Abstract
In this paper, we provide evidence that the five variables used in the study were nonlinear in nature, while finding a better Markov-switching model. The study used dailydata obtained from the Johannesburg Stock Exchange over the period from January 2010 to December 2012. An extension of Markov Switching with autoregressive model was used for empirical analysis. Prior to using this model, the series were tested for nonlinear unit root with modified Kapetanois-Shin-Snell nonlinear Augmented Dickey-Fuller (KSS-NADF) test which successfully provided positive results.Other preliminary tests selected the first lag as optimal and confirmed that stock prices may switch between two regimes. Further empirical findings proved that stock prices can be successfully modelled with Markov Switching Autoregressive model of order one. First National bank was found to have 99.64% longer stock price stability if adjustments regards tofinancialpolicies are made. Capitec Bank was the least favoured among the banks.
Highlights
In recent years, stock markets data analyses have become very important object of academic research
The conventional methods often employed by data analysts to capture the dynamics and patterns in the most financial time series based on the assumption of linearity, leading to failure by majority of models failing to address the fundamentals of most financial data
Prior to application of the moving averages (MA)-AR model, the data used was prepared by checking nonlinearity in unit root using the KSS-NLADF test
Summary
Stock markets data analyses have become very important object of academic research. Literature highlights evidence of occasional sudden breaks in many financial and economic time series, especially stock market data, making it extremely difficult for accurate prediction. These changes have been attributed to several reasons including changes in economic conditions, investor expectations, relative performance of other stock markets, responses to shocks from exogenous geopolitical events or financial crises, or disruptions due to weather related catastrophes, just to mention a few. This study contributes to literature in this area The application of this method may stimulate interest to other scholars who wish to analyse other financial data exhibiting nonlinearities.
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