A Chaos Analysis of the Dry Bulk Shipping Market
Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this pape...
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doaj-60097a5e2b724239855f81cb4c1a7b022021-09-09T13:52:15ZengMDPI AGMathematics2227-73902021-08-0192065206510.3390/math9172065A Chaos Analysis of the Dry Bulk Shipping MarketLucía Inglada-Pérez0Pablo Coto-Millán1Department of Statistics and Operational Research, Medicine Faculty, Complutense University, Plaza Ramón y Cajal, s/n Ciudad Universitaria, 28040 Madrid, SpainDepartment of Economy, University of Cantabria, 39005 Santander, SpainFinding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market.https://www.mdpi.com/2227-7390/9/17/2065chaosnonlinear dynamicscorrelation dimensionLyapunov exponentrecurrence plotsGARCH |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lucía Inglada-Pérez Pablo Coto-Millán |
spellingShingle |
Lucía Inglada-Pérez Pablo Coto-Millán A Chaos Analysis of the Dry Bulk Shipping Market Mathematics chaos nonlinear dynamics correlation dimension Lyapunov exponent recurrence plots GARCH |
author_facet |
Lucía Inglada-Pérez Pablo Coto-Millán |
author_sort |
Lucía Inglada-Pérez |
title |
A Chaos Analysis of the Dry Bulk Shipping Market |
title_short |
A Chaos Analysis of the Dry Bulk Shipping Market |
title_full |
A Chaos Analysis of the Dry Bulk Shipping Market |
title_fullStr |
A Chaos Analysis of the Dry Bulk Shipping Market |
title_full_unstemmed |
A Chaos Analysis of the Dry Bulk Shipping Market |
title_sort |
chaos analysis of the dry bulk shipping market |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2021-08-01 |
description |
Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market. |
topic |
chaos nonlinear dynamics correlation dimension Lyapunov exponent recurrence plots GARCH |
url |
https://www.mdpi.com/2227-7390/9/17/2065 |
work_keys_str_mv |
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