Showing 1 - 10 of 21
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011257033
In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is...
Persistent link: https://www.econbiz.de/10005281817
ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance densities) areinvestigated in …
Persistent link: https://www.econbiz.de/10011256653
. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed disturbancedensities …
Persistent link: https://www.econbiz.de/10011257188
methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance …
Persistent link: https://www.econbiz.de/10005137067
Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed …
Persistent link: https://www.econbiz.de/10005137117
estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient …-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns. …
Persistent link: https://www.econbiz.de/10011255484
applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …
Persistent link: https://www.econbiz.de/10011256462
-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between …
Persistent link: https://www.econbiz.de/10011256766
predicts MA(1) structure with a negative coeffient. Asynchronous updating leads to an MA(1) model for returns with GARCH($1 …,1$) innovations, and predicts a relation between the ARCH and GARCH coefficients. Heterogeneity in memory leads to long … coefficient and the relation between the ARCH and GARCH coefficients for exchange rate data. …
Persistent link: https://www.econbiz.de/10011256802