Shipping finance: Time to follow a new track?

Maritime Policy and Management

Volumn 33, Issue 3, 2006, Pages 301-320

  Goulielmos, Alexandros M ^a   Psifia, Mariniki ^a  

^a UNIVERSITY OF PIRAEUS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

FINANCE; FORECASTING; FREIGHT TRANSPORTATION; INVESTMENTS; MARKETING; RANDOM PROCESSES;

FINANCIAL MARKET; FINANCIAL PROVISION; SHIPPING;

EID: 33746515065     PISSN: 03088839     EISSN: 14645254     Source Type: Journal    
DOI: 10.1080/03088830600783301     Document Type: Article

References (73)

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7. Exception in 1983, i.e. a massive investment took place during a recession when a company believed in a symmetrical shipping cycle of four years. Two years had already passed. This is the case of Sanko shipping company. Sanko invested in recession, hoping for recovery, but at such a scale - followed by Greeks and Norwegians - that it dampened the market further and delayed the recovery by two years (1987)
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10. Used for financing construction of power generating plants, energy cogeneration facilities, bridges, tunnels and commercial aircraft
11. Called special purpose vehicle, which may be a subsidiary of the sponsor or a fund
12. Mandelbrot, B and Hudson, R.(2004) The (Mis)behavior of Markets: A Fractal View of Risk, Ruin and Reward. New York: Basic Books.
13. Mandelbrot and Hudson (ibid.) report that for the relationship between dollar and yen, half of the decline between 1986 and 2003 took place in just ten days and the same happened for the S & P 500
14. Soros, G. gained 2 billion dollars in 1992 playing with the pound
15. These are: (1) deceptive markets; (2) flexible time; (3) future volatility odds estimate; (4) inevitable market bubbles; (5) market time/ place equality; (6) market uncertainty; (7) prices leap; (8) risky markets; (9) turbulent markets; and (10) value limited. See Mandelbrot and Hudson (ibid., Chapter XII)
16. See Mandelbrot and Hudson (ibid.), p. 174. The Nile pattern found in-stock price fluctuations, international crude oil prices, London gold fixings and the deregulated US electricity market. One of the authors (Goulielmos, 2004) found it in ship total losses
17. The method developed by Hurst to determine long-memory effects and fractional Brownian (biased random walk) motion. A measurement of how the distance covered by a particle increases over longer and longer time scales. For Brownian motion (random walk), the distance covered increases with the square root of time. A series that increases at a different rate is not random
18. Stewart, J and Gill, L.(1998) Econometrics, 2nd edition. Prentice Hall Europe. The error terms or disturbances are independent and identically distributed
19. Peters, EE.(1994) Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. New York: John Wiley). In time series analysis this is quite rare
20. The relationship between d and the Hurst exponent is found to be d=H-1/ 2. Long-run memory is found when 0
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22. GARCH stands for generalized auto-regressive conditional heteroskedasticity and refers to a set of statistical tools to model data whose variability changes with time. It also means that the changes in variability are controlled by the data's own past behaviour. This model has been broadened to accommodate more circumstances than the 1982 ARCH
23. This is an attempt to mimic biological neural networks. These are also computational techniques on the market coming out of a co-operation between the computer industry and Wall Street in a hope that silicon intelligence can find profitable patterns
24. Tinbergen, J., Tonnage and Freight 1934, 1959. In Selected papers [pp.]. Also, 1931, 1959, A shipbuilding cycle?
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33. Berg-Andreassen, JA. (1997) The relationship between period and spot rates in international maritime markets Maritime Policy & Management, 24, pp. 335-350.
34. This term is explained below
35. Test for a unit root in an AR(1) process. There is considerable evidence to suggest that this test is not very powerful in general and its ability to detect the absence of a unit root when one is not present is not large (ibid. 18, p. 239)
36. A test of hypotheses about the number of cointegration relations with a non-standard distribution. It fails to have the usual central chi square distribution under the null hypothesis (ibid., p. 336)
37. Dijk D., Van and Haralambides, H and Veenstra, AW. undated, Modelling of Ocean Charter Rates; an investigation into no-linearity and parameters variation, revised version
38. Li, J and Parsons, MG. (1997) Forecasting tanker freight rate using neural networks Maritime Policy and Management Journal, 24, pp. 9-30.
39. Kavussanos, MG and Marcoulis, SN. (1997) Risk and return of US water transportation stocks over time and& over bull and bear market conditions Maritime Policy & Management, 24(2), pp. 145-158.
40. This term is explained in footnote 43
41. The amount by which the stock reacts to the market
42. The measure of the peakedness of the probability density function (normality means alpha=2)
43. Capital asset pricing model, an equilibrium-based asset-pricing model. Assets are priced accordingly to their relationship to the market portfolio of all risky assets as determined by the securities' beta (simplest version)
44. Fama, EF and French, KR. (1992) The cross-section of expected stock returns Journal of Finance, 47(2), pp. 427-465.
