Simulating the Tanker shipping market with the use of combined Generalized Autoregressive Conditional Heteroscedasticity and auto regressive moving average models (GARCH-ARMA)

Lyridis, DV; Zacharioudakis, PG; Panagopoulos, NSV

dc.contributor.author	Lyridis, DV	en
dc.contributor.author	Zacharioudakis, PG	en
dc.contributor.author	Panagopoulos, NSV	en
dc.date.accessioned	2014-03-01T02:50:12Z
dc.date.available	2014-03-01T02:50:12Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34952
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-77957961216&partnerID=40&md5=833d47f5117875886c80180e6b023684	en
dc.subject.other	ARMA model	en
dc.subject.other	Auto-regressive moving average model	en
dc.subject.other	Cash flow models	en
dc.subject.other	Data preprocessing	en
dc.subject.other	Financial time series	en
dc.subject.other	Generalized autoregressive conditional heteroscedasticity	en
dc.subject.other	Heteroscedastic	en
dc.subject.other	Keypoints	en
dc.subject.other	Management problems	en
dc.subject.other	Monte carlo simulation technique	en
dc.subject.other	Numerical simulation	en
dc.subject.other	Option pricing	en
dc.subject.other	Path generators	en
dc.subject.other	Seasonality	en
dc.subject.other	Shipping market	en
dc.subject.other	Stationary time series	en
dc.subject.other	Suezmax tanker	en
dc.subject.other	Time fields	en
dc.subject.other	Very large crude carriers	en
dc.subject.other	Commerce	en
dc.subject.other	Computer simulation	en
dc.subject.other	Economics	en
dc.subject.other	Finance	en
dc.subject.other	Financial data processing	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Oil tankers	en
dc.subject.other	Profitability	en
dc.subject.other	Risk analysis	en
dc.subject.other	Risk management	en
dc.subject.other	Ships	en
dc.subject.other	Time series	en
dc.subject.other	Cost benefit analysis	en
dc.title	Simulating the Tanker shipping market with the use of combined Generalized Autoregressive Conditional Heteroscedasticity and auto regressive moving average models (GARCH-ARMA)	en
heal.type	conferenceItem	en
heal.publicationDate	2005	en
heal.abstract	In this paper, we propose an innovative methodology framework in order to validate financial projects in Tanker shipping market. In this context, we study the behavior of the profitability of the Very Large Crude Carriers. It is a fact that data of VLCC Earnings in time field are formed in clusters of volatility hence a heteroscedastic nature is present. We construct a path generator by implementing the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) on data, which was introduced by Bollerslev and Engle and which can be used to simulate financial time series. In order to apply composite GARCH and ARMA models, data preprocessing was necessary. Therefore, we extracted seasonality patterns, we followed solid techniques to remove trend and finally a transformed stationary time series was obtained. We calculate models parameters and select the most accurate by using error criteria. A dedicated cash flow model is constructed and Monte Carlo simulation technique is performed by applying numerous earnings realizations and producing financial outcomes over future time. Various financial projects - e.g. buy a VLCC or two Suezmax Tankers, a new building or a secondhand and at what price - can be validated with numerical simulation. The described methodology is the initial key point towards cost benefit analysis, option pricing, future freight agreements and generally risk management problems in Tanker shipping market.	en
heal.journalName	1st International Symposium on Ship Operations, Management and Economics 2005	en
dc.identifier.spage	95	en
dc.identifier.epage	101	en