Long Memory Analysis of Tanker Freight Rates

油輪運費率的長期記憶分析

10.6665/JLYIT.2013.12.61

張超琦(Chao-Chi Chang)

油輪運費率 ； 長期記憶 ； 波動 ； 厚尾 ； Tanker Freight Rates ； Long Memory ； Volatilities ； Fat Tails

蘭陽學報

12期（2013 / 06 / 01）

61 - 77

英文

本文旨在探究油輪運費率的長期記憶現象。運用GPH、GSP檢定及HYGARCH、FIEGARCH長期記憶GARCH模型來檢視。研究結果顯示，採用偏態t分配的長期記憶GARCH模型可能對於油輪運費率較能精確估計，並且提升長期預測與定價的精確性。因此，對於油輪運費率的風險估計，應將其長期記憶現象納入考量，同時所採用的GARCH模型應能一併考量波動的叢聚現象、不對稱性、厚尾及長期記憶等因素。這些結果可以應用在實務界從事油輪運費市場之風險管理。

This study aims to investigate the features of tanker freight rates when there is a long memory effect. We employed the GPH test, the GSP test, the HYGARCH and the FIEGARCH models for the long memory test and estimation. Our results suggest that precise estimates of tanker freight rates may be acquired from a long memory in volatility models with the skewed Student-t distribution. Such models improve the long-term volatility forecast and produce more precise pricing of tanker freight contracts. Moreover, for the appropriate risk evaluation of tanker freight rates, the degree of persistence should be examined and modelling that includes volatility clustering, asymmetry, leptokurtosis and long range dependence should be considered. Therefore, we could extend these findings to risk management in the tanker freight markets.

1. Baillie, R.T.,Bollerslev, T.,Mikkelsen, H.O.(1996).Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity.Journal of Econometrics,73(1),151-184.
2. Bollerslev, T.(1986).Generalized autoregressive conditional heteroskedasticity.Journal of Econometrics,31,307-327.
3. Bollerslev, T.,Mikkelsen, H.O.(1996).Modelling and Pricing Long Memory in Stock Market Volatility.Journal of Econometrics,11(5),447-469.
4. Chen, Y.S.,Wang, S.T.(2004).The empirical evidence of the leverage effect on volatility in international bulk shipping market.Maritime Policy & Management,31(2),109-124.
5. Davidson, J.(2004).Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model.Journal of Business & Economic Statistics,22(1),16-29.
6. Fang, W.(2007).Analysis on long memory of the volatilities of international dry bulk freight index using fractal theory.Wireless Communications, Networking and Mobile Computing in Shanghai
7. Geweke, J.,Porter-Hudak, S.(1983).The estimation and application of long memory time series models.Journal of time series analysis,4(4),221-238.
8. Goulielmos, A.M.,Psifia, M.(2006).Shipping finance: time to follow a new track?.Maritime Policy & Management,33(3),301-320.
9. Gu, X.B.,Li, X.Y.(2009).Empirical Analysis on Long Memory Property of Baltic Dry Index.Journal of Shanghai Maritime University,30(1),40-44.
10. Kavussanos, M.G.(1996).Price risk modelling of different size vessels in tanker industry using Autoregressive Conditional Heteroscedasticity (ARCH) models.Logistics and Transportation Review,32(2),161-176.
11. Kavussanos, M.G.,Dimitrakopoulos, D.N.(2011).Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets.International Review of Financial Analysis,20(5),258-268.
12. Nelson, D.B.(1991).Conditional Heteroskedasticity in Asset Returns: A New Approach.Econometrica,59(2),347-370.
13. Phillips, P.C.B.,Shimotsu, K.(2004).Local Whittle estimation in nonstationary and unit root cases.The Annals of Statistics,32(2),656-692.
14. Robinson, P.M.(1995).Gaussian Semiparametric Estimation of Long-Range Dependence.Annals of Statistics,23,1630-1661.
15. Robinson, P.M.,Henry, M.(1999).Long and Short Memory Conditional Heteroskedasticity in Estimating the Memory Parameter in Levels.Economic Theory,15,299-336.
16. Robinson, P.M.,Zaffaroni, P.(1998).Nonlinear time series with long memory: a model for stochastic volatility.Journal of Statistical Planning and Inference,68(2),359-371.
17. Stopford, M.(2009).Maritime Economics.Oxon:Routledge.

1. Chang, Chao-chi(2016).The Return-Risk Tradeoff Relationship of Tanker Shipping Freight Indices.蘭陽學報,15,1-11.