45. Veenstra, AW.(1999) Quantitative Analysis of Shipping Markets. Delft: Delft University Press.
46. Efficiency in the sense that all relevant information available in the market is accurately reflected in the freight rates, but he advocates the need for a pricing model (taken from the bond market)
47. Kavussanos, M and Alizadeh, MA. (2002) The expectations hypothesis of the term structure and risk premiums in dry bulk shipping freight markets Journal of Transport Economics & Policy, 36, pp. 267-304.
48. Wright, G. (2000) Spot and period rates in the wet bulk shipping market: testing for long run parity Journal of Transport Economics & Policy, 34, pp. 291-300.
49. If two variables contain common stochastic trends, the stochastic trend in one is a consequence of the stochastic trend in the other
50. Kavussanos, MG and Alizadeh, MAH. (2001) Seasonality patterns in dry bulk shipping spot and time charter freight rates Transportation Research Part E, 37, pp. 443-467.
51. Denning, KC and Riley, WB and Delooze, JP. (1994) Baltic freight futures: Random walk or seasonally predictable? International Review of Economics & Finance, 3, pp. 399-428.
52. Based on the fact that normal distribution has a characteristic set of moments: The first is 0, the second is 1, the third is 0 and the fourth is 3. The test works by comparing the sample versions of the coefficient of excess skewness and the coefficient of excess kurtosis. BJ=(n(G1/6+G2/ 24) where Gs should be squared. The result will be compared with 5% critical value of a Χ squared distribution with 2 degrees of freedom (5.99)
53. ARCH models generalised by Nelson in 1991
54. Kavussanos, MG and Alizadeh, MA. (2002) Seasonality patterns in tanker spot freight rate markets Economic Modelling, 19, pp. 747-782.
55. Kavussanos, M. (2003) Time varying risks among segments of the tanker freight markets Maritime Economics & Logistics, 30, pp. 227-250.
56. Lo, AW. (1991) Long-term memory in stock market prices Econometrica, 59, pp. 1279-1313.
57. Hurst, HE. (1954) Measurement and utilization of the water resources of the Nile basin Proceedings of the Institute of Civil Engineers, 3, pp. 1-26. born in 1880 in Britain, was a hydrologist and civil servant of Imperial Britain. He was confronted with the problem of building a dam on the Nile River, to be efficient for the next 100 years. He found no obvious periodicity and it was difficult to control something with no predictable pattern [ibid. 12, p. 177]. Hurst observed that what was more important was the precise sequence of floods. After he studied the secular flood records, he devised his own formula to capture this effect in 1951-1956 in three lengthy essays: (a), (b) 1956, Methods of using long term storage in reservoirs, Proceedings of the Institute of Civil Engineers5, 51-590
58. H. Mandelbrot (1983, p. 249) called this Hurst noise. Shown by Mandelbrot, B. B., 1983, The Fractal Geometry of Nature (New York: Freeman & Co.) that 0 ≤ H ≤ 1
59. This formula comes from Peters (ibid. 19, pp. 184-185). It expresses the correlation of changes at t with all increments of time t that precede and follow it
60. Peters, EE.(1996) Chaos and Order in the Capital Markets, 2nd edition. New York: John Wiley & Sons, Inc.
61. Hurst, instead of applying the statistical tool, called range directly (difference between the highest and lowest values) he adjusted the data to remove the trend for time intervals of varying length and the starting point. After that he computed the range, which should increase at square root of time (interval), but this strangely grew faster. Mandelbrot and Hudson (ibid.12, p. 179) took that as a symptom of scaling→fractality. Then Mandelbrot placed this concept in the long dependence and extended it to the financial prices (Mandelbrot, B. B., 2002, Gaussian self-affinity and fractals: Globality, the earth, 1/f Noise & R/S (New York: Springer Verlag). Fractal is a pattern or object whose parts echo the whole, only scaled down (Mandelbrot and Hudson, ibid.12, p. 208
62. This is required for most econometric models function on the assumption that the generating process of time series is stationary
63. Some of the most commonly used 'filters' are the first differences of observations, their first logarithmic differences, their AR(1) residuals and a combination of them
64. Ibid.19
65. Always the last value of the accumulated departures has to be equal to zero (0)
66. This called by Peters (ibid.19) as a Master Stroke, making the time-series timeless and comparable along decades and centuries. This was one of his problems with the River Nile having 900 years of data
67. This is presented in Peters (ibid. 19, Chapter 5 and p. 71)
68. This equation however, due to Anis, A. A. and Lloyd, E. H., 1976, The expected value of the adjusted rescaled range of independent normal summands. BIOMETRIKA, 63, 111-116, had been rejected by Peters (ibid. 19, p. 69) who declared that this way of calculating the expected (R/S)n values cannot be used for large n values as the Γ factor values become too large
69. Anis and Lloyd (ibid. 68)
70. Siriopoulos, C and Leonditsis, A.(2000) Chaos, Analysis & Forecasting Time Series. Thessalonica: Anikoula (in Greek.
71. Siriopoulos and Leonditsis (ibid. 70, p. 15)
72. This reflects the volatilities of the market in comparison with time charter. But future research will be extended to time charter also
73. It must have a fractal dimension and must exhibit sensitive dependence on initial conditions. It is a deterministic nonlinear dynamic system that can produce random-looking results