analysing correlation matrices derived from rolling AR(1)-generalised autoregressive conditional heteroskedasticity (GARCH)(1 …,1) model estimates. The second moment properties of a linear aggregate of ARMA processes with GARCH errors are analysed and …

volatility over time. This paper uses a rolling AR(1)-GARCH(1,1) model to estimate and forecast the volatility processes for … variation over time seen in the volatility processes, as modelled by GARCH, suggest that, while volatility in returns has not …

the symmetric AR(1)-GARCH(1, 1), the asymmetric AR(1)-GJR(1, 1), and asymmetric AR(1)-EGARCH(1, 1). Of these, the …

, bond and foreign exchange. Two countries are considered, namely Australia and New Zealand. Forecasting will be conducted … using three multivariate GARCH models, namely the CCC model [T. Bollerslev, Modelling the coherence in short-run nominal … exchange rates: a multivariate generalized ARCH model, Rev. Econ. Stat. 72 (1990) 498–505], VARMA-GARCH model [S. Ling, M …

The challenge of modeling, estimating, testing, and forecasting financial volatility is both intellectually worthwhile …

assessment of the implications of modelling conditional volatility on forecasting performance. The estimated conditional …

model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting …

compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi …