Showing 1 - 10 of 80
dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …
Persistent link: https://www.econbiz.de/10010384110
Persistent link: https://www.econbiz.de/10012792858
Persistent link: https://www.econbiz.de/10009355592
Persistent link: https://www.econbiz.de/10011389921
In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] in daily data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a LM test that is resistant to patches of additive outliers. The data span...
Persistent link: https://www.econbiz.de/10011284080
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
. The methodology is hybrid because it combines a formaltesting procedure with volatility curve pattern recognition based …
Persistent link: https://www.econbiz.de/10011299968
Persistent link: https://www.econbiz.de/10011300485
volatility of individual stock returns and exchange rate returns. …
Persistent link: https://www.econbiz.de/10011332948
Persistent link: https://www.econbiz.de/10009665042