Studying the history of “economic turbulence”, we ended up with the certainty that the statistical methods used so far, by economists, are unsuitable to model it. We have mainly the methods of Normal distribution and Random Walk in mind. Moreover, the picture of reality we get from time series depends on the time-frame of the data used… Different time-frame data, different reality… In addition, econometricians provided a whole family of econometric models to approach reality, starting in early 1980s, with “autoregressive models—AR” combined or not with MA (moving averages). But even its 1986 flagship, the GARCH, with its many variations, cannot cope with a number of characteristics, one of which is leptokurtosis (small alpha, higher peaks and long tails), though some argue that it can cater for outliers. Economic turbulence, low or high—despite its characterization by Science as rare—became frequent since 1987 (Black Monday)... In late 1990s e.g. the global financial system underwent 6 crises—which have been called “near turbulences”—over a number of countries, including Russia in 1998. The next turbulence will not be one generation apart—we reckon. This paper is an attempt to invite writers to write a “theory of economic turbulence”. Turbulence is a nightmare, which wakes people up suddenly, and unexpectedly, but it is something people wish to forget… till it strikes again: turbulence stroke in 1929 on (Black) Tuesday, then in 1987 on (Black) Monday and in end-2008, the Great Recession—on 29th September. In Black Monday stock markets around the world crashed losing a huge value in a matter of very short time (Hong Kong, Europe, and USA). The Dow fell ~23%. At that time OPEC collapsed in 1986 and the price of oil doubled… The dry cargo shipping sector entered a turbulent situation since 1989, which has been deteriorated since 2015 reaching finally an alpha equal to ~1.43 < 1.70 by 2035…
References
[1]
Brooks, C. (2014) Introductory Econometrics for Finance. 3rd Edition, Cambridge University Press, Cambridge.
[2]
Stopford, M. (2009) Maritime Economics. 3rd Edition, Routledge, London. https://doi.org/10.4324/9780203891742
[3]
Jiang, S. (2015) More Evidence against the Random Walk Hypothesis. World Scientific, Singapore. https://doi.org/10.1142/9412
[4]
Mandelbrot, B. and Hudson, L.R. (2006) The (Mis) Behavior of Markets: A Fractal View of Financial Turbulence. Basic Books, New York.
[5]
Ruelle, D. and Takens, F. (1971) On the Nature of Turbulence. Communications on Mathematical Physics, 20, 167-192. https://doi.org/10.1007/BF01646553
[6]
Feynman, R. (2006) Turbulence Theory Gets a Bit Choppy. USA Today, Sept. 10. http://www.usatoday.com/tech/science/colmnist/vergano/2006-09-10-turbulence_x.htm
[7]
Mello, L. (2008) Fat-Tailed Distributions and Levy Processes.
[8]
Peters, E.E. (1994) Fractal Market Analysis: Applying Chaos Theory to Investment & Economics. Wiley, Hoboken.
[9]
Anonymous (No Date) Chapter 3, Alpha-Stable Random Variables and Processes. 26-37.
[10]
Trevethan, M. and Chanson, H. (2010) Turbulence and Turbulent Flux Events in a Small Estuary. Environmental Fluid Mechanics, 10, 345-368. https://doi.org/10.1007/s10652-009-9134-7
[11]
Juarez F. ,et al. (2011)Applying the Theory of Chaos and Complex Model of Health to Establish Relations among Financial Indicators Procedia Computer Science 3, 982-986.
[12]
Lorenz, E. (1963) Deterministic Nonperiodic Flow. Journal of Atmospheric Sciences, 20, 130-148. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
[13]
Mpountis, T. (2004) The Wonderful World of Fractals: A Tour in the New Science of Chaos and Complexity. Leader Books Publications. (In Greek)
[14]
Zou, Y., He, K. and Jiang, M. (2015) A Curvelet Based Approach to Time Series Forecasting. Procedia Computer Science, 55, 1325-1330.
[15]
Jing, L., Marlow, P. and Hui, W. (2008) An Analysis of Freight Rate Volatility in Dry Bulk Shipping Markets. Maritime Policy & Management, 35, 237-251. https://doi.org/10.1080/03088830802079987
[16]
Koopmans, T.C. (1939) Tanker Freight Rates and Tankship Building: An Analysis of Cyclical Fluctuations. De Erven F. Bohn N.V., Haarlem.
[17]
Hurst, H.E. (1951) Long-Term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-799, 800-808.
[18]
Einstein, A. (1905) Reference to the Required Movement of Small Particles Moving inside Stagnant Liquid in Accordance with the Molecule-Kinetic Theory of Heat—A New Determination. Annals of Physics, No 322.
[19]
Goulielmos, A.M. and Psifia, M.-E. (2009) Forecasting Weekly Freight Rates for One Year TC 65,000 dwt Bulk Carrier, 1989-2008, Using Nonlinear Methods. Maritime Policy & Management, 36, 411-436. https://doi.org/10.1080/03088830903187150
[20]
Goulielmos, A.M. and Psifia, M.-E. (2011) Forecasting Short-Term Freight Rate Cycles: Do We Have a More Appropriate Method than Normal Distribution? Maritime Policy & Management, 38, 645-672. https://doi.org/10.1080/03088839.2011.556673
[21]
Sugihara, G. and May, R. (1990) Nonlinear Forecasting as a Way of Distinguishing Chaos from Measurement Error in Time Series. Nature, 344, 734-740. https://doi.org/10.1038/344734a0
[22]
Steeb, W.-H. (2008) The Nonlinear Workbook. 4th Edition, World Scientific, Singapore. https://doi.org/10.1142/6883
Schroeder, M. (1991) Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. Dover Publications, Mineola.
[25]
Arthur, W.B. (1994) Increasing Returns and Path Dependence in the Economy. The University of Michigan Press, Ann Arbor. https://doi.org/10.3998/mpub.10029
[26]
Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control. S.F., Holden Day.
[27]
Mandelbrot B. ,et al. (1972)Statistical Methodology for Non-Periodic Cycles: From the Covariance to R/S Analysis Annals of Economic and Social Measurement 1, 259-290.
[28]
Syriopoulos, C. and Leontitsis, A. (2000) Nonlinear Time Series Analysis with NLTSA V.2 Computer Package. Anikoula Publications, Thessalonica. (In Greek)
[29]
Goulielmos, A.M. (2017) The Myths about Forecasting, Business Cycles and Time Series: With an Application to Shipping Industry. Modern Economy, 8, 1455-1477. https://doi.org/10.4236/me.2017.812097
