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Impacts of interval measurement on studies of economic variability: Evidence from stock market variability forecasting

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  • Ling T. He
  • Chenyi Hu

Abstract

Purpose - 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.

Suggested Citation

  • Ling T. He & Chenyi Hu, 2007. "Impacts of interval measurement on studies of economic variability: Evidence from stock market variability forecasting," Journal of Risk Finance, Emerald Group Publishing, vol. 8(5), pages 489-507, November.
  • Handle: RePEc:eme:jrfpps:v:8:y:2007:i:5:p:489-507
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    Cited by:

    1. Ling He & Chenyi Hu, 2009. "Impacts of Interval Computing on Stock Market Variability Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 33(3), pages 263-276, April.
    2. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

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    Keywords

    Stock markets; Economic forecasting;

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