臺灣博碩士論文加值系統

這篇博士論文從不同的角度，利用Diebold和Yilmaz（2009、2012）以及Baruník等人（2017）提出的一系列指標，探討了金融市場的動態聯繫。
在研究的第一部分中，我們利用2013年至2020年的特定國家的股票交易所交易基金，量化並研究了由於美中貿易戰引起的正面和負面波動產生的方向性和非對稱擴散效應對它們的共同貿易夥伴的影響。我們發現美國和日本是擴散傳輸中的顯著貢獻者，具有可觀的淨擴散，而其他貿易夥伴，甚至包括中國，對這些擴散表現出不同程度的脆弱性。我們提供充分的證據表明市場在一個獨特層次上存在非對稱的擴散聯繫。同樣，正面和負面波動的擴散在不同市場中傳播的幅度隨時間顯著變化。我們還展示負面擴散往往具有可觀的幅度，但它們並不總是壓倒正面擴散。我們發現，在研究期間，美中貿易戰及其共同貿易夥伴的一般聯繫顯著增加。
第二部分研究了在不確定時期加密貨幣市場內的擴散效應。我們運用廣義預測誤差方差分解，捕捉回報、波動性和流動性的聯繫。此外，我們分析了這些擴散效應的決定因素，重點關注基於不同類型的不確定性變數的變化。雖然加密貨幣的數據趨勢相似，但流動性聯繫的幅度遠低於回報和波動性。值得注意的是，每日數據趨勢的圖表顯示了加密貨幣相互聯繫程度的提升，這是由它們不斷增長的受歡迎度推動的趨勢。我們顯示Litecoin是回報擴散的主要傳輸者，而Bitcoin是波動性和流動性擴散的最大傳輸者。我們進一步應用迴歸模型來解釋這種聯繫，考慮了經濟和金融市場的共同不確定性。值得注意的是，我們顯示隨著經濟不確定性的降低，加密貨幣之間的聯繫程度增加。這突顯了加密貨幣作為對抗基本不確定性的替代金融工具的潛力。

This dissertation examines the dynamic connectedness within financial markets from different perceptions in two essays using the methods developed by Diebold and Yilmaz (2009, 2012) and Baruník et al. (2017). In the first essay, we quantify and examine the effect of directional and asymmetric spillovers emerging from good and bad volatility due to the United States-China trade war on their common trade partners by using the country-specific equity exchange-traded funds (ETFs) from 2013 to 2020. We show that the United States and Japan emerge as significant contributors to spillover transmission, with substantial net spillovers. Conversely, the other trade partners, and even China, exhibit varying degrees of vulnerability to these spillovers. We offer adequate evidence for asymmetric spillover connectivity of markets at a certain level. Similarly, the spillovers of good and bad volatility vary in size and alter widely as time goes by in different markets. We also show that, while negative spillovers are frequently significant, they do not always outweigh good spillovers. We find that the general connectedness of the United States-China trade war and their common trade partners substantially increased during the period of this study.
The second essay investigates the spillover effects of return, volatility, and liquidity within the cryptocurrency market during uncertain times. Furthermore, we analyze the determinants of these spillover effects, with a focus on their variations based on different types of uncertainty variables. The empirical results show that the scale of liquidity connectedness stays much lower than that of return and volatility. Litecoin is the dominant transmitter of return spillovers while Bitcoin is the largest transmitter of volatility and liquidity spillovers. We further apply a regression model to explain such connectedness with common economic and financial market uncertainty. Notably, we show that as economic uncertainty decreases, the connectedness among cryptocurrencies increases. This underscores the potential of cryptocurrencies as a substitute financial instrument for hedging against essential uncertainties.

Title page i
Letter of Approval ii
Abstract in Chinese iii
Abstract in English v
Acknowledgement vii
Table of Contents viii
List of Tables x
List of Figures xi
Chapter 1. Introduction 1
Chapter 2. Directional and asymmetric spillover connectedness effects of the United States-China trade war on their common trade partners. 3
2.1 Introduction and Literature Review 3
2. 2 Methodology 6
2.2.1 Measure realized variance and semivariances 6
2.2.2 Measure of volatility spillovers 7
2.2.3 Measure of asymmetric spillovers 9
2.2.4 Measure of directional spillover asymmetry 10
2.3 Data description and preliminary analysis 11
2.3.1 Summary statistics 12
2.4 Empirical results 12
2.4.1 Full-sample connectedness matrix of the realized spillover of the country ETFs 13
2.4.2 Net directional and pairwise realized volatility spillover analyses of the country ETFs 15
2.4.3 Spillover asymmetry analysis 17
2.4.4 Net directional realized spillovers of the country ETFs 18
2.4.5 Spillover asymmetry of realized spillovers of the country ETFs 18
2.4.6 Directional asymmetry spillovers from/to good/bad volatility 19
2.4.7 The net pairwise realized asymmetry spillovers from the United States to China 20
2.4.8 Net pairwise realized spillovers from the United States and China to their trade partners 21
2.5 Conclusions 22
Chapter 3. Cryptocurrency market spillover in times of uncertainty 23
3.1 Introduction and Literature Review 23
3.2 Methodology 28
3.2.1 Liquidity measure 28
3.2.2 Spillover model 30
3.2.3 Regression model 32
3.5 Data Description and Empirical Results 33
3.5.1 Data Description 33
3.5.2 Daily return, Volatility, and Liquidity of cryptocurrencies analyses 34
3.5.3 Total return, volatility, and liquidity connectedness analyses for cryptocurrencies 36
3.5.4 Full-sample and net pairwise return connectedness analyses for cryptocurrencies 37
3.5.5 Full-sample and net pairwise volatility connectedness analyses for cryptocurrencies 39
3.5.6 Full-sample and net pairwise liquidity connectedness analyses for cryptocurrencies 40
3.5.7 Regression analyses for spillover effects of cryptocurrencies 42
3.6 Conclusion 45
References 47

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