Showing 1 - 8 of 8
three ways: by cross-section dispersion of optimism expectations, by a GARCH series based on the optimism data and by an …
Persistent link: https://www.econbiz.de/10005342212
This paper introduces a nonparametric estimator for tail dependence in the constant conditional correlation GARCH … difference between the distribution of the tail dependence estimator under the iid and GARCH case is a scaling variance. Without …
Persistent link: https://www.econbiz.de/10005342216
Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial …
Persistent link: https://www.econbiz.de/10005342298
on the foreign exchange market. By high-frequency methodology, GARCH estimation and variance-ratio tests, the existence …
Persistent link: https://www.econbiz.de/10005342336
When a price limit regime exists for all of the stocks involved in an index, the index return is an aggregate of limited variables and thereby it is restricted to the same limits. We argue that neither a censored nor a truncated distribution model is appropriate for the aggregate return. The...
Persistent link: https://www.econbiz.de/10005342370
The properties and applications of the normal log-normal (NLN) mixture are considered. The moment of the NLN mixture is shown to be finite for any positive order. The expectations of exponential functions of a NLN mixture variable are also investigated. The kurtosis and skewness of the NLN...
Persistent link: https://www.econbiz.de/10005063629
In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the...
Persistent link: https://www.econbiz.de/10005063668
Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial …
Persistent link: https://www.econbiz.de/10005063716