Market structure dynamics during COVID-19 outbreak

Procacci, Pier Francesco; Phelan, Carolyn E.; Aste, Tomaso

doi:10.48550/arxiv.2003.10922

Cited by 4 publications

(6 citation statements)

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“…This is in contrast to time series models such as TAR [42], which are often unable to identify structural breaks, and time series clustering techniques [43][44][45], which are highly susceptible to the curse of dimensionality. In [46], which considers equities traded in the US market, the method is able to distinguish a market state associated with both the 2008 crisis period and the COVID-19 as a distinct state from the long 'bull' period post 2008. ICC was also applied in a recent note in relation to the COVID-19 pandemic [46] to identify inherent market structures.…”

Section: Identification Of Different Market Statesmentioning

confidence: 99%

“…In [46], which considers equities traded in the US market, the method is able to distinguish a market state associated with both the 2008 crisis period and the COVID-19 as a distinct state from the long 'bull' period post 2008. ICC was also applied in a recent note in relation to the COVID-19 pandemic [46] to identify inherent market structures.…”

Section: Identification Of Different Market Statesmentioning

confidence: 99%

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Quantifying impact and response in markets using information filtering networks

2022

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We present a novel methodology to quantify the `impact' of and `response' to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an $L_0$-norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock {\color{black} log-returns}. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.

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Section: Identification Of Different Market Statesmentioning

confidence: 99%

Section: Identification Of Different Market Statesmentioning

confidence: 99%

Quantifying impact and response in markets using information filtering networks

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is in contrast to time series models such as TAR [25], which are often unable to identify structural breaks, and time series clustering techniques [26,27,28], which are highly susceptible to the curse of dimensionality. In [29], which considers equities traded in the US market, the method is able to distinguish a market state associated with both the 2008 crisis period and the COVID-19 as a distinct state from the long 'bull' period post 2008. ICC was also applied in a recent note in relation to the COVID-19 pandemic [29] to identify inherent market structures.…”

Section: Identification Of Different Market Statesmentioning

confidence: 99%

“…In [29], which considers equities traded in the US market, the method is able to distinguish a market state associated with both the 2008 crisis period and the COVID-19 as a distinct state from the long 'bull' period post 2008. ICC was also applied in a recent note in relation to the COVID-19 pandemic [29] to identify inherent market structures.…”

Section: Identification Of Different Market Statesmentioning

confidence: 99%

An Information Filtering approach to stress testing: an application to FTSE markets

Seabrook¹,

Caccioli²,

Aste³

2021

Preprint

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We present a novel methodology to quantify the 'impact' of and 'response' to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an L 0 -norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock returns. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.

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“…And it seems that Bitcoin is going to fall into a new Great recession along with the most influential stock indexes. As it was noted by Pier Francesco Procacci, Carolyn E. Phelan and Tomaso Aste [5], the crisis-state representative and eventually becomes extremely dominant in March, but a lot of factors have to be included to make an adequate analysis of this problem.…”

Section: Introductionmentioning

confidence: 99%

Coverage of the Coronavirus Pandemic through Entropy Measures

Soloviev¹,

Bielinskyi²,

Kharadzjan³

2021

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The rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as awhole. Further instability related to COVID-19will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change. Trying to help people in their subsequent decisions, we demonstrate the possibility of constructing indicators of critical and crash phenomena on the example of Bitcoin market crashes for further demonstration of their efficiency on the crash that is related to the coronavirus pandemic. For this purpose, the methods of the theory of complex systems have been used. Since the theory of complex systems has quite an extensive toolkit for exploring the nonlinear complex system, we take a look at the application of the concept of entropy in finance and use this concept to construct 6 effective entropy measures: Shannon entropy, Approximate entropy, Permutation entropy, and 3 Recurrence based entropies. We provide computational results that prove that these indicators could have been used to identify the beginning of the crash and predict the future course of events associated with the current pandemic.

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Market structure dynamics during COVID-19 outbreak

Cited by 4 publications

References 1 publication

Quantifying impact and response in markets using information filtering networks

Quantifying impact and response in markets using information filtering networks

An Information Filtering approach to stress testing: an application to FTSE markets

Coverage of the Coronavirus Pandemic through Entropy Measures

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