Modeling the flow of information between financial time-series by an entropy-based approach

Benedetto, Francesco; Mastroeni, Loretta; Vellucci, Pierluigi

doi:10.1007/s10479-019-03319-7

Cited by 21 publications

(23 citation statements)

References 41 publications

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“…To have an idea about this growth, it is sufficient to mention the tremendous rise of the ratio between financial and domestic corporate profits (Di Bucchianico 2020a , b ) or the flows into commodity investments, increased from $ 15 billion in 2003 to $ 250 billion in 2009 (Irwin and Sanders 2012 ). Financial institutions, insurance companies, pension funds, among others, can be considered the major causes of these vast inflows (Zhang et al 2017 ; Wang et al 2015 ; Mi et al 2017 ; Benedetto et al 2019 ). According to the US government, the share of US GDP produced by finance, insurance and real-estate industries has risen from 15 to 24 per cent making it bigger than manufacturing and close to the size of the service sector (Mason 2016 ; Krippner 2005 ).…”

Section: Rise and Reasons Of Financial Neoliberalismmentioning

confidence: 99%

“…Tang and Xiong ( 2012 ) claim that, since the early 2000s, prices of non-energy commodity futures in the United States have become increasingly correlated with oil prices and this trend reflects the financialization of the commodity markets and helps explain the large increase in the price volatility of non-energy commodities around 2008. Zhang et al ( 2017 ), Benedetto et al ( 2019 ) investigate the role of equity markets in relation to crude oil and natural gas markets, showing that de-financialization for crude oil and natural gas markets after 2008 is not detectable in the data.…”

Section: Evidence Of Financialization On Commodity Marketsmentioning

confidence: 99%

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A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

Vellucci

2021

SN Bus Econ

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The era of financialization bas been marked by a huge increase in the size of the financial sector. The economic justifications for this expansion arise from the prevailing neo-liberal ideology; they are based on arguments like leading role of markets, economic efficiency, reallocation and spreading of risk. Given these reasons, financialization can be viewed as a form of neoliberalism and then we can use the term financial neoliberalism (Palley in Financialization: the economics of finance capital domination. Springer, 2016) to denote it. This commentary paper hopes to complement this view by highlighting the usefulness of recent “multidisciplinary” methods (i.e., methods which do not fall within the usual economic discipline). We focus first on the definition of financialization process, second on its implications on the commodity markets, and third and finally on the importance of the role of multidisciplinary methods, applied to these markets, capable of highlighting the contradictions of the neoliberalism framework, in such a way, we hope, to promote further research.

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Section: Rise and Reasons Of Financial Neoliberalismmentioning

confidence: 99%

Section: Evidence Of Financialization On Commodity Marketsmentioning

confidence: 99%

A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

Vellucci

2021

SN Bus Econ

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“…Among the commonly used estimation approaches for mutual information are the probability density-based methods such as the Burg's maximum entropy method (Burg's MEM), the kernel density estimation (KDE), and the nearest-neighbor approach. For instance, [9]- [10] used the Burg's MEM to model the flow of information between financial variables. Reference [42] also developed a new KDE for application to large high-dimensional datasets frequently used in genomic experiments.…”

Section: B Mutual Informationmentioning

confidence: 99%

“…Reference [42] also developed a new KDE for application to large high-dimensional datasets frequently used in genomic experiments. In as much the Burg's MEM of [9]- [10] and the KDE of [42] are nonparametric estimators and are suitable for high-dimensional datasets; however, they differ in three major aspects.…”

Section: B Mutual Informationmentioning

confidence: 99%

The Use of Mutual Information to Improve Value-at-Risk Forecasts for Exchange Rates

2020

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In this paper, we show a simple but novel approach in an attempt to improve value-at-risk forecasts. We use mutually dependent covariate returns to create exogenous break variables and jointly use the variables to augment GARCH models to account for time-variations and breaks in the unconditional volatility processes simultaneously. A study of hypothetical mutual dependencies between volatility and the covariates is first carried out to investigate the levels of the shared mutual information among the variables before using the augmented models to forecast 1% and 5% value-at-risks. The results provide evidence of some substantial exchange of information between volatility and the lagged exogenous covariates. In addition, the results show that the estimated augmented models have lower volatility persistence, reduced information leakages, and improved explanatory powers. Furthermore, there is evidence that our approach leads to fewer violations, improved 1% value-at-risk forecasts, and optimal daily capital requirements for all the models. There is, however evidence of relative superiority of the majority of the models for the 5% value-at-risks forecasts from our approach, although they have relatively higher failure rates. Based on these results, we recommend the incorporation of our approach to existing risk modeling frameworks. It is believed that such models may lead to fewer bank failures, expose banks to optimal market risks, and assist them in computing optimal regulatory capital requirements and minimize penalties from regulators. INDEX TERMS Exogenous break, mutual information, value-at-risk, volatility.

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“…Reference [ 15 ] expanded the analysis to of information flows of market volatility. More recent studies [ 16 , 17 ] brought more insights of information flows in commodity markets. However, all these studies focused on analysis of financial time series and statistical interpretations of financial data.…”

Section: Introductionmentioning

confidence: 99%

The Flow of Information in Trading: An Entropy Approach to Market Regimes

Liu

Chen

Yang

et al. 2020

Entropy

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In this study, we use entropy-based measures to identify different types of trading behaviors. We detect the return-driven trading using the conditional block entropy that dynamically reflects the “self-causality” of market return flows. Then we use the transfer entropy to identify the news-driven trading activity that is revealed by the information flows from news sentiment to market returns. We argue that when certain trading behavior becomes dominant or jointly dominant, the market will form a specific regime, namely return-, news- or mixed regime. Based on 11 years of news and market data, we find that the evolution of financial market regimes in terms of adaptive trading activities over the 2008 liquidity and euro-zone debt crises can be explicitly explained by the information flows. The proposed method can be expanded to make “causal” inferences on other types of economic phenomena.

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Modeling the flow of information between financial time-series by an entropy-based approach

Cited by 21 publications

References 41 publications

A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets

The Use of Mutual Information to Improve Value-at-Risk Forecasts for Exchange Rates

The Flow of Information in Trading: An Entropy Approach to Market Regimes

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