Name
Abstract | ||
|---|---|---|
Both volatility clustering and conditional nonormality can induce the leptokurtosis typically observed in financial data. An ARMA representation is used to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Formula for autocorrelations of the power GARCH process |yt|^@d are given in terms of @j-weights. The kurtosis is also derived for random coefficient GARCH, nonstationary GARCH with possibly nonnormal errors and for hidden Markov GARCH models. The theoretical autocorrelation functions for various GARCH(1,1) models are also derived. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.mcm.2004.02.032 | Mathematical and Computer Modelling |
Keywords | Field | DocType |
garch,power garch process,various class,non-gaussian garch,power garch and general garch(1,stochastic volatility,power garch,hidden markov garch model,garch model,arma representation,random coefficient garch model,kurtosis,various garch,random coefficient,nonstationary garch,1) model,autocorrelation function | Econometrics,Stochastic volatility,Multilevel model,Volatility clustering,Autoregressive conditional heteroskedasticity,Hidden Markov model,Kurtosis,Mathematics,Autocorrelation | Journal |
Volume | Issue | ISSN |
41 | 6-7 | Mathematical and Computer Modelling |
Citations | PageRank | References |
9 | 3.42 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Thavaneswaran | 1 | 130 | 21.94 |
| S. S. Appadoo | 2 | 14 | 4.57 |
| M. Samanta | 3 | 9 | 3.42 |