Name
Abstract | ||
|---|---|---|
Financial returns are often modeled as autoregressive time series with innovations having conditional heteroscedastic variances, especially with GARCH processes. The conditional distribution in GARCH models is assumed to follow a parametric distribution. Typically, this error distribution is selected without justification. In this paper, we have applied the results of Thavaneswaran and Ghahramani [A. Thavaneswaran, M. Ghahramani, Applications of combining estimating functions, in: Proceedings of the International Sri Lankan Conference: Visions of Futuristic Methodologies, University of Peradeniya and Royal Melbourne Institute of Technology (RMIT), 2004, pp. 515-532] on identification of GARCH models to a number of financial data sets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.camwa.2007.10.001 | Computers & Mathematics with Applications |
Keywords | Field | DocType |
m. ghahramani,garch,least squares,least absolute deviation,futuristic methodologies,garch model,parametric distribution,financial data set,financial return,conditional distribution,international sri lankan conference,conditional heteroscedastic variance,model identification,garch model identification,error distribution,estimating functions,time series | Econometrics,Autoregressive model,Heteroscedasticity,Conditional probability distribution,Parametric statistics,Least absolute deviations,Autoregressive conditional heteroskedasticity,System identification,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 11 | Computers and Mathematics with Applications |
Citations | PageRank | References |
1 | 0.48 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ghahramani | 1 | 4 | 1.56 |
| A. Thavaneswaran | 2 | 130 | 21.94 |