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

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.

Citation

He, L.T. and Hu, C. (2007), "Impacts of interval measurement on studies of economic variability: Evidence from stock market variability forecasting", Journal of Risk Finance, Vol. 8 No. 5, pp. 489-507. https://doi.org/10.1108/15265940710834771