Bom dia a todos,

Teremos esta semana, na quinta-feira dia 2 de Novembro, pelas 15:00,
um seminário por Jacopo De Stefani:

Title: Machine Learning for Multi-step Ahead Forecasting of Volatility
Proxies
by Jacopo De Stefani
Machine Learning Group, Université Libre de Bruxelles, Belgium

Abstract: 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.

For further information about Jacopo’s research, you can also have a
look his website: http://jdestefani.github.io

Saudações,

Alexandre Francisco