Detecting outliers in multivariate volatility models: A wavelet procedure
It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the esti...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/362068 |
| Acceso en línea: | https://hdl.handle.net/2117/362068 |
| Access Level: | acceso abierto |
| Palabra clave: | Correlations multivariate GARCH models outliers wavelets Estadística matemàtica Anàlisi multivariable Anàlisi numèrica Matemàtica financera Classificació AMS::62 Statistics::62M Inference from stochastic processes Classificació AMS::62 Statistics::62G Nonparametric inference Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations Classificació AMS::62 Statistics::62H Multivariate analysis Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers. |
|---|