Volatility forecasting using time series data mining and evolutionary computation techniques

ABSTRACT

Traditional parametric methods have limited success in estimating and forecasting the volatility of financial securities. Recent advance in evolutionary computation has provided additional tools to conduct data mining effectively. The current work applies the genetic programming in a Time Series Data Mining framework to characterize the S&P100 high frequency data in order to forecast the one step ahead integrated volatility. Results of the experiment have shown to be superior to those derived by the traditional methods.

REFERENCES

• 1: Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P., The distribution of realized exchange rate volatility. Journal of the American Statistical Association, no. 96, 42--55, 2001
• 2: Diggs, D. H., Povinelli, R. J., A Temporal Pattern Approach for Predicting Weekly Financial Time Series. Artificial Neural Networks in Engineering, 707--712, 2003

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

Additional Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES
      Subjects: Economics

General Terms:
Algorithms, Economics, Performance

Keywords:
S&P 100, data mining, financial volatility, forecasting, genetic algorithm, genetic programming