Essays on Realised Volatility Forecasting for International Stock Markets

Abstract

Modelling and forecasting market volatility is an important topic within finance research, with the aim of producing accurate forecasts, as confirmed by the plethora of academic papers written over the past few decades. Understanding volatility is crucial for market participants such as investors, policymakers, and academics. The linear Heterogeneous Autoregressive (HAR) model currently dominates the volatility models for forecasting Realised Volatility (RV). This thesis enters the ongoing volatility forecasting debate by developing further the HAR model. First, within the HAR setting volatility jumps, realised semi-variance and the leverage effect are added. With the use of a selection of loss functions and forecasting comparisons it is found that adding the leverage effect into the HAR model can produce the most accurate forecasts over daily, weekly, and monthly horizons. Second, this thesis compares the foresting ability of the Autoregressive (AR) model with flexible lags, generated by the Least Absolute Shrinkage & Selection Operator (Lasso) approach (es), to the HAR model with a fixed lag structure. In-sample results show the Lasso approach to improve the model fitness, and the out-of-sample results indicate a more flexible lag structure is preferred, especially the ordered Lasso performs the best. Third, this thesis incorporates the Smooth Transition and Markov-switching approaches with the linear HAR model in a further forecasting exercise. In-sample results show that the regime-switching models provide better estimation accuracy than the linear HAR model. For the out-of-sample results, although the regime-switching models have limited forecasting ability over the daily horizon, these do outperform the linear HAR model over weekly and monthly horizons. The Markov-switching model is found to be the best, by consistently exhibiting the most accurate forecasts over time. All the above findings have been evaluated within a risk management setting (Value at Risk & Expected Shortfall).