abstract	2
CHAPTER 1 INTRODUCTION	3
1.1 Research Background and Motivation	3
1.2 Research Objectives and Research Questions	4
1.3 Research Methodology, Data, and Main Findings	4
CHAPTER 2 LITERATURE REVIEW	6
CHAPTER 3 Data and econometric methodology	8
3.1 Data description	8
3.2 Econometric methodology	10
3.2.1 Nonparametric jump tests	10
3.2.2 HAR-based volatility forecasting models	13
3.2.3 Economic value of volatility timing strategy	15
CHAPTER 4 Empirical results and analysis	18
4.1 Descriptive statistics of realized volatility estimators	18
4.2 HAR Statistical evaluation of volatility forecasts	21
4.3 Economic evaluation of volatility forecasts	26
4.4 Transaction cost and rebalancing strategy	29
CHAPTER 5 CONCLUSIONS	34
References	35
PART II	39
Abstract	40
CHAPTER 1 INTRODUCTION	41
1.1 Background and Market Context	41
1.2 Research Gap and Objectives	41
1.3 Contribution and Structure	43
CHAPTER 2 LITERATURE REVIEW	44
CHAPTER 3 Data and econometric methodology	46
3.1 Data	46
3.2 Variable Definitions	46
3.21 Futures Returns	46
3.22 Trader Sentiment Index	46
3.23 Extreme Sentiment Variables	47
3.3 Estimation Methodology	48
3.31 Trader Sentiment and Future Market Returns	48
3.32 Trader Sentiment and Market Crash Risk	48
3.33 Statistical Estimation and Robustness Checks	50
CHAPTER 4 Empirical results and analysis	51
CHAPTER 5 CONCLUSIONS	65
References	68

 
LIST OF TABLES

PART I
Table 1. Descriptive statistics of daily returns for 2-, 5-, 10-year Treasury note futures	10
Table 2. Descriptive statistics of realized volatility estimators for 2-, 5-, 10-year Treasury note futures	19
Table 3. Key macroeconomic events (2021-2023)	20
Table 4. Estimation results of HAR-based models	22
Table 5. Statistical evaluation of HAR-based volatility forecasts	24
Table 6. Economic values of volatility timing strategy	28
Table 7. Economic values of volatility timing strategy with transaction costs	30
Table 8. Economic values of volatility timing strategy with alternative rebalancing strategy	32
PART II
Table 1. Summary Statistics (2010.1-2024.12)	53
Table 2. Predictive Regressions of Treasury Futures Returns on Trader Sentiment Indices (2022.1–2024.8)	55
Table 3. Predictive Regressions of Crash Risk on Trader Sentiment Indices	57
Table 4. Predictive Power of Extreme Sentiment on Treasury Futures Returns During the Moderate Rate Hike Period (2016–2018)	58
Table 5. Predictive Power of Extreme Sentiment on Treasury Futures Returns During the Aggressive Rate Hike Period (2022–2023)	63

 
LIST OF FIGURES

Fig 1. Daily average trading volumes of US Treasury note futures	9

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