Sensitivity of stock returns to macroeconomic risk in Kenya Chris Musyoki University of Aberdeen 1st Year 14th October 2011 BAFA Conference on Emerging Economies, Sunderland
Introduction • Kenyan economy • Free price determination • No restriction in foreign currency trading • Free investment funds transfer • Agriculture and tourism leading income sources • More imports from China etc than exports to Europe • Local investors the majority in Nairobi Stock Exchange • Microeconomic instability • High inflation rate (Jan 2011=10% & August 2011=16%) • Extreme foreign exchange rate (US/Ksh80 in Jan 2011 & US/Ksh94 August 2011) • High interest rate (91-day Treasury bill Jan 2011=2% & August 2011=8%)
Research hypothesis • How does macroeconomic instability affect stock returns? • Are investors compensated for high risks? • Do positive news and negative news have differential effect? • Do investors incur excessive losses due to market risk? • Which industrial sectors are highly risky?
Data from DataStream • Macroeconomic variables • Consumer Price Index (CPI) • US Dollar exchange rate to Kenya Shilling • 91-days Treasury Bills rate (Non-stationary hence Ignored) • Portfolios returns • Ten different industrial portfolios • Each portfolio consist of two industries • Study period • 30thJune 2008 ~ 31st May 2011
Methodology • TGARCH (1,1)-in-mean Model Rt = α1 + α2DLCPI + α3DLKENUSD + α4DLTBILLS + λδt + µt δ2t = β1 + Σpβ2µ2t-1 + Σqβ3δ2t-1 + ωµ2t-1Фt-1 • Value-at-Risk Model VaRupt= - PRt + Zαδt√(H/P) VaRdownt= PRt - Zαδt√(H/P) • Backtesting(Kupiec, 1995) LR = 2ln [(1-F)N-DFD] – 2ln[(1-α)N-DαD] • Student t-distribution
Summary • Macroeconomic instability effect • Inflation rate negatively affects agricultural portfolio • Foreign exchange rate negatively affect most portfolios • Portfolio performance • Agricultural portfolio sensitive to market risk yet has positive risk-return trade-off • Most risky is investment portfolio while least risky is manufacturing portfolio • High shock persistency especially on commercial, automobile and energy portfolios • Automobile and commercial portfolios have statistically insignificant parameters
Reference • Fan, Y., Zhang, Y., Tsai, H., et al (2008). "Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach", Energy Economics, vol. 30, no. 6, pp. 3156-3171. • Obadović, M.D. & Obadović, M.M. (2009). "An analytical method of estimating value-at-risk on the Belgrade stock exchange", Economic Annals, vol. 54, no. 183, pp. 119-138. • Thupayagale, P. (2010). "Evaluation of GARCH-based models in value-at-risk estimation: Evidence from emerging equity markets", Investment Analysts Journal, vol. 72, pp. 13-29.