| 000 | 02274nam a22002537a 4500 | ||
|---|---|---|---|
| 003 | ZW-GwMSU | ||
| 005 | 20240322084926.0 | ||
| 008 | 240322b |||||||| |||| 00| 0 eng d | ||
| 022 | _a02664666 | ||
| 040 |
_aMSU _bEnglish _cMSU _erda |
||
| 050 | 0 | 0 | _aHB139.T52 ECO |
| 100 | 1 |
_aMedeiros, Marcelo C _eauthor |
|
| 245 | 1 | 0 |
_aModeling multiple regimes in financial volatility with a flexible coefficient GARCH (1,1) model _ccreated by Marcelo C. Medeiros and Alvaro Veiga |
| 264 | 1 |
_aCambridge: _bCambridge University Press, _c2009. |
|
| 336 |
_2rdacontent _atext _btxt |
||
| 337 |
_2rdamedia _aunmediated _bn |
||
| 338 |
_2rdacarrier _avolume _bnc |
||
| 440 |
_aEconometric theory _vVolume 25, number 1 |
||
| 520 | 3 | _an this paper a flexible multiple regime GARCH(1,1)-type model is developed to describe the sign and size asymmetries and intermittent dynamics in financial volatility. The results of the paper are important to other nonlinear GARCH models. The proposed model nests some of the previous specifications found in the literature and has the following advantages. First, contrary to most of the previous models, more than two limiting regimes are possible, and the number of regimes is determined by a simple sequence of tests that circumvents identification problems that are usually found in nonlinear time series models. The second advantage is that the novel stationarity restriction on the parameters is relatively weak, thereby allowing for rich dynamics. It is shown that the model may have explosive regimes but can still be strictly stationary and ergodic. A simulation experiment shows that the proposed model can generate series with high kurtosis and low first-order autocorrelation of the squared observations and exhibit the so-called Taylor effect, even with Gaussian errors. Estimation of the parameters is addressed, and the asymptotic properties of the quasi-maximum likelihood estimator are derived under weak conditions. A Monte-Carlo experiment is designed to evaluate the finite-sample properties of the sequence of tests. Empirical examples are also considered. | |
| 650 |
_aFinancial economics _vVolatility _xARCH model |
||
| 700 | 1 |
_aVeiga, Alvaro _eco author |
|
| 856 | _uhttps://doi.org/10.1017/S026646660809004X | ||
| 942 |
_2lcc _cJA |
||
| 999 |
_c164495 _d164495 |
||