Multifractal analysis of spot rates in tanker markets and their comparisons with crude oil markets

Zheng, Shiyuan; Lan, Xiangang

doi:10.1016/j.physa.2015.10.061

Cited by 21 publications

(24 citation statements)

References 24 publications

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“…To explore the multifractal property of Bitcoin, we calculate the generalized Hurst exponent using multifractal detrended fluctuation analysis (MF-DFA) [15] by using 1-min return data. The MF-DFA, which is an extended method of DFA, can investigate multifractal properties of non-stationary time series, and has been successfully applied for a variety of financial markets, such as stock [16,17,18,19,20,21,22,23], commodity [16,24,25,26,27], tanker [28], derivative [29], foreign exchange rates [30,31,32,33,34], and electricity markets [35]. An especially interesting application of multifractal analysis is measuring the degree of multifractality of time series, which can be related to the degree of efficiency of financial markets [36,37,38,39].…”

Section: Introductionmentioning

confidence: 99%

Statistical properties and multifractality of Bitcoin

Takaishi

2018

Physica A: Statistical Mechanics and its Applications

110

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Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the Gaussian expectation, we find that convergence to that is very slow. Skewness is found to be negative at time scales shorter than one day and becomes consistent with zero at time scales longer than about one week. We also investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models, and find no evidence of it. On exploring multifractality using multifractal detrended fluctuation analysis, we find that the Bitcoin time series exhibits multifractality. The sources of multifractality are investigated, confirming that both temporal correlation and the fat-tailed distribution contribute to it. The influence of "Brexit" on June 23, 2016 to GBP-USD exchange rate and Bitcoin is examined in multifractal properties. We find that, while Brexit influenced the GBP-USD exchange rate, Bitcoin was robust to Brexit.

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Section: Introductionmentioning

confidence: 99%

Statistical properties and multifractality of Bitcoin

Takaishi

2018

Physica A: Statistical Mechanics and its Applications

110

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“…However, one hidden purpose was to note and induce future research onwards to nonlinear models with a view to improve forecasting. We noted with satisfaction that after our first effort in 2006 [19], colleagues started to use chaotic models [20] in forecasting and to quote us as well [21]. 13 Inland waterways: 5 papers; Panama Canal: 5; shipping market modeling & analysis: 23; policy: 5; risk: 9; maritime innovations: 4; cruise and ferry: 4; short sea shipping: 1.…”

Section: Aimmentioning

confidence: 99%

“…However, the work of BV (1985-1993) put aside as the research had then to face the "stationarity" problem 23 , where shipping data were no exception. 21 His PhD in London City University was on "(an aggregated) econometric model of the world shipping markets analyzing dry and wet, 2nd hand ship and freight rate markets, shipbuilding and scrap"… 22 Ships that transport in bulk one uniform cargo for their entire space, either liquid or dry. 23 A stochastic process is "strictly" stationary if its properties are unaffected by a change in its time origin [26] "Primarily descriptive, legalistic and historical": 53 33 "A formal examination, or inspection, of the conditions or characters (surveys)": 5 0…”

Section: The Beenstock and Vergottis Model (1985-1993)mentioning

confidence: 99%

Maritime Economic Modeling: A Survey, 2006-2016

Γουλιέλμος¹

2018

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We surveyed quite a number of papers (~60), where one third of them belonged to maritime economic dynamic systems, meaning multi variable models. The emphasis we placed was on works published the last 11 years or so (2006-2016). In addition, we provided a general picture of maritime economic research from 1973 to 2013. The survey had to be combined with advances in General Econometrics. As a result and despite long delays maritime economists (Glen et al. [1] applied the VAR-vector autoregressive (1987-1988) model due to Sims [2]. But this was not the only one as co-integration [3] [4] dominated the scene given that SEM-simultaneous equations model had defects in relation to exogenous variables. Moreover, VEC-the vector error correction model and a representation of VAR appeared based on Granger's representation theorem. The models for 2006-2016 were 36 including 5 for maritime futures after 2003 and 7 on forecasting (2003-2016). A description of a shipping cycle has been given with the presence of time.

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“…They argue that the multiscale model provides a feasible approach to analyze the cyclic dynamics of crude oil prices and prove the cyclic period is 4.3 years [14]. Zheng and Lan [15] use a multifractal de-trend fluctuation analysis to analyze the dynamic characteristic of five different tanker markets. By comparing three different aspects including non-periodic cycles, the Hurst exponents, and origins of multifractality with crude oil markets, they find that the tanker markets are more fractal [15].…”

Section: Introductionmentioning

confidence: 99%

“…Zheng and Lan [15] use a multifractal de-trend fluctuation analysis to analyze the dynamic characteristic of five different tanker markets. By comparing three different aspects including non-periodic cycles, the Hurst exponents, and origins of multifractality with crude oil markets, they find that the tanker markets are more fractal [15]. Benhmad [16] analyze the cyclical co-movement between crude oil prices and US Gross Domestic Product (GDP) using the wavelet analysis and the Granger causality test and found the existence of a cyclical relationship in the multiscale domain [16].…”

Section: Introductionmentioning

confidence: 99%

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Zha

et al. 2016

Sustainability

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Abstract:Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

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Multifractal analysis of spot rates in tanker markets and their comparisons with crude oil markets

Cited by 21 publications

References 24 publications

Statistical properties and multifractality of Bitcoin

Statistical properties and multifractality of Bitcoin

Maritime Economic Modeling: A Survey, 2006-2016

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

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