The entropy as a tool for analysing statistical dependences in financial time series

Darbellay, Georges A.; Wuertz, Diethelm

doi:10.1016/s0378-4371(00)00382-4

Cited by 126 publications

(57 citation statements)

References 11 publications

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“…Urbach [11] defends a strong relationship between entropy, dependence and predictability. This relation has been studied by several authors, namely Granger and Lin [4], Maasoumi and Racine [9], Darbellay and Wuertz [3]. On the basis of the above arguments we aim to evaluate in this paper the efficiency of a new entropy-based independence test without requiring the specification of mean-variance models and theoretical distribution probabilities.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Independence Test

2006

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Abstract. This paper presents a new test of independence (linear and nonlinear) among distributions, based on the entropy of Shannon. The main advantages of the presented approach are the fact that this measure does not need to assume any type of theoretical probability distribution and has the ability to capture the linear and nonlinear dependencies, without requiring the specification of any kind of dependence model.

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Section: Introductionmentioning

confidence: 99%

Entropy-Based Independence Test

2006

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“…Urbach (2000) and Dionisio et al (2006) support a strong relationship between entropy, dependence and predictability. This relation has been studied by several authors, namely Darbellay and Wuertz (2000), Granger et al (2004), Dionisio et al (2004), Reshef et al (2011), Kraskov andGrassberger (2009), andYao (2003). Dependence measure MINE defined by Reshef et al (2011) is found to be capable of calculating dependence between variables related to each other in different ways.…”

Section: Some Methods Commonly Used To Measure Dependence Between Varmentioning

confidence: 99%

A new estimate of mutual information based measure of dependence between two variables: properties and fast implementation

Jain

Murthy

2015

Int. J. Mach. Learn. & Cyber.

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This article proposes a new method to estimate an existing mutual information based dependence measure using histogram density estimates. Finding a suitable bin length for histogram is an open problem. We propose a new way of computing the bin length for histogram using a function of maximum separation between points. The chosen bin length leads to consistent density estimates for histogram method. The values of density thus obtained are used to calculate an estimate of an existing dependence measure. The proposed estimate is named as MIDI (Mutual Information based Dependence Index). Some important properties of MIDI have also been stated.The performance of the proposed method has been compared to generally accepted measures like distance correlation (dcor), Maximal Information Coefficient (MINE) in terms of accuracy and computational complexity with the help of several artificial data sets with different amounts of noise. The proposed method is able to detect many types of relationships between variables, without making any assumption about the functional form of the relationship. The power statistics of proposed method illustrate their effectiveness in detecting non linear relationship. Thus, it is able to achieve generality without a high rate of false positive cases. MIDI is found to work better on a real life data set than competing methods. The proposed method is found to overcome some of the limitations which occur with dcor and MINE. Computationally, MIDI is found to be better than dcor and MINE, in terms of time and memory, making it suitable for large data sets.

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“…Then, the indicator will have a higher weight in the synthetical evaluation (Tang et al [44] and Zhang et al [45]). As argued by Burg [46], Darbellay and Wuertz [47] and Li et al [48], synthesizing the indexes with entropy method has four advantages. First, adopting the entropy method to determine the index weight can avoid human interference factors and thus obtain more realistic assessment.…”

Section: Disaster Indicatormentioning

confidence: 99%

Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data

Guo

Liu

et al. 2015

Sustainability

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Using a newly developed integrated indicator system with entropy weighting, we analyzed the panel data of 577 recorded disasters in 30 provinces of China from 1985-2011 to identify their links with the subsequent economic growth. Meteorological disasters promote economic growth through human capital instead of physical capital. Geological disasters did not trigger local economic growth from 1999-2011. Generally, natural disasters overall had no significant impact on economic growth from 1985-1998. Thus, human capital reinvestment should be the aim in managing recoveries, and it should be used to regenerate the local economy based on long-term sustainable development.

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The entropy as a tool for analysing statistical dependences in financial time series

Cited by 126 publications

References 11 publications

Entropy-Based Independence Test

Entropy-Based Independence Test

A new estimate of mutual information based measure of dependence between two variables: properties and fast implementation

Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data

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