Relation between regional uncertainty spillovers in the global banking system

Tungsong, Sachapon; Caccioli, Fabio; Aste, Tomaso

doi:10.21314/jntf.2018.040

Cited by 12 publications

(8 citation statements)

References 54 publications

(87 reference statements)

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“…In an earlier and somewhat related work, Marschinski & Kantz [18] defined and used effective transfer entropy to quantify contagion in financial markets. Similarly, Tungsong et al [19] used transfer entropy to develop upon the previous work by Diebold & Yilmaz [20] in quantifying spillover effects between financial markets, generalizing the methodology and estimating the time evolution of interconnectedness between financial systems.…”

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confidence: 99%

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Keskin

Aste

2020

R. Soc. open sci.

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Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.

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confidence: 99%

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Keskin

Aste

2020

R. Soc. open sci.

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“…We investigated collective movements of currency prices and currency sentiment by computing Kendall cross-correlations [12] and non-parametric transfer entropy [13,14] of daily log-returns, log P rice(t) − log P rice(t − 1) (differences of the logarithm of the price between a day and the previous), and daily changes of the logarithm of the number of messages classified positive or negative, log(Number of messages with positive sentiment on day t)− log(Number of messages with positive sentiment on day t−1). The choice of the log-returns for prices is standard in financial literature [15].…”

Section: Methodsmentioning

confidence: 99%

“…Causality was studied by estimating transfer entropy computed by means of a non-parametric histogram methodology, using 4 equally spaced bins (see in [14]). Transfer entropies were computed for log-price returns and log-volume positive sentiment changes.…”

Section: Methodsmentioning

confidence: 99%

Cryptocurrency market structure: connecting emotions and economics

Aste

2019

Digit Finance

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We study the dependency and causality structure of the cryptocurrency market investigating collective movements of both prices and social sentiment related to almost two thousand cryptocurrencies traded during the first six months of 2018. This is the first study of the whole cryptocurrency market structure. It introduces several rigorous innovative methodologies applicable to this and to several other complex systems where a large number of variables interact in a non-linear way, which is a distinctive feature of the digital economy. The analysis of the dependency structure reveals that prices are significantly correlated with sentiment. The major, most capitalised cryptocurrencies, such as bitcoin, have a central role in the price correlation network but only a marginal role in the sentiment network and in the network describing the interactions between the two. The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions. Overall our study uncovers a complex and rich structure of interrelations where prices and sentiment influence each other both instantaneously and with lead-lag causal relations. A major finding is that minor currencies, with small capitalisation, play a crucial role in shaping the overall dependency and causality structure. Despite the high level of noise and the short time-series we verified that these networks are significant with all links statistically validated and with a structural organisation consistently reproduced across all networks.

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“…In an earlier and somewhat related work, Marschinski and Kantz [ 8 ] defined and used effective transfer entropy to quantify contagion in financial markets. Similarly, Tungsong et al [ 9 ] developed the previous work by Diebold and Yilmaz [ 10 ] in quantifying spillover effects between financial markets, generalizing the methodology and estimating the time evolution of interconnectedness between financial systems. Kyrtsou, C et al [ 11 ] introduced a Granger causality test using the nonlinear statistic of asymmetric partial transfer entropy to explore the complex relationships between the S&P500, VIX and volume.…”

Section: Introductionmentioning

confidence: 99%

Information Flow Analysis between EPU and Other Financial Time Series

Yao

2020

Entropy

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We investigate the strength and direction of information flow among economic policy uncertainty (EPU), US imports and exports to China, and the CNY/US exchange rate by using the novel concept of effective transfer entropy (ETE) with a sliding window methodology. We verify that this new method can capture dynamic orders effectively by validating them with the linear transfer entropy (TE) and Granger causality methods. Analysis shows that since 2016, US economic policy has contributed substantially to China-US bilateral trade and that China is making passive adjustments based on this trade volume. Unlike trade market conditions, China’s economic policy has significantly influenced the exchange rate fluctuation since 2016, which has, in turn, affected US economic policy.

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Relation between regional uncertainty spillovers in the global banking system

Cited by 12 publications

References 54 publications

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Cryptocurrency market structure: connecting emotions and economics

Information Flow Analysis between EPU and Other Financial Time Series

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