Abstract: The accurate estimation and forecasting of volatility is of utmost importance for anyone who
participates in the financial market as it affects the whole financial system and, consequently,
the whole economy. It has been a popular subject of research with no general conclusion as to
which model provides the most accurate forecasts. This thesis enters the ongoing debate by
assessing and comparing the forecasting performance of popular volatility models. Moreover,
the role of key parameters of volatility is evaluated in improving the forecast accuracy of
the models. For these purposes a number of US and European stock indices is used. The
main contributions are four. First, I find that implied volatility can be per se forecasted
and combining the information of implied volatility and GARCH models predict better the
future volatility. Second, the GARCH class of models are superior to the stochastic volatility
models in forecasting the one-, five- and twenty two-days ahead volatility. Third, when the
realised volatility is modelled and forecast directly using time series, I find that the HAR model
performs better than the ARFIMA. Finally, I find that the leverage effect and implied volatility
significantly improve the fit and forecasting performance of all the models.
Publication Year: 2016
Publication Date: 2016-09-01
Language: en
Type: dissertation
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