臺灣博碩士論文加值系統

此論文探討了在重大事件發生後(本篇包含COVID-19等事件)，散裝航運市場、國際商品市場及情緒指標對於美國掛牌散裝航運公司股價所產生的波動性外溢效果。採用 GARCH-MIDAS 模型進行實證分析，樣本期間從 2018 年 3 月 1 日至 2022 年 12 月 30 日，並以美國成為世界上確診COVID-19病例最多的國家之日期2022 年 4 月 10 日為斷點，分為COVID-19前後期。結果顯示，散裝航運市場與國際商品市場的指標在事件後對多數公司股價都產生了波動外溢。情緒指標方面，VIX及FSI對少數公司股價產生外溢效果，PCR只在短期對散裝航運公司股價有波動外溢，TSI則對大部份公司股價都產生了波動外溢。本研究有助於機構投資人藉由波動外溢的結果來決定其投資標的及進出場時機，以期賺取超額報酬。

This paper investigates the volatility spillover effects of the dry bulk shipping market, international commodity market, and sentiment indicators on the stock prices of US-listed bulk shipping companies after major events, including COVID-19. The empirical analysis is conducted using the GARCH-MIDAS model, with a sample period from March 1, 2018, to December 30, 2022. The breakpoint is set at April 10, 2022, when the US became the country with the highest confirmed COVID-19 cases worldwide, dividing the period into pre- and post-COVID-19 periods. The results show that the indicators of the dry bulk shipping market and international commodity market exhibit volatility spillovers to the stock prices of most companies after the event. As for sentiment indicators, VIX and FSI have spillover effects on the stock prices of a few companies, PCR has short-term volatility spillovers on bulk shipping company stocks, and TSI has volatility spillovers on the stock prices of most companies. This study provides insights for institutional investors to determine their investment targets and entry and exit timing based on the results of volatility spillovers, aiming to earn excess returns.

CHAPTER 1. INTRODUCTION 8
1.1 Research Background and Motivation 8
1.2 Investor Sentiment, International Commodity Market and Bulk Shipping Market 9
1.3 Research Purpose 10
CHAPTER 2. LITERATURE REVIEW 12
2.1 Bulk Shipping Market Literature 12
2.2 International Commodity Market Literature 14
2.3 Financial Contagion and Sentiment Proxies Literature 16
2.3.1 Financial Contagion 16
2.3.2 Sentiment Proxies 17
2.4 GARCH-MIDAS Literature 19
2.5 Research Hypothesis 21
CHAPTER 3. DATA AND METHODOLOGY 22
3.1 Data Description 22
3.1.1 Dry Bulk Shipping Companies 24
3.1.2 Baltic Shipping Market 25
3.1.3 International Commodity Market Futures 27
3.1.4 Sentiment Proxies 28
3.2 Stationarity and Unit Root Test 29
3.2.1 ADF Test 29
3.2.2 PP Test 30
3.2.3 Heteroscedasticity Test 31
3.3 GARCH MIDAS Model 32
CHAPTER 4. EMPIRICAL RESULTS AND ANALYSIS 34
4.1 Descriptive Statistics 34
4.2 Results of Unit Root Test and Heteroskedasticity Test 54
4.3 Result of GARCH MIDAS Model 55
4.3.1 Before COVID-19 55
4.3.2 After COVID-19 66
4.4 The Finding of Empirical Research 78
CHAPTER 5. CONCLUSION 85
5.1 Research finding 85
5.2 Implications of Investment 88
5.3 Research Limitations and Future Research 90

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