methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

applied within a Bayesian analysisof a GARCH-mixture model which is used for the evaluation of theValue-at-Risk of the return …

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. …

, namely, GARCH (1,1), GJR (1,1) and EGARCH (1,1), are used to estimate the abnormal rate of change in the number of tourists …

for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out …

parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids …

This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH … empirical application to S&P index log-returns. Several non-nested GARCH-type models are estimated and combined to predict the …