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

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.