Impacts of interval measurement on studies of economic variability: Evidence from stock market variability forecasting
AbstractPurpose – The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies. Design/methodology/approach – The study uses interval measured data to forecast the variability of future stock market changes. The variability (interval) forecasts are then compared with point data-based confidence interval forecasts. Findings – Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data. Originality/value – An interval forecast for stock prices essentially consists of predicted levels and a predicted variability which can reduce perceived uncertainty or risk embedded in future investments, and therefore, may influence required returns and capital asset prices.
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Bibliographic InfoArticle provided by Emerald Group Publishing in its journal Journal of Risk Finance.
Volume (Year): 8 (2007)
Issue (Month): 5 (November)
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Web page: http://www.emeraldinsight.com
Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
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- Ling He & Chenyi Hu, 2009. "Impacts of Interval Computing on Stock Market Variability Forecasting," Computational Economics, Society for Computational Economics, vol. 33(3), pages 263-276, April.
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