1. Sensitivity of stock returns to macroeconomic risk in Kenya Chris Musyoki University of Aberdeen 1 st Year 14 th October 2011 1 BAFA Conference on Emerging Economies, Sunderland

2. 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%) 2

3. 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? 3

4. 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 ▫30 th June 2008 ~ 31 st May 2011 4

5. 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 VaR up t = - PR t + Z α δ t √(H/P) VaR down t = PR t - Z α δ t √(H/P) Backtesting (Kupiec, 1995) LR = 2ln [(1-F) N-D F D ] – 2ln[(1-α) N-D α D ] Student t-distribution 5

6. Portfolio construction PORTFOLIOCOMPANY1COMPANY2 AGRICULTURALREA VIPINGO PLANTATIONSSASINI AUTOMOBILESCMC HOLDINGSSAMEER AFRICA BANKINGDIAMOND TRUST (KENYA)STANDARD CHARTERED BANK COMMERCIALKENYA AIRWAYSNATION MEDIA GROUP CONSTRUCTIONATHI RIVER MININGEAST AFRICAN CABLES ENERGYKENOLKOBILKENYA POWER & LIGHTING INSURANCEPAN AFRICAN INSURANCESJUBILEE HOLDINGS INVESTMENTCENTUM INVESTMENTOLYMPIA CAP.KENYA MANUFACTURINGBAT KENYAEAST AFRICAN BREWERIES TELECOMMUNICATIONSAFARICOMACCESS KENYA GROUP 6

7. Returns statistics DETAILS Mean Std. Dev. Skewness Kurtosis Jarque- Bera Probability ADF unit root AGRICUL-0.00630.12021.08225.761317.95110.0001-5.1519*** AUTOMOB-0.01500.1060-0.30903.87931.68440.4308-6.1061*** BANKING0.00740.0604-1.35855.747721.77540.0000-4.3819*** COMM0.00180.0919-1.05324.718310.77570.0046-5.8125*** CONSTR0.01110.0846-0.38443.96392.21680.3301-4.8738*** ENERGY-0.00190.0951-0.56603.42932.13780.3434-5.1191*** INSUR0.00570.1132-1.00515.195612.92300.0016-6.2196*** INVEST-0.01100.1174-0.19102.95020.21650.8974-5.2132*** MANUF0.00640.0582-0.50655.39649.87130.0072-4.6227*** TELECOM-0.03650.1244-0.73613.47423.48860.1748-5.5819*** CPI0.00690.00721.43205.468020.84530.0000-3.4228** KENUSD0.00780.02131.43145.903424.24510.0000-3.9378*** 7

8. TGARCH(1,1)-M results (1) R t = α 1 + α 2 DLCPI + α 3 DLKENUSD + λδ t + µ t δ 2 t = β 1 + Σβ 2 µ 2 t-1 + Σβ 3 δ 2 t-1 + ωµ 2 t-1 Ф t-1 Portfolioα1α2α3λβ1β2β3ωR-squared AGRICUL-0.0395*-3.652*-0.8810.612*0.0041.378-0.270-0.1040.152 0.0450.0010.0750.0220.3080.0770.1290.919 AUTOMOB0.4912.317-0.549-4.7450.001-0.0330.984*-0.1360.297 0.6510.5890.6920.6790.6280.7630.0000.690 BANKING0.044-2.419-1.220*-2.2660.002-0.0350.525-0.0700.277 0.2160.4790.0470.7340.8460.9720.8390.957 COMM0.346-3.456-5.481-0.8410.0070.323*1.045*-1.1180.269 0.2260.8580.4810.5510.8700.000 0.838 CONSTR0.0160.526-2.300*0.004-0.3110.4340.3270.153 0.0840.7940.000 0.1890.0720.5490.332 8

9. TGARCH(1,1)-M results (2) R t = α 1 + α 2 DLCPI + α 3 DLKENUSD + λδ t + µ t δ 2 t = β 1 + Σβ 2 µ 2 t-1 + Σβ 3 δ 2 t-1 + ωµ 2 t-1 Ф t-1 Portfolioα1α1 α2α2 α3α3 λβ1β2β3ωR-squared ENERGY0.116-0.954-1.908*-1.3650.003-0.1310.751*-0.0750.191 0.2920.7500.0200.3380.2640.4690.0280.700 INSUR-0.017-0.251-2.627*0.3790.007-0.0320.494-0.1170.236 0.8810.9350.0070.7000.8110.9580.8320.892 INVEST0.378-1.971-3.214*-3.8740.0050.1380.1910.2740.340 0.5380.3300.0030.5800.3880.5640.7030.734 MANUF0.027-1.942-1.303*1.0930.001-0.2020.4360.1690.307 0.3970.0720.0000.9450.5750.7000.7050.729 TELECOM0.006-1.524-2.812*-0.1720.006-0.2010.4090.3440.281 0.9650.5330.0000.9020.6130.2800.7270.436 9

10. Value-at-Risk (VaR) results (1) One month holding period with VaR at 95% confidence level VaRt(up) = - PRt + Zαδt√(H/P) and VaRt(down) = PRt - Zαδt√(H/P) LR = 2ln[(1-F)N-DFD] – 2ln[(1-α)N-DαD] LR critical value=3.84 PortfolioVaR Mean Max Min Std. Dev.Failure Failure rateLR statistic AGRICULupside0.0430.315-0.3480.11640.1142.27* downside-0.0430.348-0.3150.116300.857151.55 AUTOMOBupside0.0780.361-0.1880.11510.0290.40* downside-0.0780.188-0.3610.115340.971194.73 BANKINGupside0.0160.215-0.0940.06170.2009.78 downside-0.0160.094-0.2150.061320.914171.56 COMMupside0.3100.846-0.0370.20900.0003.59* downside-0.3100.037-0.8460.209340.971194.73 CONSTRupside0.0230.268-0.1560.08650.1434.33 downside-0.0230.156-0.2680.086320.914171.56 10

11. Value-at-Risk (VaR) results (2) One month holding period with VaR at 95% confidence level VaRt(up) = - PRt + Zαδt√(H/P) and VaRt(down) = PRt - Zαδt√(H/P) LR = 2ln[(1-F)N-DFD] – 2ln[(1-α)N-DαD] LR critical value=3.84 PortfolioVaR Mean Max Min Std. Dev.Failure Failure rateLR statistic ENERGYupside0.0330.295-0.1490.09880.22913.07 downside-0.0330.149-0.2950.098310.886161.27 INSURupside0.0350.403-0.1750.11330.0860.78* downside-0.0350.175-0.4030.113310.886161.27 INVESTupside51.235600.738-0.096127.68400.0003.59* downside-51.2350.096-600.738127.684340.971194.73 MANUFupside0.0100.189-0.1480.05890.25716.69 downside-0.0100.148-0.1890.058300.857151.55 TELECOMupside0.0710.416-0.1220.12420.0570.04* downside-0.0710.122-0.4160.124280.800133.45 11

12. 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 12

13. 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. 13