Abstract:
Proportional runs volatility model is an innovation from the requirements volatility model that is employed in the Computer Science discipline and has the potential of measuring historical stock return volatility. This research employed balanced panel data consisting of monthly closing stock price data for a sample of 21 stocks listed in the Nairobi Securities Exchange that were selected using purposive sampling method from a population of 56 stocks during years 2001 and 2010. Historical stock return volatility was then measured using the proportional run volatility and standard deviation metrics for comparison purpose. The results were presented graphically and using inferential statistics including: Pearson’s correlation and t-test tabulations. The findings indicated that graphically there seems to be some similar behavior in the relationship between the curves of proportional runs and standard deviation models. As per the Pearson’s correlation analysis, there appeared a moderately strong relationship. However as pet the one sample t-test analysis, there was a significant difference between the proportional run volatility and standard deviation metrics. The conclusion was therefore that there was mixed findings as to whether the proportional runs volatility metric has the potential of measuring historical stock return volatility in a manner similar to that of the standard deviation metric.
