IDEAS home Printed from https://ideas.repec.org/a/spd/journl/v67y2016i1p38-53.html
   My bibliography  Save this article

Measuring Tanker Market Future Risk with the use of FORESIM

Author

Listed:
  • Dimitrios Lyridis

    (National Technical University of Athens, (School of Naval Architecture and Marine Engineering))

  • Nikolaos Manos

    (National Technical University of Athens, (School of Naval Architecture and Marine Engineering))

  • Panayotis Zacharioudakis

    (National Technical University of Athens, (School of Naval Architecture and Marine Engineering))

  • Athanassios Pappas

    (National Technical University of Athens, (School of Naval Architecture and Marine Engineering))

  • Aristidis Mavris

    (Atlas Maritime)

Abstract

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of the tanker market risk. This is the first attempt to express future tanker market risk in relation to current market fundamentals. We follow a system’s analysis seeking for internal and external parameters affecting risk. Therefore we apply dynamic features in risk measurement taking into account all Tanker market characteristics and potential excitations from non-systemic parameters as well as their contribution to freight level formulation and fluctuation. In this way we are able to measure the behavior of future market risk as long as twelve months ahead with very encouraging results. The output information is therefore useful in all aspects of risk analysis and decision making in shipping markets.

Suggested Citation

  • Dimitrios Lyridis & Nikolaos Manos & Panayotis Zacharioudakis & Athanassios Pappas & Aristidis Mavris, 2017. "Measuring Tanker Market Future Risk with the use of FORESIM," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 38-53, January-M.
  • Handle: RePEc:spd:journl:v:67:y:2016:i:1:p:38-53
    as

    Download full text from publisher

    File URL: https://spoudai.unipi.gr/index.php/spoudai/article/download/2571/2629/2571-3079-1-SM
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    2. Jun Li & Michael G. Parsons, 1997. "Forecasting tanker freight rate using neural networks," Maritime Policy & Management, Taylor & Francis Journals, vol. 24(1), pages 9-30, January.
    3. Engle, Robert F. (ed.), 1995. "ARCH: Selected Readings," OUP Catalogue, Oxford University Press, number 9780198774327.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Panos K. Pouliasis & Christos Bentsos, 2024. "Oil price uncertainty and the relation to tanker shipping," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2472-2494, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michelle Sheran Sylvester, 2007. "The Career and Family Choices of Women: A Dynamic Analysis of Labor Force Participation, Schooling, Marriage and Fertility Decisions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(3), pages 367-399, July.
    2. Capps, Oral Jr. & Havlicek, Joseph Jr., 1980. "National And Regional Household Demands For Meats And Seafood In The U.S.: A Complete Systems Approach," 1980 Annual Meeting, July 27-30, Urbana-Champaign, Illinois 278409, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Delis, Manthos & Savva, Christos & Theodossiou, Panayiotis, 2020. "A Coronavirus Asset Pricing Model: The Role of Skewness," MPRA Paper 100877, University Library of Munich, Germany.
    4. Faruk, Balli, 2006. "New Patterns in International Portfolio Allocation and Income Smoothing," MPRA Paper 10121, University Library of Munich, Germany, revised 14 Aug 2008.
    5. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    6. Koutmos, Dimitrios, 2012. "An intertemporal capital asset pricing model with heterogeneous expectations," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1176-1187.
    7. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    8. Weber, Enzo, 2009. "Financial Contagion, Vulnerability and Information Flow: Empirical Identification," University of Regensburg Working Papers in Business, Economics and Management Information Systems 431, University of Regensburg, Department of Economics.
    9. Shanker, Latha & Balakrishnan, Narayanaswamy, 2005. "Optimal clearing margin, capital and price limits for futures clearinghouses," Journal of Banking & Finance, Elsevier, vol. 29(7), pages 1611-1630, July.
    10. Emilie Alberola & Julien Chevallier & Benoît Chèze, 2008. "The EU Emissions Trading Scheme : Disentangling the Effects of Industrial Production and CO2 Emissions on Carbon Prices," Working Papers hal-04140795, HAL.
    11. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    12. Tony Caporale & Barbara McKiernan, 1998. "The Fischer Black Hypothesis: Some Time‐Series Evidence," Southern Economic Journal, John Wiley & Sons, vol. 64(3), pages 765-771, January.
    13. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    14. Ahmed, Walid M.A., 2018. "On the interdependence of natural gas and stock markets under structural breaks," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 149-161.
    15. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    16. Charles K.D. Adjasi, 2009. "Macroeconomic uncertainty and conditional stock-price volatility in frontier African markets: Evidence from Ghana," Journal of Risk Finance, Emerald Group Publishing, vol. 10(4), pages 333-349, August.
    17. Kenneth Beller & John R. Nofsinger, 1998. "On Stock Return Seasonality And Conditional Heteroskedasticity," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 21(2), pages 229-246, June.
    18. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    19. Ram Bhar & Carl Chiarella, 1995. "The Estimation of the Heath-Jarrow-Morton Model by Use of Kalman Filtering Techniques," Working Paper Series 54, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    20. Abraham, Katharine G & Farber, Henry S, 1987. "Job Duration, Seniority, and Earnings," American Economic Review, American Economic Association, vol. 77(3), pages 278-297, June.

    More about this item

    Keywords

    Tanker Market; Freight Rates; Forecasting; Modeling; Simulation; Artificial Neural Networks (ANN);
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spd:journl:v:67:y:2016:i:1:p:38-53. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SPOUDAI Journal of Economics and Business (email available below). General contact details of provider: https://edirc.repec.org/data/depirgr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.