The purpose of this article is to propose an integrated approach for measuring the financial risk in the shipping business. The integrated approach combines the autoregressive conditional heteroskedasticity model, historical data distribution goodness-of-fit test and Monte Carlo simulation. First, a typical forecasting based on the autoregressive conditional heteroskedasticity model is presented to evaluate future cash flows and find that it continues to fall at short periods. Second, the cash flow at-risk measurement by simulation is to understand maximum potential cash flow deficit, and through exposure to develop a risk management strategy that will enable the shipowners or operators to effectively identify, quantify and control most financial risks and exposures.
KavussanosMGVisvikisID. Market interaction in return and volatilities between spot and forward shipping freight markets. J Bank Financ2004; 28: 2015–2049.
ZacharioudakisPGLyridisDV. FORESIM: an innovative simulation technique combining stochastic models and artificial neural networks—the tanker market case. In: Proceedings of the annual conference of the International Association of Maritime Economists, International Association of Maritime Economists, IAME, Lemessos, June 23–25: 2005.
8.
LyridisDVZacharioudakisPOusantzopoulosG. A multi-regression approach to forecasting freight rates in the dry bulk shipping market using neural networks. In: Proceedings of the annual conference of the International Association of Maritime Economists, International Association of Maritime Economists, IAME, Izmir, June 30–July 2: 2004.
FajardoJFariasAHaas OrnelasJR. Goodness-of-fit test focuses on conditional value at risk: an empirical analysis of exchange rates. Braz Rev Financ2008; 6(2): 139–155.
11.
BerkowitzJ. Testing the distributional assumptions embedded in risk models. University of California, Irvine, CA, 2002, http://www.uh.edu/~jberkowi/disttest.pdf
12.
EngleRF. ARCH selected readings. New York: Oxford University Press, 1995.
13.
TsayRS. Conditional heteroskedastic time series models. J Am Stat Assoc1987; 82: 590–604.
14.
HigginsMLBeraAKLeeS. A class of nonlinear ARCH models. Unpublished manuscript, Department of Economics, University of Illinois, Champaign, IL, 1991.
15.
DieboldFXPaulyP. Has the EMS reduced member country exchange rate volatility?Empir Econ1988; 13: 81–102.
16.
BollerslevT. Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH approach. Rev Econ Stat1990; 72: 498–505.
17.
LevyPLemeshowS. Sampling of populations: methods and applications. New York: John Wiley & Sons, 1991.
18.
EvansJOlsonD. Introduction to simulation and risk analysis. 2nd ed.Upper Saddle River, NJ: Prentice Hall, 2001.
19.
FaberJMarkowskaAEyringV. A global maritime emission trading system: design and impacts on the shipping sector, countries and regions. 2010, Delft, CE Delft Report, Publication number: 10.7829.05, 2010.
20.
WangCMLiHS. Financial risk management in bulk shipping company: case of the capesize bulk carrier owners. In: Proceedings of the 9th conference of Shipping and Logistics University Network across Taiwan Strait, National Taiwan Ocean University, Keelung, Taiwan, 26 March 2012.
21.
TsolakisS. Econometric analysis of bulk shipping markets implications for investment strategies and financial decision-making. Doctoral Dissertation, Erasmus University Rotterdam, 2005.
