本論文以美國、歐洲、日本及香港等四國之恐慌指數及其對應之指數為軸，以三種角度切入研究新冠疫情對金融市場之影響。首先以多變量GARCH模型研究恐慌指數於各國間之外溢效果，分析各國恐慌指數於新冠疫情前後之連動性差異；第二，使用縱橫資料門檻模型為研究方法，以門檻效果之差異判別股市對恐慌指數之反應變化；最後，針對GARCH(1,1)及RiskMetrics等兩種常用之風險價值評估模型，分析其於新冠疫情後之效用變化。第一部份之實證結果顯示，在外溢效果之研究中，各國之恐慌指數除歐洲波動率指數指數於疫情前外，與前一交易日相比皆有顯著之收斂效果。另外，與疫情前相比，各國恐慌指數間之連動性於疫情後有顯著提升之現象。顯著相關之組合由8組提升至9組外，正向影響更由3組提升至6組，顯示恐慌指數之敏感性及外溢效果，經新冠疫情之衝擊後有顯著提升。第二部份，於門檻效果之研究之中，本論文發現恐慌指數變動在股市交易日對於隔日報酬率影響具有門檻效果，但效果僅在疫情前存在，疫情後無顯著門檻。而當日恐慌指數變動，於疫情前後皆對當日股市報酬率有顯著負向影響，但其影響性於疫情後較弱。另外，恐慌指數變動在股市交易日對於隔日之成交量影響亦具有門檻效果，而其門檻值於疫情後有所下降。顯示市場投資者因應恐慌指數變化而進行停損或是停利的行為較為溫和。第三部份之實證結果顯示，因投資市場因疫情產生結構性改變，GARCH(1,1)模型之風險價值評估效果並無法達到標準。而RiskMetrics模型將恐慌指數代入後，其風險價值評估效果較佳。

As the characteristics of the fear index are informative. indicative, and objective, this study focuses on how the COVID-19 pandemic affect the performance and the application of the fear index in relation to financial markets of 4 countries from 3 perspectives, including the CBOE VIX in US, the VSTOXX in EU, the Nikkei Average Volatility Inde in Japan, and the HSI Volatility Index in Hong Kong. In part 1, the study adopts the multivariate GARCH model to investigate the fear spillover effect between four fear indices. The empirical results suggest that the fear spillover has strengthened since the COVID-19 impact as significant correlations increase from 8 to 9 combinations from 4 fear indices; moreover, the positive ones increase from 3 to 6. In part 2, the study adopts the Panel Threshold model to discover the threshold effect regarding how investors are influenced by the fear index before and after the COVID-19 impact. The empirical results indicate that the threshold effect exists in the cross-day effect from the changes in the fear index to stock returns rate only before the COVID-19 pandemic; whereas, the intraday effect exists regardless of the COVID-19 pandemic, and the effect is weaker during the pandemic. Furthermore, the threshold effect is also found in the cross-day effect regarding changes in the fear index to trade volume changes, and the threshold value is lowered during the pandemic period, suggesting that investors are relatively moderate in exiting the markets. In part 3, the two VaR models – the GARCH(1,1) and the RiskMetrics model, were examined regarding their feasibility and accuracy after the impact from the COVID-19. Due to the obvious structural change in the stock markets, the GARCH(1,1) model are found less accurate than the RiskMetrics model considering the fear index as the risk factor from the empirical results provided in this study.

CONTENTS
PART I.................	1
Abstract.................2
1. Introduction.................2
2. Literature Review.................5
3. Methodology.................8
3.1 Unit root test.................9
3.2 Cointegration test.................10
3.3 BEKK multivariate GARCH model.................12
4. Data analysis.................14
4.1 Data description.................14
4.2 Unit root tests and cointegrating test.................15
4.3 Observations among fear indices.................17
5 Multivariate GARCH(1,1) – the BEKK model.................19
5.1 Interpreting the long-term fear spillover effect.................19
5.2 Changes from the impact of the COVID-19 pandemic.................24
6. Conclusion.................27

PART II.................29
Abstract.................30
1. Introduction.................30
2. Literature Review.................34
2.1 Discovering investor sentiment and their behaviors.................34
2.2 The Convid-19 impact on investors.................35
2.3 The panel threshold model on study of investor sentiment.................36
3. Methodology.................37
3.1 Single threshold estimation and definition of variables.................37
3.2 Testing for a single threshold.................40
3.3 Asymptotic distribution of slope coefficient.................41
4. Empirical results.................42
5. Conclusion.................50

PART III.................53
Abstract.................54
1. Introduction.................54
2. Data and Methodology.................56
3. Empirical Results.................59
3.1 The historical simulation approach – the GARCH (1,1) model.................60
3.2 The RiskMetrics method.................61
4. Conclusion.................65
References.................67

LIST OF TABLES
PART I
Table 1. Descriptive Statistics of fear indices.................15
Table 2. The ADF and PP unit root tests on fear indices.................16
Table 3. Cointegrating vector test.................17
Table 4. Multivariate correlations between the VIX, VSTOXX, NKVI and VHSI.................23
Table 5. Multivariate correlations between the VIX, VSTOXX, NKVI and VHSI in the COVID-19 pandemic period.................26

PART II
Table 1. Descriptive Statistics.................47
Table 2. Threshold Estimates.................47
Table 3. Regression estimates - Return (RTN).................49
Table 4. Regression estimates - Volume (VOL).................50

PART III
Table 1. Descriptive Statistics.................59
Table 2. VaR results from the GARCH(1,1) model.................63
Table 3. VaR results from the RiskMetrics model.................64

LIST OF FIGURES
PART I
Figure 1. Daily prices of the fear indices.................19

PART II
Figure 1. Pre-COVID construction of the confidence intervals (trade volume) .................48
Figure 2. During-COVID construction of the confidence intervals (trade volume) .................48

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