n Management Dynamics : Journal of the Southern African Institute for Management Scientists - A probabilistic approach to categorise equity index volatility

Volume 18, Issue 2
  • ISSN : 1019-567X



Forecasting volatility in stock indices and currency returns have been major areas of research in financial economics. In general, volatility of stock indices may relate to the swings in mean or standard deviation of the returns of the last trading days. Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. The main thrust of this study is to suggest an alternative approach for modelling and related analysis of swings in weekly / monthly means of asset returns. In the new approach suggested here, volatility in weekly / monthly means of stock returns is conceived as a function of mean asset returns and its standard error, and is further classified into various states according to predetermined perceptions of the market player. Fairly accurate knowledge of such states of volatility in weekly / monthly mean asset returns will be of considerable help to a prospective or an existing market player. A probability model defined on different states of volatility with respect to means of equity prices / indices is proposed. Based on the probability model, two different measures of volatility are also considered. Two asymptotically distribution-free tests, namely the chi-square and the likelihood ratio test of goodness-of-fit for the hypothesis of the absence of volatility, are proposed. The approach suggested here will be of interest to researchers, stock market investors and analysts. As an application of the proposed model, we analyse Botswana stock market data from 1999-2005. The probability models are generated for weekly and monthly equity indices.

Loading full text...

Full text loading...


Article metrics loading...


This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error