The Validity of the CAPM in the Russian and Kazakhstan Capital Market with Conditional

Meansemivarianceand Higher-order Moments Specifications

Tamara Teplova,Professor, Head of Master Program “Financial Markets”,

State University - Higher School of Economics, Evgeniya Shutova,

State University - Higher School of Economics, Russia.

EUROPEAN FINANCIAL MANAGEMENTSYMPOSIUM

Beijing ,2011

The Laboratory of Financial Markets Analysis http://fmlab.hse.ru

CAPM Modifications for Emerging Markets. Analysis of Alternative

CAPM Beta Specifications

Exchange Rate Risk and Stock Price Dynamics. Evidence from Top

Russian Companies

Measuring Liquidity in Stock and Bond Market. Is Liquidity

Risk Offset by Excess Return?

CDS Spreads as Indicators for the Government and Corporate Bond

Market Performance 

The popularity of the The popularity of the CAPMCAPM in a in a period of crisis has not decreasedperiod of crisis has not decreased• The studies based on the surveys of over 11 thousand US The studies based on the surveys of over 11 thousand US

chief financial directors conducted by the Duke University and chief financial directors conducted by the Duke University and the CFO Magazine have shown that nearly 75% of the CFO Magazine have shown that nearly 75% of respondents used the CAPM framework to take decisions in respondents used the CAPM framework to take decisions in 2008 and 20092008 and 2009 . .

• Source: Graham, John; Campbell Harvey, Equity risk Source: Graham, John; Campbell Harvey, Equity risk premium amid a global financial crisis, Evidance from the premium amid a global financial crisis, Evidance from the Global CFO Outlook survey 2009. SSRN WP; Graham, J. R., Global CFO Outlook survey 2009. SSRN WP; Graham, J. R., C. R. Harvey, 2009, The CFO Global Business Outlook: 1996-C. R. Harvey, 2009, The CFO Global Business Outlook: 1996-2009. http://www.cfosurvey.org.2009. http://www.cfosurvey.org.

Whether the CAPM beta completely measures systematic risk??Critical assumptions of CAPM:Critical assumptions of CAPM: quadratic investor’s utility function quadratic investor’s utility function the the assumptionassumption of the normal distribution of returns of the normal distribution of returns

the mean and variance suffice to describe the distribution completely

Evidence from emerging markets:Evidence from emerging markets: no simultaneous symmetrical and normal distribution of no simultaneous symmetrical and normal distribution of

the expected returnthe expected return (high kurtosis,(high kurtosis, asymmetry) asymmetry) law liquidity of most equities serious problems such law liquidity of most equities serious problems such

as understating the beta which is calculated using the as understating the beta which is calculated using the regression methodregression method

Purpose of investigation

development of alternative CAPM specifications

The replacement the original beta by downside systematic risk measures.

Incorporating higher-order moments.

Using conditional CAPM instead of unconditional construction.

Methodology

Two-step procedure Fama and MacBeth (1973)

1.The estimation of risk factors of each individual stocks (time-series regressions).

2.The estimation of the cross-sectional relationship between the mean return of assets and estimated risk factors.

Downside measures of risk Beta of Harlow and Rao (1989) with benchmark

equal to mean equal to zero

Beta of Estrada (2002) with benchmark equal to mean equal to zero

Gain-Loss Spread (GLS) of Estrada (2008)

20,min

0,min(

mm

mmiiHRi RE

RRE

20,min

0,min(0,min

mm

mmiiEi RE

RRE

M

t

N

ittLG LGTEEGLS

1 1

)/1(

Higher-order moments as Higher-order moments as SSystematic ystematic RRisk isk FFactoractorss Systematic skewness (co-skewness, third moment,

gamma)

The corresponding measure of downside co-skewness risk (HR-gamma) to HR beta:

The measure of systematic downside co-skewness risk (E-gamma):

Systematic kurtosis (co-kurtosis, forth moment, delta)

3

2

(()(

mm

mmiitim RERE

RERRERE

4

3

(()(

mm

mmiitim RERE

RERRERE

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2)(

)0,min()0,min()(

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mmiiHRim RE

RRE

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)0,min()0,min()min(

mm

mmiiEim RE

RRE

Conditional Capital Asset Pricing ModelsImpact evaluation of general market conditions on the adequacy of asset pricing models The conditional four-moment model САРМ:

imtimtimtimtimtimttit kkkkkkR )1()1()1( 6543210

1k0)( ftmt RR 0)( ftmt RR 0kwhere when and where

Literature review

Downside Higher-order Conditional framework moments construction

Bawa&Lindenberg(1977), Arditti (1971), Pettengill, Sundaram Harlow & Rao (1989), Francis (1975) , and Mathur (1995),Estrada (2002, 2007) Kraus&Litzenberger(1976), Galagedera&Maharaj (2004)

The object of research

?? KazakhstanKazakhstan Russia Russia

49 49 Russian companiesRussian companies 10 Kazakhstan companies 10 Kazakhstan companies Source of data: Source of data: MICEXMICEX and KASE and KASE Sample period: Sample period: January 2006 – December 2010January 2006 – December 2010 FrequencyFrequency ofof data: weekly returns.data: weekly returns. WeeklyWeekly returns are calculated as returns are calculated as::

