Search

Complaints of heightened risks in the sub-Saharan African equities markets are rife in the practitioner literature. Investors need an understanding of the volatility dynamics in these frontier markets. This paper uses the Hidden Markov Models to detect the points of regime changes in the volatility in the markets of Ghana, Kenya, Nigeria and Botswana. The daily closing indices of the exchanges and modeled 2- and 3-regimes in the market were used. Information criteria selected the best fitting model of 2-regime changes corresponding to periods of low and high volatilities. This has been shown through smoothed volatility plots depicting times of regime changes over the sample periods. Investors will be guided in the strategies they choose by setting price filters according to the particular regimes. For regulators, the work will help in setting risk sensitive capital based on market regimes so that firms do not carry too much capital than is required. Keywords: Stylized properties, regime changes, sub-Saharan equities, expectation maximization algorithm, price filters.

We investigated regime switching behaviour of the broad aggregate returns of the Ghana Stock Exchange within the context of frontier markets. The data covered the daily period from January 04, 2011 to March 31, 2017. We made the following findings: (a) there are clear market regimes corresponding to periods of tranquillity and turbulence as demonstrated by the vastly different unconditional volatilities associated with the low and high regimes in the data (b) contrary to investor beliefs, frontier markets are less risky than reported in the popular press and (c) the returns from frontier markets are positively skewed. The regime switching model employed was compared to the workhorse GARCH(1,1) model used for volatility estimation in finance using the Deviance Information Criteria. In our findings, the MS-GARCH performed better than the GARCH(1,1), confirming recent studies in volatility modeling. The implications of these findings for trading strategies, investment and portfolio choices and risk management have been highlighted. For policymakers this study will provide a counter argument for granting tax exemptions to global investors on the basis of perceived elevated risk in frontier markets.

We adopt a granular approach to estimating the risk of equity returns in sub-Saharan African frontier equity markets under the assumption that, returns are influenced by developments in the underlying economy. Four countries were studied – Botswana, Ghana, Kenya and Nigeria. We found heterogeneity in the evolution of volatility across these markets and also that two-regime switching volatility models describe better the heteroscedastic returns generating processes in these markets using the deviance information criteria. We backtest the results to assess whether the models are a good fit for the data. We concluded that, the selected models are the most suitable for predicting the volatility of future returns in the markets studied.

The study used the Markov regime switching model to investigate the presence of regimes in the volatility dynamics of the returns of JSE All-Share Index (ALSI). Volatility regimes are as a result of sudden changes in the underlying economy generating the market returns. In all, twelve candidate models were fitted to the data. Estimates from the regime switching model were compared to the industry standard non-switching GARCH (1,1) using the Deviance Information Criteria (DIC). The results show that the two-regime switching EGARCH model with skewed Student t innovations describes better the return of the JSE Index. Additionally, we backtest the model results in order to confirm our findings that the two-regime switching EGARCH is the best of the models for the sample period.

The four-parameter generalised lambda distribution provides the flexibility required to describe the key moments of any distribution as compared with the normal distribution which characterises the distribution with only two moments. As markets have increasingly become nervous, the inadequacies of the normal distribution in capturing correctly the tail events and describing fully the entire distribution of market returns have been laid bare. The focus of this paper is to compare the generalised method of moments (GMM) and maximum likelihood essential estimates (MLE) methods as subsets of the GLD for a better fit of JSE All Share Index returns data. We have demonstrated that the appropriate method of the GLD to completely describe the measures of central tendency and dispersion by additionally capturing the risk dimensions of skewness and kurtosis of the return distribution is the Generalised Method of Moments (GMM) with the Kolmogorov-Smirnoff Distance good-of-fit statistics and the quantile-quantile graph. These measures are very important to any investor in the equity markets.