本研究使用 GARCH-MIDAS 模型以及傳遞熵對 2008 年 1 月 11 日至 2020 年 4 月 30 日的數據集進行了檢驗，分析了波羅的海乾散貨指數對商品期貨、匯率、股票、原油市場的外溢效果，結果顯示出波羅的海乾散貨指數外溢效果隨期 間而有所變化。波羅的海乾散貨指數的外溢效果在全樣本時期影響不顯著，但我 們在 2018 年後發生的中美貿易戰中卻產生格外顯著的關係。我們可以推斷波羅 的海乾散貨指數在金融商品市場中為短期指標而非長期指標，特別是在對經濟有 重大衝擊的事件上。此外，航運市場與匯率、農產品、原油等市場之間有著顯著 的關聯，由於船運運載的商品包括原油、農產品等皆是與美元為計價方式，美元 升值可能會導致商品價格下跌，這種匯率市場與期貨市場之間的影響揭示了金融 傳導之間的聯繫。目前現有文獻中，有關於船運市場與金融商品市場之間的文章 少之又少，本研究旨在希望透過實證研究，為波羅的海乾散貨指數對匯率、農產 品和原油期貨等市場的外溢效果提供更有力的證據。為了檢測各市場中彼此造成的波動性強烈，本研究選擇以下八個資料變數:波羅的海乾散貨運價指數、美元 指數、標普高盛商品指數、黃豆期貨、布蘭特原油期貨、調整過後的道瓊全球航 運指數、S&P 鋼鐵指數、全球碳指數。我們運用這幾種變數的月波動率對日報酬 率進行回測，來觀察彼此之間是否有外溢效果的產生，本文采用 GARCH-MIDAS 模型來測量日月波動度的影響，可以為投資者在日後的經濟衝擊影響事件中提供 有用的資訊，並作出合適的決策。

This study uses the GARCH-MIDAS model and transfer entropy to test the data set from January 11, 2008 to April 30, 2020, and analyzes the spillover of the Baltic dry bulk index to commodity futures, exchange rates, stocks, and crude oil markets The results show that the spillover effect of the Baltic Dry Bulk Index varies with the period. The spillover effect of the Baltic Dry Index was not significant during the entire sample period, but we have had a particularly significant relationship in the China-US trade war that occurred after 2018. We can infer that the Baltic Dry Index is a short-term indicator rather than a long-term indicator in the financial commodity market, especially in events that have a major impact on the economy.At present, there are very few articles on the relationship between the shipping market and the financial commodity market in the existing literature. This research aims to provide stronger evidence on the spillover effect of the Baltic Dry Index on exchange rates, agricultural products and crude oil futures through an empirical study . In order to detect the strong volatility caused by each other in each market . We use the monthly volatility of these variables to back-test the daily rate of return to observe whether there is a spillover effect between each other. This article uses the GARCH-MIDAS model to measure the impact of daily and monthly volatility, which can provide investors useful information and make appropriate decisions during future economic shocks.

一、 中文部分
1. 林宏銘 (2010)，「美元、股票市場、債券市場及商品市場之互動關係研究」， 國立成功大學財務金融研究所碩士論文。
2. 陳玉樹 (2011)，「原物料指數與股市、匯市之關聯性的研究」，國立政治大學金融研究所碩士論文。
3. 張瀞之、劉錫謙 (2012)，「時間序列方法探討波羅的海綜合運價指數與運 輸類股之研究─以美國與臺灣為研究對象」，台灣銀行季刊，第六十一卷 第二期，頁 191∼207。

二、 英文部分
Adland, R., & Strandenes, S. P. (2007). A discrete-time stochastic partial equilibrium model of the spot freight market. Journal of Transport Economics and Policy (JTEP), 41(2), 189-218.
Alexandridis, G., Kavussanos, M. G., Kim, C. Y., Tsouknidis, D. A., & Visvikis, I. D. (2018). A survey of shipping finance research: Setting the future research agenda. Transportation Research Part E: Logistics and Transportation Review, 115, 164-212.
Alizadeh, A. H. (2013). Trading volume and volatility in the shipping forward freight market. Transportation Research Part E: Logistics and Transportation Review, 49(1), 250-265.
Alizadeh, A. H., Kappou, K., Tsouknidis, D., & Visvikis, I. (2015). Liquidity effects and FFA returns in the international shipping derivatives market. Transportation Research Part E: Logistics and Transportation Review, 76, 58-75.
Allen, F., & Gale, D. (2000). Comparing financial systems: MIT press.
Andriosopoulos, K., Doumpos, M., Papapostolou, N. C., & Pouliasis, P. K. (2013). Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms. Transportation Research Part E: Logistics and Transportation Review, 52, 16-34.
Angelopoulos, J., Sahoo, S., & Visvikis, I. D. (2020). Commodity and transportation economic market interactions revisited: New evidence from a dynamic factor model. Transportation Research Part E: Logistics and Transportation Review, 133, 101836.
Antonakakis, N., & Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41, 303-319.
