Showing 1 - 10 of 162
for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH …
Persistent link: https://www.econbiz.de/10011518597
Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock...
Persistent link: https://www.econbiz.de/10011571257
for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH …
Persistent link: https://www.econbiz.de/10011640555
, including the diagonal BEKK model of Baba et al. (1985) and Engle and Kroner (1995), VARMA-GARCH model of Ling and McAleer (2003 …
Persistent link: https://www.econbiz.de/10011531101
The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical...
Persistent link: https://www.econbiz.de/10011531127
Persistent link: https://www.econbiz.de/10011542373
Persistent link: https://www.econbiz.de/10010412920
markets over the period 1998-2014 with a DCC-GARCH model. We look at the factors influencing those correlations, adopting a …
Persistent link: https://www.econbiz.de/10011451631
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
Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The …
Persistent link: https://www.econbiz.de/10013040932