Volatility plays an important role in option pricing and risk management. It
is crucial that volatility is modelled as accurately as possible in order to forecast
with confidence. The challenge is in the selection of the ‘best’ model with so many
available models and selection criteria. The Model Confidence Set (MCS) solves
this problem by choosing a group of models that are equally good. A set of GARCH
models were estimated for several JSE indices and the MCS was used to trim the
group of models to a subset of equally superior models. Using the Mean Squared
Error to evaluate the relative performance of the MCS, GARCH (1,1) and Random
Walk, it was found that the MCS, with an equally weighted combination of models,
performed better than the GARCH (1,1) and Random Walk for instances where
volatility in the returns data was high. For instances of low volatility in the returns,
the GARCH (1,1) had superior 5-day forecasts but the MCS had better performance
for 10-days and greater. The EGARCH (2,1) volatility model was selected by the
MCS for 5 out of the 6 indices as the most superior model. The Random Walk was
shown to have better long term forecasting performance.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/15056 |
| Date | 29 July 2014 |
| Creators | Song, Matthew |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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