Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies

In finance, volatility is defined as a measure of variation of a trading price series over time. As volatility is a latent variable, several measures, named proxies, have been proposed in the literature to represent such quantity. The purpose of our work is twofold. On one hand, we aim to perform a statistical assessment of the relationships among the most used proxies in the volatility literature. On the other hand, while the majority of the reviewed studies in the literature focuses on a univariate time series model (NAR), using a single proxy, we propose here a NARX model, combining two proxies to predict one of them, showing that it is possible to improve the prediction of the future value of some proxies by using the information provided by the others. Our results, employing artificial neural networks (ANN), k-Nearest Neighbours (kNN) and support vector regression (SVR), show that the supplementary information carried by the additional proxy could be used to reduce the forecasting error of the aforementioned methods. We conclude by explaining how we wish to further investigate such relationship.