Arigoni, A., Newman, A., Turner, C., & Kaptur, C. (2017). Optimizing global thermal coal shipments. Omega, 72, 118-127.
Aron, A., Aron, E. N., Tudor, M., & Nelson, G. (1991). Close relationships as including other in the self. Journal of personality and social psychology, 60(2), 241.
Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH‐MIDAS approach. Journal of Forecasting, 32(7), 600-612.
Avramov, D. (2002). Stock return predictability and model uncertainty. Journal of Financial Economics, 64(3), 423-458.
Baumeister, C., & Kilian, L. (2016). Understanding the Decline in the Price of Oil since June 2014. Journal of the Association of Environmental and resource economists, 3(1), 131-158.
Beltratti, A., & Morana, C. (2006). Breaks and persistency: macroeconomic causes of stock market volatility. Journal of econometrics, 131(1-2), 151-177.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Boltzmann, L. (1877). On the relation between the second law of the mechanical theory of heat and the probability calculus with respect to the theorems on thermal equilibrium. Kais Akad Wiss Wien Math Natumiss Classe, 76, 373-435.
Buxton, I. (1991). The market for ship demolition. Maritime Policy & Management, 18(2), 105-112.
Chen, S., Meersman, H., & Van de Voorde, E. (2010). Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets. Maritime Economics & Logistics, 12(1), 65-90.
Choi, K.-H., & Kim, D.-Y. (2018). Relationship between Baltic Dry Index and Crude Oil Market. Journal of Korea Port Economic Association, 34(4), 125-140.
Clausius, R. (1865). Über verschiedene für die Anwendung bequeme Formen der Hauptgleichungen der mechanischen Wärmetheorie. Annalen der Physik, 201(7), 353-400.
Conrad, C., Custovic, A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.
Conrad, C., & Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models. Journal of Applied Econometrics, 35(1), 19-45.
Conrad, C., Loch, K., & Rittler, D. (2014). On the macroeconomic determinants of long-term volatilities and correlations in US stock and crude oil markets. Journal of Empirical Finance, 29, 26-40.
Daugherty, M. S., & Jithendranathan, T. (2015). A study of linkages between frontier markets and the US equity markets using multivariate GARCH and transfer entropy. Journal of Multinational Financial Management, 32, 95-115.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: An intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13.
Dorion, C. (2016). Option valuation with macro-finance variables. Journal of Financial and Quantitative Analysis, 51(4), 1359-1389.
Drobetz, W., Schilling, D., & Tegtmeier, L. (2010). Common risk factors in the returns of shipping stocks. Maritime Policy & Management, 37(2), 93-120.
Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42(6), 1137-1159.
Engelen, S., Meersman, H., & Voorde, E. V. D. (2006). Using system dynamics in maritime economics: an endogenous decision model for shipowners in the dry bulk sector. Maritime Policy & Management, 33(2), 141-158.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.
Engle, R. F., & White, H. (1999). Cointegration, causality, and forecasting: a Festschrift in Honour of Clive WJ Granger: Oxford University Press on Demand.
Ghysels, E., Kvedaras, V., & Zemlys-Balevičius, V. (2020). Mixed data sampling (MIDAS) regression models. In Handbook of Statistics (Vol. 42, pp. 117-153): Elsevier.
Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53-90.
Giannarakis, G., Lemonakis, C., Sormas, A., & Georganakis, C. (2017). The effect of Baltic Dry Index, gold, oil and usa trade balance on dow jones sustainability index world. International Journal of Economics and Financial Issues, 7(5), 155.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
Graham, M., Peltomäki, J., & Piljak, V. (2016). Global economic activity as an explicator of emerging market equity returns. Research in International Business and Finance, 36, 424-435.
Greenwood, R., & Hanson, S. G. (2015). Waves in ship prices and investment. The Quarterly Journal of Economics, 130(1), 55-109.
Gu, Y., Dong, X., & Chen, Z. (2020). The relation between the international and China shipping markets. Research in Transportation Business & Management, 100427.
Han, L., Jin, J., Wu, L., & Zeng, H. (2019). The volatility linkage between energy and agricultural futures markets with external shocks. International Review of Financial Analysis.
Han, L., Wan, L., & Xu, Y. (2020). Can the Baltic Dry Index predict foreign exchange rates? Finance Research Letters, 32, 101157.
Jin, X., Lin, S. X., & Tamvakis, M. (2012). Volatility transmission and volatility impulse response functions in crude oil markets. Energy Economics, 34(6), 2125-2134.
Jordan, S. J., Vivian, A., & Wohar, M. E. (2016). Can commodity returns forecast Canadian sector stock returns? International Review of Economics & Finance, 41, 172-188.
Kalouptsidi, M. (2014). Time to build and fluctuations in bulk shipping. American Economic Review, 104(2), 564-608.
Kavussanos, M., Visvikis, I., & Dimitrakopoulos, D. (2010). Information linkages between Panamax freight derivatives and commodity derivatives markets. Maritime Economics & Logistics, 12(1), 91-110.
Kavussanos, M. G. (1996). Comparisons of volatility in the dry-cargo ship sector: Spot versus time charters, and smaller versus larger vessels. Journal of Transport economics and Policy, 67-82.
