Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data
IntroductionRealized volatility analysis of assets in the Brazilian market within a multivariate framework is the focus of this study. Despite the success of volatility models in univariate scenarios, challenges arise due to increasing dimensionality of covariance matrices and lower asset liquidity...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2024.1379891/full |
| _version_ | 1797221726469226496 |
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| author | Leonardo Ieracitano Vieira Márcio Poletti Laurini |
| author_facet | Leonardo Ieracitano Vieira Márcio Poletti Laurini |
| author_sort | Leonardo Ieracitano Vieira |
| collection | DOAJ |
| description | IntroductionRealized volatility analysis of assets in the Brazilian market within a multivariate framework is the focus of this study. Despite the success of volatility models in univariate scenarios, challenges arise due to increasing dimensionality of covariance matrices and lower asset liquidity in emerging markets.MethodsIn this study, we utilize intraday stock trading data from the Brazilian Market to compute daily covariance matrices using various specifications. To mitigate dimensionality issues in covariance matrix estimation, we implement penalization restrictions on coefficients through regressions with shrinkage techniques using Ridge, LASSO, or Elastic Net estimators. These techniques are employed to capture the dynamics of covariance matrices.ResultsComparison of covariance construction models is performed using the Model Confidence Set (MCS) algorithm, which selects the best models based on their predictive performance. The findings indicate that the method used for estimating the covariance matrix significantly impacts the selection of the best models. Additionally, it is observed that more liquid sectors demonstrate greater intra-sectoral dynamics.DiscussionWhile the results benefit from shrinkage techniques, the high correlation between assets presents challenges in capturing stock or sector idiosyncrasies. This suggests the need for further exploration and refinement of methods to better capture the complexities of volatility dynamics in emerging markets like Brazil. |
| first_indexed | 2024-04-24T13:10:01Z |
| format | Article |
| id | doaj.art-c6fd3a9a08a04af5b51888a5c0449563 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-24T13:10:01Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-c6fd3a9a08a04af5b51888a5c04495632024-04-05T04:55:12ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872024-04-011010.3389/fams.2024.13798911379891Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market dataLeonardo Ieracitano VieiraMárcio Poletti LauriniIntroductionRealized volatility analysis of assets in the Brazilian market within a multivariate framework is the focus of this study. Despite the success of volatility models in univariate scenarios, challenges arise due to increasing dimensionality of covariance matrices and lower asset liquidity in emerging markets.MethodsIn this study, we utilize intraday stock trading data from the Brazilian Market to compute daily covariance matrices using various specifications. To mitigate dimensionality issues in covariance matrix estimation, we implement penalization restrictions on coefficients through regressions with shrinkage techniques using Ridge, LASSO, or Elastic Net estimators. These techniques are employed to capture the dynamics of covariance matrices.ResultsComparison of covariance construction models is performed using the Model Confidence Set (MCS) algorithm, which selects the best models based on their predictive performance. The findings indicate that the method used for estimating the covariance matrix significantly impacts the selection of the best models. Additionally, it is observed that more liquid sectors demonstrate greater intra-sectoral dynamics.DiscussionWhile the results benefit from shrinkage techniques, the high correlation between assets presents challenges in capturing stock or sector idiosyncrasies. This suggests the need for further exploration and refinement of methods to better capture the complexities of volatility dynamics in emerging markets like Brazil.https://www.frontiersin.org/articles/10.3389/fams.2024.1379891/fullrealized volatilityshrinkagehigh-frequency datapenalized estimationLASSORidge |
| spellingShingle | Leonardo Ieracitano Vieira Márcio Poletti Laurini Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data Frontiers in Applied Mathematics and Statistics realized volatility shrinkage high-frequency data penalized estimation LASSO Ridge |
| title | Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data |
| title_full | Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data |
| title_fullStr | Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data |
| title_full_unstemmed | Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data |
| title_short | Multivariate realized volatility: an analysis via shrinkage methods for Brazilian market data |
| title_sort | multivariate realized volatility an analysis via shrinkage methods for brazilian market data |
| topic | realized volatility shrinkage high-frequency data penalized estimation LASSO Ridge |
| url | https://www.frontiersin.org/articles/10.3389/fams.2024.1379891/full |
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