The Extent of Volatility Predictability Evaluation of forecasting accuracy dependent on time, distribution and model order
Abstract
This thesis focuses on the accuracy and ability of out-of-sample volatility forecasting
over different time horizons. Using data at daily frequency we forecast the future
volatility over multiple time horizons (1, 3, 6, 9 and 12 months) and evaluate the
goodness of forecasting by comparing the Naïve, ARCH, GARCH, EGARCH and
GJR-GARCH models using the MSE and the Predictive Power (P). We include
different probability distributions for the error terms in an attempt to improve the
models accuracy. The research is conducted using three indices: FTSE 100, S&P 500
and the Hang Seng. We find that the goodness of forecasting accuracy decreases
dramatically after the 3 month horizon and the selection of a more representative error
distribution improves the accuracy of the short term forecasts. The results also show
that the higher order GARCH models, beyond (1,1), do not improve the forecasting
accuracy.
Description
Masteroppgave økonomi og administrasjon- Universitetet i Agder, 2015