A wavelet and neural network model for the prediction of dry bulk shipping indices

Shipping markets are typically volatile in nature, as manifest in the dynamics of freight rates. According to shipowners’ risk propensity, volatility-related decisions vary from what kind of contract (time/voyage charter) to engage in to whether to enter/exit the business. After a sharp drop in freight rates at the end of 2008, a discussion about appropriate risk management concepts and statistical tools is needed. Owing to the magnitude of investment required in shipping, any additional information regarding the future direction of market volatility is of the utmost importance. The ambition of this article is exactly the same: to study fluctuations in the freight rates of the Baltic Panamax route 2A and the Baltic Panamax route 3A, using a tool of analysis that is new to shipping economics: a hybrid model of wavelets and neural networks. The wavelet multiscale decomposition of time series reveals volatility dynamics across different time frequencies and will uncover patterns that will be used by neural networks for prediction.

[1]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..

[2]  R. Gencay,et al.  An Introduction to Wavelets and Other Filtering Methods in Finance and Economics , 2001 .

[3]  Todd R. Ogden,et al.  Wavelet Methods for Time Series Analysis , 2002 .

[4]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Kevin Cullinane,et al.  A portfolio analysis of market investments in dry bulk shipping , 1995 .

[6]  Chris Chatfield,et al.  The Analysis of Time Series: An Introduction , 1990 .

[7]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[8]  Amir H. Alizadeh,et al.  Shipping derivatives and risk management , 2009 .

[9]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[10]  T. Bollerslev.  Generalized autoregressive conditional heteroskedasticity with applications in finance , 1986 .

[11]  Manolis G. Kavussanos,et al.  Price Risk Modelling of Different Size Vessels in the Tanker Industry Using Autoregressive Conditional Heteroskedasticity (ARCH) Models , 1996 .

[12]  N. Taleb.  Black Swans and the Domains of Statistics , 2007 .

[13]  B. Mandelbrot.  The Variation of Certain Speculative Prices , 1963 .

[14]  Manolis G. Kavussanos,et al.  Comparisons of Volatility in the Dry-Cargo Ship Sector. Spot versus Time-Charters, and Smaller Versus Larger Vessels , 1996 .

[15]  Chris Chatfield,et al.  The Analysis of Time Series , 1990 .

[16]  Chris Chatfield,et al.  The Analysis of Time Series: An Introduction , 1981 .

[17]  James B. Ramsey,et al.  The contribution of wavelets to the analysis of economic and financial data , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[18]  G. Peter Zhang.  Neural Networks in Business Forecasting , 2003 .

[19]  Patrick M. Crowley,et al.  An Intuitive Guide to Wavelets for Economists , 2005 .

[20]  P. Fishwick,et al.  Feed-forward Neural Nets as Models for Time Series Forecasting , 1993 .

[21]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .