A model-free characterization of causality

Baghli, Mustapha

doi:10.1016/j.econlet.2005.12.016

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Mutual information is non-negative and I(X, X) = H(X). It is also worth noting that such an approach is inherently model-free [39]. Additionally, partial mutual information I(X, Y |Z) denotes the part of mutual information I(X, Y ) that is not in Z and is defined as [58]:…”

Section: Methodsmentioning

confidence: 99%

“…This is the most used approach, even though it goes against the assumption of the complexity of those systems [23,24] and the solid evidence of the nonlinearity of financial markets with regards to stock returns [25][26][27][28][29], market index returns [30][31][32][33][34] and currency exchange rate changes [25,[35][36][37][38]. This has recently been addressed by exchanging Pearson's correlation coefficient with a more general (model-free [39]) measure of mutual information, which allows the study to account for nonlinearity and not rely on the assumption of multivariate normality [40,41].…”

Section: Introductionmentioning

confidence: 99%

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Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

You

Fiedor

Hołda

2015

JRFM

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Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-art method for analyzing the structure and risk within stock markets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stock market in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson's correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

You

Fiedor

Hołda

2015

JRFM

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“…A comprehensive introduction to TE is provided by Bossomaier et al [ 11 ], whereas an overview of causality detection based on information-theoretic approaches in time series analysis can be found in [ 15 ]. A non-parametric characterization of causality relying on conditional entropy was proposed by Baghli [ 16 ].…”

Section: Background: Transfer Information Entropymentioning

confidence: 99%

Transfer Information Energy: A Quantitative Indicator of Information Transfer between Time Series

Caţaron

Andonie

2018

Entropy

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We introduce an information-theoretical approach for analyzing information transfer between time series. Rather than using the Transfer Entropy (TE), we define and apply the Transfer Information Energy (TIE), which is based on Onicescu's Information Energy. Whereas the TE can be used as a measure of the reduction in uncertainty about one time series given another, the TIE may be viewed as a measure of the increase in certainty about one time series given another. We compare the TIE and the TE in two known time series prediction applications. First, we analyze stock market indexes from the Americas, Asia/Pacific and Europe, with the goal to infer the information transfer between them (i.e., how they influence each other). In the second application, we take a bivariate time series of the breath rate and instantaneous heart rate of a sleeping human suffering from sleep apnea, with the goal to determine the information transfer heart → breath vs. breath → heart. In both applications, the computed TE and TIE values are strongly correlated, meaning that the TIE can substitute the TE for such applications, even if they measure symmetric phenomena. The advantage of using the TIE is computational: we can obtain similar results, but faster.

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“…(Diks & Panchenko, 2005) critically discussed the previous tests of (Hiemstra & Jones, 1994). As the most recent development in economics, (Baghli, 2006) proposes information-theoretic statistics for a model-free characterization of causality, based on an evaluation of conditional entropy.…”

Section: Transfer Entropy Other Information-theoretical Measures Andmentioning

confidence: 99%

The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships

Hlaváčková‐Schindler¹

2012

Theoretical and Methodological Approaches to Social Sciences and Knowledge Management

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A model-free characterization of causality

Cited by 11 publications

References 13 publications

Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

Transfer Information Energy: A Quantitative Indicator of Information Transfer between Time Series

The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships

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