Forecasting the volatility of stock price index

Authors:
Tae Hyup Roh
Affiliations:
Department of Business, Management Information System, Seoul Women’s University, Seoul, Korea
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 3
Cited 0

Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation

Decision Support Systems
Forecasting S&P 500 stock index futures with a hybrid AI system

Decision Support Systems
A hybrid genetic-neural architecture for stock indexes forecasting

Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurate volatility forecasting is the core task in the risk management in which various portfolios’ pricing, hedging, and option strategies are exercised. Prior studies on stock market have primarily focused on estimation of stock price index by using financial time series models and data mining techniques. This paper proposes hybrid models with neural network and time series models for forecasting the volatility of stock price index in two view points: deviation and direction. It demonstrates the utility of the hybrid model for volatility forecasting.