UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Poster Paper

Shawash, J; Selviah, DR; (2010) FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Poster Paper. Presented at: Algorithmic Trading - Future Directions and Opportunities for Research, University College London, London, UK. Green open access

[img]
Preview
PDF
Poster_Algorithmic_Trading_ver2.pdf
Available under License : See the attached licence file.

Download (1MB)

Abstract

This poster paper compares Higher Order Neural Networks (HONN) with Neural Networks, and linear regression for short term forecasting of stock market index daily returns. Two new HONNs, the Correlation HONN (CHONN) and the Horizontal HONN (HorizHONN) outperform all other models tested in terms of the Akaike Information Criterion, out-of-sample root mean square error, of FTSE100 and NASDAQ giving out-of-sample Hit Rates of up to 60% with AIC improvement up to 6.2%. New hybrid models for volatility estimation are formed by combining CHONN with E/GARCH are compared with conventional EGARCH, providing up to 2.1% and 2.7% AIC improvement for FTSE100 and NASDAQ.

Type: Conference item (UNSPECIFIED)
Title: FTSE 100 Returns and Volatility estimation using Higher Order Neural Networks - Poster Paper
Event: Algorithmic Trading - Future Directions and Opportunities for Research
Location: University College London, London, UK
Open access status: An open access version is available from UCL Discovery
Keywords: Correlation, Higher Order, Neural Network, volatility, returns, FTSE, finance, financial, prediction, forecast, estimation
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering
URI: http://discovery.ucl.ac.uk/id/eprint/19924
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item