Macroeconomic Uncertainty and Conditional Stock Market Volatility in South Africa*

Abstract

This paper analyses how systematic risk emanating from the macroeconomy is transmitted into stock market volatility using augmented autoregressive Generalised Autoregressive Conditional Heteroscedastic (AR‐GARCH) and vector autoregression (VAR) models. Also examined is whether the relationship between the two is bidirectional. By imposing dummies for the 1997‐1998 Asian and the 2007‐2009 sub‐prime financial crises, the study further analyses whether financial crises affect the relationship between macroeconomic uncertainty and stock market volatility. The findings show that macroeconomic uncertainty significantly influences stock market volatility. Although volatilities in inflation, the gold price and the oil price seem to play a role, it is found that volatility in short‐term interest rates and exchange rates are the most important, suggesting that South African domestic financial markets are increasingly becoming interdependent. Finally, the results show that financial crises increase volatility in the stock market and in most macroeconomic variables, and, by so doing, strengthen the effects of changes in macroeconomic variables on the stock market.

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

“…Furthermore, the use of monthly data is in accordance with strong financial literature (e.g. see Bloom, 2009;Braun, Nelson, & Sunier, 1995;Chinzara, 2011;Doukas, Hall, & Lang, 2003;Faff & Brailsford, 1999;Hansson & Hordahl, 1998;Khan, Muneer, & Anuar, 2013;Lanne & Luoto, 2008;Manolis, Stelios, & Angelos, 2002;Sadorsky, 2001 andWest &Worthington, 2006). Then, as is practice in the financial literature, the return series will be expressed in logarithmic difference between the two successive prices acquiring the continuous compounding returns (i.e.…”

“…In this Generalised ARCH model, he sets the current conditional variance as a function of the previous square error term and past conditional variance. It is indeed incredible that this one GARCH (1, 1) model can be sufficiently applied in any financial time series in order to comprehend the volatility dynamics (for example, see Chinzara, 2011;Engle, 2004 andElyasiani et al, 2011). Following the strong financial literature (see, for example, Chinzara, 2011;Engle, 2004;Elyasiani et al, 2011 andGoudarzi &Ramanarayanan, 2010), this research study also applied GARCH (1, 1) to estimate various volatility dynamics.…”

“…For stationarity to hold, it is necessary that the sum of ARCH (α i ) and GARCH (β i ) terms must be less than one (Chinzara, 2011;Elyasiani et al, 2011). If their sum is equal to one, the condition is said to be integrated in variance, whereas the current volatility shocks are to be considered in forecasting the future volatility for all future periods (Engle & Bollerslev, 1986;Karmakar, 2007).…”

“…Hence, GARCH (1, 1) stands as most appropriate order for this purpose. The following is the general univariate equation regarding this model (Chinzara, 2011):…”

Socio-economy and stock market volatility

“…They have recapitulated that macroeconomic volatility significantly causes stock market volatility. However, Oseni and Nwosa (2011) have not found any evidence on the causal relationship between stock market volatility and the volatility in interest rate and inflation rate but Chinzara (2011) have found that macroeconomic uncertainty significantly affects stock market volatility (See also: Okoli, 2012). Kadir et al (2011) have examined the impact of interest rate volatility and exchange rate volatility on stock return volatility.…”