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Sensitivity of stock returns to macroeconomic risk in Kenya

Chris MusyokiUniversity of Aberdeen1st Year

14th October 2011

BAFA Conference on Emerging Economies, Sunderland

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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%)

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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?

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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▫30th June 2008 ~ 31st May 2011

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Methodology•TGARCH (1,1)-in-mean Model

Rt = α1 + α2DLCPI + α3DLKENUSD + α4DLTBILLS + λδt + µtδ2t = β1 + Σpβ2µ2t-1 + Σqβ3δ2t-1 + ωµ2t-1Фt-1

•Value-at-Risk ModelVaRup

t = - 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

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Portfolio constructionPORTFOLIO COMPANY1 COMPANY2

AGRICULTURAL REA VIPINGO PLANTATIONS SASINI

AUTOMOBILES CMC HOLDINGS SAMEER AFRICA

BANKING DIAMOND TRUST (KENYA) STANDARD CHARTERED BANK

COMMERCIAL KENYA AIRWAYS NATION MEDIA GROUP

CONSTRUCTION ATHI RIVER MINING EAST AFRICAN CABLES

ENERGY KENOLKOBIL KENYA POWER & LIGHTING

INSURANCE PAN AFRICAN INSURANCES JUBILEE HOLDINGS

INVESTMENT CENTUM INVESTMENT OLYMPIA CAP.KENYA

MANUFACTURING BAT KENYA EAST AFRICAN BREWERIES

TELECOMMUNICATION SAFARICOM ACCESS KENYA GROUP

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Returns statistics

DETAILS Mean Std. Dev. Skewness Kurtosis

Jarque-

Bera

Probabilit

y

ADF unit

root

AGRICUL -0.0063 0.1202 1.0822 5.7613 17.9511 0.0001 -5.1519***

AUTOMOB -0.0150 0.1060 -0.3090 3.8793 1.6844 0.4308 -6.1061***

BANKING 0.0074 0.0604 -1.3585 5.7477 21.7754 0.0000 -4.3819***

COMM 0.0018 0.0919 -1.0532 4.7183 10.7757 0.0046 -5.8125***

CONSTR 0.0111 0.0846 -0.3844 3.9639 2.2168 0.3301 -4.8738***

ENERGY -0.0019 0.0951 -0.5660 3.4293 2.1378 0.3434 -5.1191***

INSUR 0.0057 0.1132 -1.0051 5.1956 12.9230 0.0016 -6.2196***

INVEST -0.0110 0.1174 -0.1910 2.9502 0.2165 0.8974 -5.2132***

MANUF 0.0064 0.0582 -0.5065 5.3964 9.8713 0.0072 -4.6227***

TELECOM -0.0365 0.1244 -0.7361 3.4742 3.4886 0.1748 -5.5819***

CPI 0.0069 0.0072 1.4320 5.4680 20.8453 0.0000 -3.4228**

KENUSD 0.0078 0.0213 1.4314 5.9034 24.2451 0.0000 -3.9378***

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TGARCH(1,1)-M results (1)Rt = α1 + α2DLCPI + α3DLKENUSD + λδt + µt

δ2t = β1 + Σβ2µ2

t-1 + Σβ3δ2t-1 + ωµ2

t-1Фt-1

Portfolio α1 α2 α3 λ β1 β2 β3 ωR-squared

AGRICUL -0.0395* -3.652* -0.881 0.612* 0.004 1.378 -0.270 -0.104 0.152

  0.045 0.001 0.075 0.022 0.308 0.077 0.129 0.919  AUTOMOB 0.491 2.317 -0.549 -4.745 0.001 -0.033 0.984* -0.136 0.297

  0.651 0.589 0.692 0.679 0.628 0.763 0.000 0.690  

BANKING 0.044 -2.419 -1.220* -2.266 0.002 -0.035 0.525 -0.070 0.277

  0.216 0.479 0.047 0.734 0.846 0.972 0.839 0.957  

COMM 0.346 -3.456 -5.481 -0.841 0.007 0.323* 1.045* -1.118 0.269

  0.226 0.858 0.481 0.551 0.870 0.000 0.000 0.838  

CONSTR 0.016 0.526 -2.300* 0.004 -0.311 0.434 0.327 0.153

  0.084 0.794 0.000   0.189 0.072 0.549 0.332  

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TGARCH(1,1)-M results (2)Rt = α1 + α2DLCPI + α3DLKENUSD + λδt + µt

δ2t = β1 + Σβ2µ2

t-1 + Σβ3δ2t-1 + µω 2

t-1Фt-1

Portfolio α1 α2 α3 λ 1β 2β 3β ω R-squared

ENERGY 0.116 -0.954 -1.908* -1.365 0.003 -0.131 0.751* -0.075 0.191

  0.292 0.750 0.020 0.338 0.264 0.469 0.028 0.700  

INSUR -0.017 -0.251 -2.627* 0.379 0.007 -0.032 0.494 -0.117 0.236

  0.881 0.935 0.007 0.700 0.811 0.958 0.832 0.892  

INVEST 0.378 -1.971 -3.214* -3.874 0.005 0.138 0.191 0.274 0.340

  0.538 0.330 0.003 0.580 0.388 0.564 0.703 0.734  

MANUF 0.027 -1.942 -1.303* 1.093 0.001 -0.202 0.436 0.169 0.307

  0.397 0.072 0.000 0.945 0.575 0.700 0.705 0.729  

TELECOM 0.006 -1.524 -2.812* -0.172 0.006 -0.201 0.409 0.344 0.281

  0.965 0.533 0.000 0.902 0.613 0.280 0.727 0.436  

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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

Portfolio VaR Mean Max Min Std. Dev. FailureFailure

rateLR

statistic

AGRICUL upside 0.043 0.315 -0.348 0.116 4 0.114 2.27*

  downside -0.043 0.348 -0.315 0.116 30 0.857 151.55

AUTOMOB upside 0.078 0.361 -0.188 0.115 1 0.029 0.40*

  downside -0.078 0.188 -0.361 0.115 34 0.971 194.73

BANKING upside 0.016 0.215 -0.094 0.061 7 0.200 9.78

  downside -0.016 0.094 -0.215 0.061 32 0.914 171.56

COMM upside 0.310 0.846 -0.037 0.209 0 0.000 3.59*

  downside -0.310 0.037 -0.846 0.209 34 0.971 194.73

CONSTR upside 0.023 0.268 -0.156 0.086 5 0.143 4.33

  downside -0.023 0.156 -0.268 0.086 32 0.914 171.56

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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

Portfolio VaR Mean Max Min Std. Dev. FailureFailure

rateLR

statistic

ENERGY upside 0.033 0.295 -0.149 0.098 8 0.229 13.07

  downside -0.033 0.149 -0.295 0.098 31 0.886 161.27

INSUR upside 0.035 0.403 -0.175 0.113 3 0.086 0.78*

  downside -0.035 0.175 -0.403 0.113 31 0.886 161.27

INVEST upside 51.235 600.738 -0.096 127.684 0 0.000 3.59*

  downside -51.235 0.096 -600.738 127.684 34 0.971 194.73

MANUF upside 0.010 0.189 -0.148 0.058 9 0.257 16.69

  downside -0.010 0.148 -0.189 0.058 30 0.857 151.55

TELECOM upside 0.071 0.416 -0.122 0.124 2 0.057 0.04*

  downside -0.071 0.122 -0.416 0.124 28 0.800 133.45

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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

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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.