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Forecasting Financial Indices: The Baltic Dry Indices

Author

Listed:
  • Eleftherios I. Thalassinos
  • Mike P. Hanias
  • Panayiotis G. Curtis
  • John E. Thalassinos

Abstract

The main aim of this paper is to use chaos methodology in an attempt to predict the Baltic Dry Indices (BDI, BCI, BPI) using the invariant parameters of the reconstructed strange attractor that governs the system’s evolution. This is the result of the new emerging field in econo-physics which mainly consists of autonomous physic-mathematical models that have been already applied to financial analysis. The proposed methodology is estimating the optimal delay time and the minimum embedding dimension with the method of False Nearest Neighbors (FNN). Monitoring the trajectories of the corresponding strange attractor we achieved a 30, 60, 90 and 120 time steps out of sample prediction.

Suggested Citation

  • Eleftherios I. Thalassinos & Mike P. Hanias & Panayiotis G. Curtis & John E. Thalassinos, 2013. "Forecasting Financial Indices: The Baltic Dry Indices," International Journal of Maritime, Trade & Economic Issues (IJMTEI), International Journal of Maritime, Trade & Economic Issues (IJMTEI), vol. 0(1), pages 109-130.
    • Handle: RePEc:ers:ijmtei:v:i:y:2013:i:1:p:109-130
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    References listed on IDEAS

    as
    1. Eleftherios I. Thalassinos & Mike P. Hanias & Panayiotis G. Curtis & Yannis E. Thalassinos, 2009. "Chaos theory: forecasting the freight rate of an oil tanker," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 1(1), pages 76-88.
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    More about this item

    Keywords

    Chaos methodology; False Nearest Neighbors; Baltic Dry Indices;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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