Yield curve and business cycle dynamics in South Africa: new evidence from a Markov switching model - Rotich, Mercyline Chepkemoi (Rhodes University, Faculty of Commerce, Department of Economics and Economic History)
Globally, several empirical studies have demonstrated the ability of the yield spread to predict a recession in a country. In South Africa, previous studies have not only shown the yield curve's predictive power but have further demonstrated that it outperforms other commonly used variables, such as the growth rate of real money supply, changes in stock prices, and the index of leading economic indicators. However, some recent studies have shown that the yield spread (the spread between 10-year bonds and 3-month Treasury bills) gave false signals of recession. In this study, we explore the possible reasons for the false signals of the yield spread by addressing the following questions. Does the yield spread used matter? Does the measure of the business cycle used matter? And do the estimation techniques used matter? To address the first question, unlike the previous studies, this paper uses four different yield spreads- depicting short-term, medium-term, and long-term government bonds against the backdrop of a changing structure of bond holding, which reflects the increasing risk eversion of investors in South Africa. Second, the paper used different measures of business cycles, namely industrial production index, lagging, coincident, and leading economic indicators. The empirical models were estimated using both univariate and multivariate Markov switching models. As economic theory suggests, the univariate Markov switching model was used to determine if each variable exhibits a significant regime switching. The multivariate Markov switching model was estimated for each business cycle and yield spread variable, with each of the other variables serving as a non-switching explanatory variable, thereby addressing potential endogeneity concerns and the predictive power of the explanatory variable. Finally, the multivariate Markov switching model was estimated for three monthly sample periods, a full sample for 1986 to 2022, and two sub-samples "“ 1986 to 2009 and 2010 to 2022. This analysis consistently reveals significant regime-switching behavior across all the series thus, affirming the superiority of the regime switching model over the standard model used in previous studies. By analyzing the transition probabilities and the expected durations between these regimes, we find that including the spreads in the business cycle model improves the models' predictability, with the medium-term bonds spread performing better than the usual long-term spread. The smoothed regime probability of the best-performing models is compared with the SARB recession dates; the two closely resemble each other, proving that the Markov switching model can help predict the turning points in the business cycle in South Africa.
Keywords: Yield curve; Business cycles -- South Africa; Markov processes; Recessions -- South Africa; Multivariate analysis