Seabrook, Isobel;
Caccioli, Fabio;
Aste, Tomaso;
(2022)
Quantifying impact and response in markets using information filtering networks.
Journal of Physics: Complexity
, 3
(2)
, Article 025004. 10.1088/2632-072x/ac6721.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Quantifying impact and response in markets using information filtering networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/2632-072x/ac6721 |
| Publisher version: | https://doi.org/10.1088/2632-072x/ac6721 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Stress testing, Systemic risk, Elliptical conditional probability |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147939 |
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