The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.
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.
Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data.
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.
