A comparison of volatility prediction between ARIMA-GARCH and VAR models
Abstract
In this thesis the authors use ARIMA-GARCH and VAR to predict future
volatility of 6 macroeconomic variables from the US. The data is monthly and
spans the period 1964-2014, where the last 20 years are used as the out-of-sample
period. The univariate GARCH models are widely used in volatility estimations
in the elds of macroeconomics and nance, and the authors feel that there are
better ways of predicting volatility. We nd that VAR outperform both GARCH
and EGARCH when it comes to predicting future volatility out-of-sample, which
is consistent with previous research. The naive model is found to outperform both
GARCH and EGARCH, while there is no conclusive answer for which GARCH
model is superior.
Description
Masteroppgave økonomi og administrasjon- Universitetet i Agder, 2015