Dual Long Memory Properties in Marine Freight Rates
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
This paper examines the long memory properties in three indices of the marine freight transportation market. In particular, the existence of the long memory property in the level of returns and volatility is the focus of the study. For this purpose, the ARFIMA, ARFIMA–GARCH, and ARFIMA–FIGARCH models were estimated under the assumption of normal and skewed Student–t distributions. The empirical analysis of this study provides some interesting results: First, the results of the ARFIMA model indicate the existence of long memory, implying possible inefficiencies in the Baltic freight transportation market. Second, the results of the ARFIMA–GARCH model indicate strong persistence in the volatility and a weakening presence of long memory in the returns. Third, the results of the ARFIMA–FIGARCH model show a strong long memory in the volatility of the indices examined. Only the Baltic Dry Index (BDI) case exhibits the dual long memory property in the level of returns and volatility. Fourth, a skewed Student–t distribution may be appropriate to capture the tendency of residual distributions referring to asymmetry and leptokurtosis. We think that the models proposed in this study and the empirical results are useful to predict the dynamics of the marine freight rates.
Abstract
This paper examines the long memory properties in three indices of the marine freight transportation market. In particular, the existence of the long memory property in the level of returns and volatility is the focus of the study. For this purpose, the ARFIMA, ARFIMA–GARCH, and ARFIMA–FIGARCH models were estimated under the assumption of normal and skewed Student–t distributions. The empirical analysis of this study provides some interesting results: First, the results of the ARFIMA model indicate the existence of long memory, implying possible inefficiencies in the Baltic freight transportation market. Second, the results of the ARFIMA–GARCH model indicate strong persistence in the volatility and a weakening presence of long memory in the returns. Third, the results of the ARFIMA–FIGARCH model show a strong long memory in the volatility of the indices examined. Only the Baltic Dry Index (BDI) case exhibits the dual long memory property in the level of returns and volatility. Fourth, a skewed Student–t distribution may be appropriate to capture the tendency of residual distributions referring to asymmetry and leptokurtosis. We think that the models proposed in this study and the empirical results are useful to predict the dynamics of the marine freight rates.
참고문헌 (26)
- Baillie, R. T., "Long Memory Processes and Fractional Integration in Econometrics", Journal of Econometrics, 73(1), pp.5~59 (1996)
- Baillie, R. T., "Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity", Journal of Econometrics, 74(1), pp.3~30 (1996)
- Baillie, R. T., "Further Long Memory Properties of Inflationary Shocks", Southern Economic Journal, 68(3), pp.496~510 (2002)
- Baillie, R. T., "Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach", Journal of Economic Dynamics and Control, 33(1), pp.1577~1592 (2009)
- Batchelor, R., "Forecasting Spot and Forward Prices in the International Freight Market", International Journal of Forecasting, 12(1), pp.101~114 (2007)
- Beine, M., "Central Bank Interventions and Jumps in Double Long Memory Models of Daily Exchange Rates", Journal of Empirical Finance, 10(5), pp.641~660 (2003)
- Beine, M., "Accounting for Conditional Leptokurtosis and Closing Days Effects in FIGARCH Models of Daily Exchange Rates", Applied Financial Economics, 12(8), pp.589~600 (2002)
- Bollerslev, T., "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances", Econometric Reviews, 11(2), pp.143~172 (1992)
- Cheung, Y. W., "Tests for Fractional Integration: A Monte Carlo Investigation", Journal of Time Series Analysis, 14, pp.331~345 (1993)
- Cullinane, K. P. B., "A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited", International Journal of Maritime Economics, 1(2), pp.15~39 (1999)
- Ding, Z., "A Long Memory Property of Stock Returns and a New Model", Journal of Empirical Finance, 1(1), pp.83~106 (1993)
- Granger, C. W. J., "Long Memory Relationships and the Aggregation of Dynamic Models", Journal of Econometrics, 14(2), pp.227~238 (1980)
- Granger, C. W. J., "Occasional Structural Breaks and Long Memory", Journal of Empirical Finance, 11(3), pp.399~421 (2004)
- Granger, C. W. J., "An Introduction to Long Memory Time Series Models and Fractional Differencing", Journal of Time Series Analysis, 1, pp.5~39 (1980)
- "The Best Economic Indicator You’ve Never Heard of"[URL]
- Hosking, J. R. M., "Fractional Differencing", Biometrika, 68(1), pp.165~176 (1981)
- Kang, S. H., "Long Memory Properties in Return and Volatility: Evidence from the Korean Stock Market", Physica A, 385(2), pp.591~600 (2007)
- Karlaftis, M. G., "Memory Properties and Fractional Integration in Transportation Time-Series", Transportation Research Part C, 17(4), pp.444~453 (2009)
- Kasman, A., "Dual Long Memory Property in Returns and Volatility: Evidence from the CEE Countries' Stock Markets", Emerging Markets Review, 10(2), pp.122~139 (2009)
- Kavussanos, M. G., "The Expectations Hypothesis of the Term Structure and Risk Premiums in Dry Bulk Shipping Freight Markets", Journal of Transport Economics and Policy, 36(2), pp.267~304 (2002)
- Kavussanos, M. G., "Market Interactions in Returns and Volatilities between Spot and Forward Shipping Freight Markets", Journal of Banking & Finance, 28(8), pp.2015~2049 (2004)
- Lambert, P., "Modelling Financial Time Series using GARCH-Type Models and a Skewed Student Density" (2001), Université de Liège
- Qu, L., "Time Series Forecasting Model with Error Correction by Structure Adaptive RBF Neural Network", Proceedings of the 6th World Congress on Intelligent Control and Automation (2006)
- Sengupta, R., "Recent Movements in the Baltic Dry Index", Economic Synopses, 12(1) (2009), Federal Reserve Bank of St. Louis
- Taylor, Stephen, "Modelling Financial Time Series" (1986), John Wiley & Sons
- Tvedt, J., "A New Perspective on Price Dynamics of the Dry Bulk Market", Maritime Policy & Management, 30(3), pp.221~230 (2003)