Publicação
The detection and estimation of long memory in stochastic volatility
| dc.contributor.author | Breidt, F. Jay | |
| dc.contributor.author | Crato, Nuno | |
| dc.contributor.author | Lima, Pedro de | |
| dc.date.accessioned | 2023-05-02T10:28:46Z | |
| dc.date.available | 2023-05-02T10:28:46Z | |
| dc.date.issued | 1998 | |
| dc.description.abstract | We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV model over existing (short-memory) volatility models. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Breidt, F. Jay; Nuno Crato and Pedro de Lima .(1998). “The detection and estimation of long memory in stochastic volatility”. Journal of Econometrics, Vol. 83: pp. 325-348. (Search PDF in 2023). | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/27684 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.subject | Fractional ARMA | pt_PT |
| dc.subject | EGARCH | pt_PT |
| dc.subject | Spectral Likelihood Estimators | pt_PT |
| dc.title | The detection and estimation of long memory in stochastic volatility | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |