Can Model Combination Improve Volatility Forecasting?
|Author:||Mgr. Sabyrzhan Tyuleubekov|
|Year:||2019 - summer|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||Nowadays, there is a wide range of forecasting methods and forecasters encounter several
challenges during selection of an optimal method for volatility forecasting. In order to make use of
wide selection of forecasts, this thesis tests multiple forecast combination methods.
Notwithstanding, there exists a plethora of forecast combination literature, combination of
traditional methods with machine learning methods is relatively rare. We implement the following
combination techniques: (1) simple mean forecast combination, (2) OLS combination, (3) ARIMA
on OLS combined fit, (4) NNAR on OLS combined fit and (5) KNN regression on OLS combined
fit. To our best knowledge, the latter two combination techniques are not yet researched in
academic literature. Additionally, this thesis should help a forecaster with three choice
complication causes: (1) choice of volatility proxy, (2) choice of forecast accuracy measure and
(3) choice of training sample length. We found that squared and absolute return volatility proxies
are much less efficient than Parkinson and Garman-Klass volatility proxies. Likewise, we show
that forecast accuracy measure (RMSE, MAE or MAPE) influences optimal forecasts ranking.
Finally, we found that though forecast quality does not depend on training sample length, we see
that forecast combination methods outperform standalone methods on a longer training sample.
Finally, we found that KNN regression on OLS combined fit on medium training sample
outperforms other methods for Garman-Klass volatility estimate.