Forecasting Volatility: A survey on the forecasting capability of Autoregressive Conditional Heteroskedasticity-models and Historical Volatility-models
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2020-08-21
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en
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Volatility Forecasting is arguably one of the most pivotal aspects of empirical financial asset
pricing and risk management. During the 2008 Financial Crisis it became evident that many standard
forecasting models where inadequate to predict severe volatility. This thesis attempts to provide a
thorough survey on existing volatility forecasting literature. It expands on the topic by comparing different
forecasting models by means of a Root-Mean-Square analysis. Forecasts have been performed both insample
and out-of-sample. It then focusses on the effects that certain market conditions might have on the
forecasting capability of the models. By taking advantage of a more recent dataset it utilizes the COVID-
19 pandemic of 2020 as a turbulent sample period. This thesis has found that exogenous shocks severely
impact the forecasting capability of all addressed forecasting models. The HIS-models in particular are
heavily impacted by these shocks. Furthermore, HIS-models appear poor out-of-sample estimators
compared to ARCH-models. The thesis concludes that the GJR-GARCH 1.1 model appears to be the best
forecasting model out there. In contrast, it is concluded that the Naïve method of volatility forecasting is
the worst model available.
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Faculteit der Managementwetenschappen