Impacts of Interval Computing on Stock Market Variability Forecasting

Authors:
Ling T. He;Chenyi Hu
Affiliations:
Department of Economics and Finance, University of Central Arkansas, Conway, USA 72035;Department of Computer Science, University of Central Arkansas, Conway, USA 72035
Venue:
Computational Economics
Year:
2009

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Abstract

This study uses the interval computing approach to forecast the annual and quarterly variability of the stock market. We find that the forecasting accuracy is significantly higher than the OLS lower and upper bound forecasting. The strength of the interval computing comes from its data processing. It uses lower and upper bound information simultaneously, no variability information is lost in parameter estimation. The quarterly interval (variability) forecasts suggest that the interval computing method outperforms the OLS lower and upper bound forecasting in both stable and volatile periods.