Academic journal article Journal of Global Business and Technology

Editorial

Academic journal article Journal of Global Business and Technology

Editorial

Article excerpt

Against the backdrop of the 2008 financial crisis, the first paper by Pat Obi, Shomir Sil, and Jeong-Gil Choi examines the risk impact of the crisis on the South African stock market. The South African equity market is selected because unlike the developed capital markets in the West, its financial services industry was more closely regulated. The exposure of the country's banking industry to the global contagion was therefore limited. This benefit was in large part due to the South African National Credit Act of 2007. This legislation strongly discouraged banks from engaging in the types of speculative and subprime credit transactions that led to widespread bank failures in the United States and Europe between 2007 and 2010. Notwithstanding, the wave of uncertainty that resulted from the economic downturn was evident in the pricing of both real and financial assets across South Africa in particular in the first quarter of 2009. This uncertainty reflected the depth of financial risk that defined the global contagion in the banking industry at the time.

In this study, the impact of market risk on the valuation of South African equity securities is examined. Market risk is measured with value-at-risk (VaR). This risk management metric, which is advocated by the Basel Committee on Bank Supervision, calculates the maximum investment loss expected in a defined trading period at a given confidence level. Today, VaR is employed by virtually all internationally active financial institutions around the world. The volatility measure used to calculate VaR is obtained by the method of generalized autoregressive conditional heteroscedasticity (GARCH). The GARCH approach is used in order to incorporate the widely documented time-varying volatility associated with financial time series such as stock returns. The authors show in particular that using traditional VaR calculation methods underestimates the intensity of portfolio losses, especially in periods of high volatility. As this study reveals, the period of the 2008 financial crisis was a time when such volatility persistence was prevalent. Thus, to improve the volatility forecasts and therefore make the VaR estimates more realistic, the GARCH specification is used. Calculations of VaR are then performed over several times in an out-of-sample period so as to ascertain the forecast accuracy of the GARCH volatility estimates.

One of the initial studies that applied the GARCH process to estimate value-at-risk was Engle (2001). He used GARCH (1,1) to obtain volatility forecasts with which he then estimated out-of-sample VaR for the S&P 500 around the period of the 2001 U.S. recession (Engle, R.F., "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, 2001, 15(4): 157-168). With respect to the South African market, Cheng et al (2009) investigated the out-of-sample value-at-risk forecasts for gold prices by considering oil price volatilities (Cheng, W., Su, J., and Tzou, Y., "Value-at-Risk Forecasts in Gold Market Under Oil Shocks," Middle Eastern Finance and Economics, 2009, Issue 4, 48-64).

The equity market data for this study were obtained from January 1995 to March 2009. Risk assessment was based on a portfolio of 1,000,000 South African rands (about US$100,000 at the exchange rate of 10 rands to US$1). The observation period for the empirical analysis is split into two subperiods: in-sample and out-of-sample. The in-sample period begins January 1995 and ends January 2006. The out-of-sample period is from February 2006 to March 2009. The out-of-sample period encompasses the time of the global financial crisis. This latter period is further subdivided into the pre-crisis period of February 2006 to October 2007 and the crisis time of November 2007 until March 2009.

The empirical results of Obi, Sil, and Choi's study show that the GARCH volatility approach for estimating a portfolio's value-at-risk is better at reflecting the true impact of market risk under various volatility conditions. …

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