| dc.description.abstract | Extensive research has been done within the field of finance to better predict future volatility
and anticipate changes in financial market uncertainty. The advent of more advanced machine
learning methods, such as artificial neural networks, has led to ground-breaking
improvements to modeling capabilities across many fields and industries, including finance
and volatility forecasting. These advances have led to rendering some of the previous state of
the art models obsolete. Even though it has been established that artificial neural networks are
capable of outperforming traditional finance forecasting models when it comes to volatility
forecasting, it remains an open question whether a more advanced machine learning algorithm
can benefit from incorporating the strengths of specialized volatility forecasting models. In
this study, we seek to uncover whether traditional finance volatility forecasting models, such
as GARCH type models, contain unique information that when combined with artificial
neural networks can lead to more capable models and improved prediction accuracy.
We will explore these effects by looking into S&P 500 one-day-ahead volatility using
GARCH type models to generate volatility forecasts and include those into different artificial
neural networks to measure improvements in forecasting capabilities. GARCH forecasts will
be added into the different artificial neural networks in the form of two different types of
ensemble models. One approach being a stacked ensemble, and the other an averaging
ensemble. We find evidence to suggest that even though the GARCH type models
consistently underperform compared to artificial neural networks, there is sufficient grounds
to conclude that there is great potential in combining different volatility forecasting models to
attain better volatility predictions. | en_US |
