Essays on Volatility Forecasting

Abstract

This dissertation has five chapters. The first chapter provides an overview of the topic and the dissertation. In the second chapter, which is jointly with Andrew Patton, we design a customized time series to images transformation method that is tailored specifically for volatility. We then apply convolution neural network models on the transformed volatility images and find the forecasting performance is significantly better than both the econometrics and machine learning benchmark models in classification and regression. Moreover, we also demonstrate that the customized time series to images transformation approach performs much better than the standard transformation that is off-the-shelf from the machine learning literature. In the third chapter, which is also jointly with Andrew Patton, we design machine learning algorithms to flexibly estimate the optimal bespoke weighting schemes for constructing RV measure that is tailored specifically to volatility forecasting applications. We find the bespoke RVs have very different schemes than the equal-weighting scheme in the standard RV estimator, and models using bespoke RVs have significant improvements in forecasting performance. Besides obtaining superior forecasting performance using bespoke RVs, we also open the black box and investigate the sources of forecast gains. Chapter 4 is jointly with Tim Bollerslev and Andrew Patton, where we combine hierarchical clustering methods with the realized semi-correlation to understand whether there are structural break changes in terms of the relationships among different stocks in the context of Covid-19. We design customized algorithms to detect the optimal number of clusters and structural break, and we find there is indeed a structural break related to the Covid-19 and interesting Covid related clusters emerged after the pandemic. Lastly, we show that by leveraging the realized-semicorrelation implied clusters, one could achieve better risk management objectives. Chapter 5 concludes.