In this paper, both Seasonal ARIMA and Holt-Winters models are developed to predict the monthly car sales in South Africa using data for the period of January 1994 to December 2013. The purpose of this study is to choose an optimal model suited for the sector. The three error metrics; mean absolute error, mean absolute percentage error and root mean square error were used in making such a choice. Upon realizing that the three forecast errors could not provide concrete basis to make conclusion, the power test was calculated for each model proving Holt-Winters to having about 0.3% more predictive power. Empirical results also indicate that Holt-Winters model produced more precise short-term seasonal forecasts. The findings also revealed a structural break in April 2009, implying that the car industry was significantly affected by the 2008 and 2009 US financial crisis.
The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.
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.
With the adoption of the inflation targeting by the South African Reserve Bank (SARB) in 2000, the average inflation radically went down. Earlier 2000, the inflation rate was recorded at 8.8% that is January 1999; then a year later went down to 2.65%. What’s more, this paper builds up an early warning system (EWS) model for predicting the event of high inflation in South Africa. Periods of high and low inflation were distinguished by utilizing Markov-switching model. Utilizing the results of regime classification, logistic regression models were then assessed with the goal of measuring the likelihood of the event of high inflation periods. Empirical results demonstrate that the proposed EWS model has some potential as a corresponding instrument in the SARB’s monetary policy formulation based on the in-sample and out-of-sample forecasting performance.
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