Kavussanos, M. G. (1997). The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector. Applied Economics, 29(4), 433-443.
Kavussanos, M. G., Visvikis, I. D., & Dimitrakopoulos, D. N. (2014). Economic spillovers between related derivatives markets: The case of commodity and freight markets. Transportation Research Part E: Logistics and Transportation Review, 68, 79-102.
Kilian, L., & Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267-1287.
Knapp, S., Kumar, S. N., & Remijn, A. B. (2008). Econometric analysis of the ship demolition market. Marine Policy, 32(6), 1023-1036.
Kullback, S. (1997). Information theory and statistics: Courier Corporation.
Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of econometrics, 54(1-3), 159-178.
Lee, C.-S., Chung, C.-C., Lee, H.-S., Gan, G.-Y., & Chou, M.-T. (2016). An interval-valued fuzzy number approach for supplier selection. Journal of Marine Science and Technology, 24(3), 384-389.
Lee, S.-S., Lee, J.-K., Park, B.-J., Lee, D.-K., Kim, S.-Y., & Lee, K.-H. (2006). Development of internet-based ship technical information management system. Ocean engineering, 33(13), 1814-1828.
Li, J., Liang, C., Zhu, X., Sun, X., & Wu, D. (2013). Risk contagion in Chinese banking industry: A Transfer Entropy-based analysis. Entropy, 15(12), 5549-5564.
Lin, A. J., Chang, H. Y., & Hsiao, J. L. (2019). Does the Baltic Dry Index drive volatility spillovers in the commodities, currency, or stock markets? Transportation Research Part E: Logistics and Transportation Review, 127, 265-283.
Lopez, C., & Delatte, A.-L. (2013). Commodity and equity markets: Some stylized facts from a copula approach.
López, R. (2014). Volatility contagion across commodity, equity, foreign exchange and Treasury bond markets. Applied Economics Letters, 21(9), 646-650.
Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
Merikas, A. G., Merika, A. A., & Koutroubousis, G. (2008). Modelling the investment decision of the entrepreneur in the tanker sector: choosing between a second-hand vessel and a newly built one. Maritime Policy & Management, 35(5), 433-447.
Mo, D., Gupta, R., Li, B., & Singh, T. (2018). The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets. Economic Modelling, 70, 543-560.
Morales, L., & Andreosso-O'Callaghan, B. (2014). The global financial crisis: World market or regional contagion effects? International Review of Economics & Finance, 29, 108-131.
Olson, E., Vivian, A. J., & Wohar, M. E. (2014). The relationship between energy and equity markets: Evidence from volatility impulse response functions. Energy Economics, 43, 297-305.
Ou, W., Zhou, B., Shen, J., Lo, T. W., Lei, D., Li, S., . . . Lu, J. (2020). Thermal and Nonthermal Effects in Plasmon‐Mediated Electrochemistry at Nanostructured Ag Electrodes. Angewandte Chemie International Edition, 59(17), 6790-6793.
Paas, T., & Kuusk, A. (2012). Contagion of financial crises: what does the empirical evidence show? Baltic Journal of Management.
Pan, Z., Wang, Y., Wu, C., & Yin, L. (2017). Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model. Journal of Empirical Finance, 43, 130-142.
Papapostolou, N. C., Pouliasis, P. K., & Kyriakou, I. (2017). Herd behavior in the drybulk market: an empirical analysis of the decision to invest in new and retire existing fleet capacity. Transportation Research Part E: Logistics and Transportation Review, 104, 36-51.
Paye, B. S. (2012). ‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106(3), 527-546.
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
Priyankara, E. (2018). Services Exports and Economic Growth in Sri Lanka: Does the Export-Led Growth Hypothesis Hold for Services Exports? Journal of Service Science and Management, 11(04), 479.
Rose, A., & Glick, R. (1998). Contagion and Trade: Why are Currency Crises Regional.
Schreiber, T. (2000). Measuring information transfer. Physical review letters, 85(2), 461.
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.
Shannon, C. E. (1951). Prediction and entropy of printed English. Bell system technical journal, 30(1), 50-64.
Silvennoinen, A., & Thorp, S. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42-65.
Skiadopoulos, G. (2013). Advances in the commodity futures literature: A review. The journal of Derivatives, 20(3), 85-96.
Stopford, M. (2008). Maritime economics 3e: Routledge.
Sun, X., Liu, C., Wang, J., & Li, J. (2020). Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach. International Review of Financial Analysis, 101453.
Tan, X., & Ma, Y. (2017). The impact of macroeconomic uncertainty on international commodity prices. China Finance Review International.
Tola, A., & Wälti, S. (2015). Deciphering financial contagion in the euro area during the crisis. The Quarterly Review of Economics and Finance, 55, 108-123.
Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455-1508.
Wijnolst, N., & Wergeland, T. (2009). Shipping innovation: IOS Press.
Yolland, J. B. (1979). Ship finance and Euro-markets. Maritime Policy & Management, 6(3), 175-181.