CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishOne of the main problems in modelling multivariate conditional covariance time series is the parameterization of the correlation structure. If no constraints are imposed, it implies a large number of unknown coefficients. The most popular models propose parsimonious representations, imposing similar correlation structures to all the series or to groups of time series, but the choice of these groups is quite subjective. A statistical approach is proposed to detect groups of homogeneous time series in terms of correlation dynamics for one of the widely used models: the Dynamic Conditional Correlation model. The approach is based on a clustering algorithm, which uses the idea of distance between dynamic conditional correlations, and the classical Wald test, to compare the coefficients of two groups of dynamic conditional correlations. The proposed approach is evaluated in terms of simulation experiments and applied to a set of financial time series.
Identifying Financial Time Series with Similar Dynamic Conditional Correlation / E. OTRANTO. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 54(2010), pp. 1-15.
| Titolo: | Identifying Financial Time Series with Similar Dynamic Conditional Correlation |
| Autori: | |
| Data di pubblicazione: | 2010 |
| Rivista: | |
| Citazione: | Identifying Financial Time Series with Similar Dynamic Conditional Correlation / E. OTRANTO. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 54(2010), pp. 1-15. |
| Abstract: | One of the main problems in modelling multivariate conditional covariance time series is the parameterization of the correlation structure. If no constraints are imposed, it implies a large number of unknown coefficients. The most popular models propose parsimonious representations, imposing similar correlation structures to all the series or to groups of time series, but the choice of these groups is quite subjective. A statistical approach is proposed to detect groups of homogeneous time series in terms of correlation dynamics for one of the widely used models: the Dynamic Conditional Correlation model. The approach is based on a clustering algorithm, which uses the idea of distance between dynamic conditional correlations, and the classical Wald test, to compare the coefficients of two groups of dynamic conditional correlations. The proposed approach is evaluated in terms of simulation experiments and applied to a set of financial time series. |
| Handle: | http://hdl.handle.net/11570/1910169 |
| Appare nelle tipologie: | 14.a.1 Articolo su rivista |
File in questo prodotto:
http://hdl.handle.net/11570/1910169
Copyright © 2021