Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution
The financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under di...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/10/1307 |
| _version_ | 1797514631072186368 |
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| author | Mauricio A. Valle Jaime F. Lavín Nicolás S. Magner |
| author_facet | Mauricio A. Valle Jaime F. Lavín Nicolás S. Magner |
| author_sort | Mauricio A. Valle |
| collection | DOAJ |
| description | The financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under different high and low return volatility episodes at the 2008 Subprime Crisis and the 2020 COVID-19 outbreak. We carry out an inference process to identify the interactions, in which we implement the a pairwise Ising distribution model describing the first and second moments of the distribution of the discretized returns of each asset. Our results indicate that second-order interactions explain more than 80% of the entropy in the system during the Subprime Crisis and slightly higher than 50% during the COVID-19 outbreak independently of the period of high or low volatility analyzed. The evidence shows that during these periods, slight changes in the second-order interactions are enough to induce large changes in assets correlations but the proportion of positive and negative interactions remains virtually unchanged. Although some interactions change signs, the proportion of these changes are the same period to period, which keeps the system in a ferromagnetic state. These results are similar even when analyzing triadic structures in the signed network of couplings. |
| first_indexed | 2024-03-10T06:34:20Z |
| format | Article |
| id | doaj.art-0c5be51d9ba048558bf55bd67cf0b440 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:34:20Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0c5be51d9ba048558bf55bd67cf0b4402023-11-22T18:11:00ZengMDPI AGEntropy1099-43002021-10-012310130710.3390/e23101307Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise DistributionMauricio A. Valle0Jaime F. Lavín1Nicolás S. Magner2Facultad de Economía y Negocios, Universidad Finis Terrae, Santiago 7501015, ChileEscuela de Negocios, Universidad Adolfo Ibáñez, Santiago 7941169, ChileFacultad de Economía y Empresa, Universidad Diego Portales, Santiago 8370109, ChileThe financial market is a complex system in which the assets influence each other, causing, among other factors, price interactions and co-movement of returns. Using the Maximum Entropy Principle approach, we analyze the interactions between a selected set of stock assets and equity indices under different high and low return volatility episodes at the 2008 Subprime Crisis and the 2020 COVID-19 outbreak. We carry out an inference process to identify the interactions, in which we implement the a pairwise Ising distribution model describing the first and second moments of the distribution of the discretized returns of each asset. Our results indicate that second-order interactions explain more than 80% of the entropy in the system during the Subprime Crisis and slightly higher than 50% during the COVID-19 outbreak independently of the period of high or low volatility analyzed. The evidence shows that during these periods, slight changes in the second-order interactions are enough to induce large changes in assets correlations but the proportion of positive and negative interactions remains virtually unchanged. Although some interactions change signs, the proportion of these changes are the same period to period, which keeps the system in a ferromagnetic state. These results are similar even when analyzing triadic structures in the signed network of couplings.https://www.mdpi.com/1099-4300/23/10/1307return volatilitiesmaximum entropy principlefinancial crisispairwise interactionsfrustrationKullback-Leibler divergence |
| spellingShingle | Mauricio A. Valle Jaime F. Lavín Nicolás S. Magner Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution Entropy return volatilities maximum entropy principle financial crisis pairwise interactions frustration Kullback-Leibler divergence |
| title | Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution |
| title_full | Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution |
| title_fullStr | Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution |
| title_full_unstemmed | Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution |
| title_short | Equity Market Description under High and Low Volatility Regimes Using Maximum Entropy Pairwise Distribution |
| title_sort | equity market description under high and low volatility regimes using maximum entropy pairwise distribution |
| topic | return volatilities maximum entropy principle financial crisis pairwise interactions frustration Kullback-Leibler divergence |
| url | https://www.mdpi.com/1099-4300/23/10/1307 |
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