Volatility Forecasting using Time Series Data Mining and Evolutionary Computation Techniques

被引：0

作者：

Ma, Irwin ^{[1
]}

Wong, Tony ^{[1
]}

Sankar, Thiagas ^{[1
]}

机构：

[1] Ecole Technol Super, Montreal, PQ H3C 1K3, Canada

来源：

GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 | 2007年

关键词：

Financial Volatility; forecasting; genetic algorithm; genetic programming; data mining; S&P 100;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2262 / 2262

页数：1

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