1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002
Keywords
garch, futures contracts, volatility, rolling regressions
Start Date
1-7-2002 12:00 AM
Abstract
Recently the modelling and forecasting of volatility has received much attention in the literature. Asvolatility is generally unobservable, it must be estimated. The GARCH(1,1) specification remains the mostwidely used time-varying financial volatility model in practice. This paper evaluates the adequacy andeffectiveness of AR(1)-GARCH(1,1) in modelling and forecasting volatility in daily price returns on futurescontracts for the two most important metals traded on the London Metal Exchange, namely aluminium andcopper. The empirical analysis examines the properties of parameter estimates, robust t-ratios, momentconditions, and forecasts derived from rolling regressions.
Modelling Time-Varying Volatility in Non-Ferrous Metals Markets
Recently the modelling and forecasting of volatility has received much attention in the literature. Asvolatility is generally unobservable, it must be estimated. The GARCH(1,1) specification remains the mostwidely used time-varying financial volatility model in practice. This paper evaluates the adequacy andeffectiveness of AR(1)-GARCH(1,1) in modelling and forecasting volatility in daily price returns on futurescontracts for the two most important metals traded on the London Metal Exchange, namely aluminium andcopper. The empirical analysis examines the properties of parameter estimates, robust t-ratios, momentconditions, and forecasts derived from rolling regressions.
