Analysing volatility in equity indices - A Markov approach for Botswana domestic company indices

 

 

K.S. Madhava Rao; K.K. Moseki

Department of Statistics, University of Botswana, Gaborone. raom@mopipi.ub.bw, mosekikk@mopipi.ub.bw

 

 

---

ABSTRACT

In financial economics, forecasting volatility in stock indices and currency returns has received considerable attention in the last two decades. Many traditional econometric methods forecast asset returns by a point prediction of volatility. The central contribution of this paper is to suggest an alternative approach for modelling and related analysis of asset returns. In this approach, the volatility in stock returns is defined in terms of categories depending on the mean of stock returns and its standard error. This classification naturally allows the study of volatility in terms of a Markov model. The approach suggested here will be of interest to academics, stock market investors, and analysts.

---

OPSOMMING

Op die terrein van die finansiële ekonomie het die vooruitskatting van volatiliteit in die aandeelindekse en wisselkoerse baie aandag getrek oor die afgelope twee dekades. Verskeie tradisionele ekonometriese vooruitskattingsmodelle baseer die vooruitskatting van opbrengste op 'n puntvooruitskatting van die wisselvalligheid. Die bydrae van hierdie artikel is om 'n alternatiewe metode voor te stel vir die modellering. Volgens die model word die volatiliteit van opbrengste gekategoriseer op grond van die gemiddelde opbrengste en die standaardfout. Dit skep geleetheid vir die toepassing van 'n Markov-model. Die model sal akademici, beleggers en analiste interesseer.

---

 

 

“Full text available only in PDF format”

 

 

REFERENCES

[1] Bhat, N.U. 1972. Elements of applied stochastic processes. John Wiley & Sons, Inc., New York.         [ Links ]

[2] Bollerslev, T.A. 1987. A conditional heteroscedastic time series model for speculative prices and rates of return. Review of Economics and Statistics, Vol. 69, pp. 542-547.         [ Links ]

[3] Casella, G. & Berger, R.L. 2002. Statistical inference. Duxbury, United Kingdom.         [ Links ]

[4] Engel, R.F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, Vol. 50, pp. 987-1007.         [ Links ]

[5] Grant, E.L. & Leavenworth, R.S. 1980. Statistical quality control. McGraw-Hill, New York.         [ Links ]

[6] Isaacson, D.L. & Madsen, R.W. 1976. Markov chains theory and applications. John Wiley & Sons, London.         [ Links ]

 

 

* Corresponding author