Multiscale entropy analysis of crude oil price dynamics

Martina, Esteban; Rodríguez, Eduardo; Escarela‐Perez, R.; Alvarez-Ramı́rez, José

doi:10.1016/j.eneco.2011.03.012

Cited by 103 publications

(38 citation statements)

References 44 publications

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“…In fact, Theil and Leenders [40], Fama [13], and Dryden [11] may be considered seminal papers in this field. More recently, Martina et al [26] and Ortiz et al [29] applied entropy and multiscale entropy analysis to assess crude oil price efficiency. Alvarez-Ramírez et al [3] also used entropy methods to quantify the dynamics of the informational efficiency of the US stock market over the last 70 years.…”

Section: Permutation Information Theory Quantifiersmentioning

confidence: 99%

The (in)visible hand in the Libor market: an information theory approach

et al. 2015

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This paper analyzes several interest rates time series from the United Kingdom during the period 1999 to 2014. The analysis is carried out using a pioneering statistical tool in the financial literature: the complexityentropy causality plane. This representation is able to classify different stochastic and chaotic regimes in time series. We use sliding temporal windows to assess changes in the intrinsic stochastic dynamics of the time series. Anomalous behavior in the Libor is detected, especially around the time of the last financial crisis, that could be consistent with data manipulation.

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Section: Permutation Information Theory Quantifiersmentioning

confidence: 99%

The (in)visible hand in the Libor market: an information theory approach

et al. 2015

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“…MSE (Mean Square Error) and RMSE (Root Mean Square Error) are metrics to evaluate the complexity and irregularity of time series with different scale factors. It is used in classification of physiological time series [30], eccentricity fault detection of motor spindle [31], dynamical change of crude oil price [32], and network traffic analysis and anomaly detection [33]. In this work, the multiscale entropy with different scale factors is used to select the scale information to process the alarm time series.…”

Section: Related Workmentioning

confidence: 99%

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

Liang

Long

Zuo

et al. 2018

Wireless Communications and Mobile Computing

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The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM (Hidden Markov Model) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted HMM based algorithm is proposed to predict the security situation of the mobile network. The multiscale entropy is used to address the low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized. Moreover, the autocorrelation coefficient can reasonably use the association between the characteristics of the historical data to predict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive, with good performance in prediction speed and accuracy when compared to existing design.

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“…Recently we have identified some recent research endeavors in the financial literature to model the multiscal data structure using the multiscale entropy theory. For example, in the energy economics literature, Martina et al [32] introduced the entropy concept to analyze the efficiency of the crude oil markets [32]. Ortiz-Cruz et al [33] introduced multiscale entropy theory to analyze the multiscale data structure in the crude oil market [33].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

Zou

2015

Entropy

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Abstract:For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.

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Multiscale entropy analysis of crude oil price dynamics

Cited by 103 publications

References 44 publications

The (in)visible hand in the Libor market: an information theory approach

The (in)visible hand in the Libor market: an information theory approach

A Security Situation Prediction Algorithm Based on HMM in Mobile Network

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

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