Showing 1 - 10 of 11
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...
Persistent link: https://www.econbiz.de/10010325845
We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
Persistent link: https://www.econbiz.de/10010326055
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...
Persistent link: https://www.econbiz.de/10011380135
Persistent link: https://www.econbiz.de/10009720703
We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels,...
Persistent link: https://www.econbiz.de/10010364103
We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...
Persistent link: https://www.econbiz.de/10011531139
Persistent link: https://www.econbiz.de/10010494787
We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
Persistent link: https://www.econbiz.de/10011386468
We consider a new copula method for mixed marginals of discrete and continuous random variables. Unlike the Bayesian methods in the literature, we use maximum likelihood estimation based on closed-form copula functions. We show with a simulation that our methodology performs similar to the...
Persistent link: https://www.econbiz.de/10010464789
Persistent link: https://www.econbiz.de/10010191403