Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility
Abstract
We are interested in discovering expressions for financial prediction using
Nested Monte Carlo Search and Genetic Programming. Both methods are applied
to learning from financial time series to generate nonlinear functions for
market volatility prediction. The input data, that is a series of daily prices of European
S&P500 index, is filtered and sampled in order to improve the training
process. Using some assessment metrics, the best generated models given by both
approaches for each training sub-sample, are evaluated and compared. Results
show that Nested Monte Carlo is able to generate better forecasting models than
Genetic Programming for the majority of learning samples.