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The risk-return relationship in the South Africa stock market - African ...

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TT21 2 L( ) L t( ) log( ) log(2) [8]2t1 t12 where <strong>the</strong> first two conditional moments should be correctly specified to ensure that<strong>the</strong> parameters estimation is consistent.3.2 DataAnderson and Bollerslev (1997) argue that <strong>the</strong> use of high frequency data isdesirable to uncover <strong>the</strong> <strong>risk</strong>-<strong>return</strong> <strong>relationship</strong>. This is because it allows for a bettermeasurement of <strong>risk</strong> and enables precise identification of <strong>the</strong> <strong>risk</strong>-<strong>return</strong> trade-off.This is because high frequency data produces better estimates of conditionalvolatility process. <strong>The</strong> <strong>risk</strong>-<strong>return</strong> trade-off is estimated us<strong>in</strong>g daily <strong>return</strong>s on 50<strong>market</strong> and <strong>in</strong>dustry <strong>stock</strong> price <strong>in</strong>dexes of <strong>the</strong> Johannesburg <strong>stock</strong> exchange listedcompanies. <strong>The</strong>se <strong>market</strong> and <strong>in</strong>dustry <strong>stock</strong> price <strong>in</strong>dexes are weighted by <strong>market</strong>capitalisation, where <strong>in</strong>dustry <strong>stock</strong> price <strong>in</strong>dexes assume <strong>the</strong> Industry ClassificationBenchmark (ICB) system.<strong>The</strong> bond exchange yields on R153 (short term government bond) and R186 (longterm government bond) are used to approximate <strong>the</strong> <strong>risk</strong>-free rate of <strong>in</strong>terest. Moststudies use <strong>the</strong> three month treasury bill rate for this purpose. However, <strong>the</strong> <strong>risk</strong> freerate of <strong>return</strong> is only available <strong>in</strong> monthly frequency. All data are sourced from <strong>the</strong><strong>South</strong> <strong>Africa</strong>n Reserve Bank database and spans <strong>the</strong> period January 04, 1995 toFebruary 06, 2009. This yields 2635 data po<strong>in</strong>ts.<strong>The</strong> data for <strong>the</strong> <strong>in</strong>dustry <strong>stock</strong> price <strong>in</strong>dexes of aerospace and defence, personalcare and household products, tobacco, utilities, electricity, gas distribution, gas,water, and multiutilities as well as alternative exchange were not available and assuch, <strong>the</strong>ir <strong>risk</strong>-<strong>return</strong> <strong>relationship</strong>s could not be estimated. <strong>The</strong> <strong>market</strong> and <strong>in</strong>dustrialgroups’ <strong>stock</strong> <strong>in</strong>dexes descriptions are detailed <strong>in</strong> table A1 <strong>in</strong> <strong>the</strong> appendix and <strong>the</strong>irdescriptive statistics are shown on <strong>in</strong> table 1 below. Accord<strong>in</strong>g to <strong>the</strong> descriptivestatistics, consumer goods, food producers, equity <strong>in</strong>vestments as well asdevelopment and venture capital <strong>stock</strong> price <strong>in</strong>dexes show high volatility dur<strong>in</strong>g <strong>the</strong>sample period based on standard deviations.6

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