Predicting Regime Shifts in Johannesburg Stock Exchange All-Share Index (JSE-ALSI): A Markov-Switching Approach

Katleho Daniel Makatjane () and Edward Kagiso Molefe ()
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Katleho Daniel Makatjane: Basetsana Consultants, Republic of South Africa
Edward Kagiso Molefe: University of the Free State, Republic of South Africa

Eurasian Journal of Economics and Finance, 2020, vol. 8, issue 2, 95-103

Abstract: It has been proven several times that linear models are unable to encapsulate nonlinear dynamics of macroeconomic and financial data such as inflation rates, exchange rates and stock prices to mention fewer. As a result, to overcome this problem, this current study adopted the nonlinear models due to the fact that they have required qualities to apprehend nonlinearity in a dataset. In order to predict a regime shifts, a five-day Johannesburg stock exchange allshare index (JSE-ALSI) spanning period from 02 January 2003 to 28 June 2019 was used as an experimental unit. This current study firstly employed Teräsvirta neural network test to detect the presence of nonlinearity and proceeded to estimate a two regime Markov-Switching autoregressive (MS-AR). The results of Teräsvirta neural network test revealed a highly significant nonlinearity with permanent seasonality as demonstrated by Kruskal-Wallis test. The predicted regime shifts by a latent dynamic allowed the autoregressive and variance parameters to promptly react to vital systemic shocks. As a result, this current study allowed volatility to oscillate between high and low volatility regimes that produced an expected duration of high volatility of two year and two months. This was a clear indication that there is a regime shifts in JSE-ALI which are modeled using Markov-Chain (MC) stochastic process. These findings may be used to inform robust policy making with the aim of safeguarding both the JSE and other global stock markets from the episodes of stock market crash. Moreover, other researchers can utilize the results of this study to calculate the risk associated with structural breaks and high volatility periods.

Keywords: All Share Index; Johannesburg Stock Exchange; Markov-Switching; Nonlinear Modeling (search for similar items in EconPapers)
Date: 2020
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