100*5

5

t

tti P

PPr

ASIAASIA

Russian and Kazakhstan stock exchanges Russia Kazakhstan

RTS MICEX KASE

  RTS MICEX KASE Currency USD Rubles Tenge

Number of listings >500 696 125

Market capitalization

USD 1072,332 bln USD 966 bln USD 60.7 bln

Trading volume USD 8601 bln USD 2110 bln

USD 206.5 bln

Dynamics of KZKAK and MICEX index for the period: 01/2008-12/2010

KZKAK indexKZKAK index

MICEX indexMICEX index

30-day volatility of KZKAK and MICEX during 01/2008-12/2010

Top 10 summary statistics of Russian companies: January 2010 –December 2010

CompanyCompany TiTiссkerker ТТ AMAM SDSD ExcessExcess AsymmetryAsymmetryGAZPROM GAZP Equity 48 -0,44 3,67 0,09 0,13

VTB BANK VTBR Equity 46 0,03 3,24 0,87 0,02

LUKOIL LKOH Equity 48 0,00 3,11 0,18 0,06NORILSK NICKEL GMKN Equity 47 0,52 3,57 0,59 -0,03

ROSNEFT OIL ROSN Equity 48 -0,56 3,81 -0,01 0,16

SBERBANK SBER03 Equity 48 -0,54 4,00 0,38 -0,32SBERBANK

PRIV SBERP03 Equity 46 -0,69 3,76 -0,35 -0,48

SEVERSTAL SHMF Equity 46 0,61 4,95 -0,16 0,11SURGUTNE

FTEGAS SNGS Equity 47 -0,29 3,63 0,87 -0,05

TRANSNEFT TRNFP Equity 45 0,34 5,79 1,83 0,91

Top 10 summary statistics of Kazakhstan companies: January 2010 –December 2010

CompanyCompany TickerTicker ТТ AMAM SDSD ExcessExcess AsymmetryAsymmetryKAZAKHTELECOM - PREF KZTKp KZ equity 42 0,17 4,33 16,83 -3,41

BTA BANK BTAS KZ equity 44 -4,53 14,62 0,45 -0,50

KAZKOMMERTSBANK KKGB KZ Equity 46 -0,58 5,55 1,85 0,19KAZMUNAIGAS EXPLORATION

PROD- PREF RDGZp KZ equity 47 -0,18 5,29 2,50 -0,26

KAZMUNAIGAS EXPLORATION PROD RDGZ KZ Equity 42 -1,14 3,34 0,92 0,17

KAZAKHTELECOM KZTK KZ Equity 45 0,71 3,92 3,63 1,49

KAZAKHMYS PLС GB_KZMS KZ Equity 43 -0,86 6,21 1,68 -0,35

HALYK SAVINGS BANK-KAZAKHSTN HSBK KZ Equity 40 -0,15 6,09 2,02 -0,71

EURASIAN NATURAL RESOURCES GB_ENRC KZ Equity 38 0,60 3,61 -0,36 0,12

BANK CENTERCREDIT CCBN KZ Equity 38 0,36 3,62 1,64 0,44

Testing hypothesis

1. Downside risk measures are better for explaining cross-sectional return variations than traditional beta especially during the crisis.

2. The inclusion of higher-order moments (the gamma coefficient of systematic asymmetry and the delta coefficient of systematic kurtosis) may contribute to the explanatory power of one- and-multi-factor models.

3. Co-skewness plays a more important role in explaining Russian returns while co-kurtosis is consistently influential for Kazakhstan stock returns due to the fact that Russian stocks are more skewed but less leptokurtic than Kazakhstan stocks.

Are traditional and downside beta good measures of risk?

betabeta 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. insignificant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

βiD Estrada with τ=μβiD Estrada with τ=μ 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. insignificant

Kazakhstan Stat. insignificant Stat. insignificant Stat. significant

βiD HR with τ=μβiD HR with τ=μ 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. insignificant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

βiD Estrada with τ=0βiD Estrada with τ=0 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. significant Stat. significant Stat. significant

Kazakhstan Stat. insignificant Stat. insignificant Stat. significant

βiD HR with τ=0βiD HR with τ=0 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. significant Stat. significant Stat. significant

Kazakhstan Stat. significant Stat. significant Stat. significant

Are traditional and downside co-skewness good measures of risk?

gammagamma 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. significant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

gamma Estrada with τ=μgamma Estrada with τ=μ 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. insignificant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

gamma HR with τ=μgamma HR with τ=μ 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. insignificant Stat. significant Stat. insignificant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

gamma Estrada with τ=0gamma Estrada with τ=0 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. significant Stat. significant Stat. significant

Kazakhstan Stat. insignificant Stat. insignificant Stat. significant

gamma HR with τ=0gamma HR with τ=0 2006-20072006-2007 2008-20092008-2009 20102010Russsia Stat. significant Stat. significant Stat. significant

Kazakhstan Stat. insignificant Stat. insignificant Stat. insignificant

The best one -The best one -factor modelfactor model 2006-20072006-2007 2008-20092008-2009 20102010

Russia Model with downside βiD Estrada with τ=0

Model with downside βiD HR with τ=0

Model with downside βiD HR with τ=0

R2 12% 27% 26%

Kazakhstan Model wih Gain-Loss Spread

Model with downside βiD HR with τ=0

Model wih Gain-Loss Spread

R2 63% 42% 87%

The best two -The best two -factors modelfactors model 2006-20072006-2007 2008-20092008-2009 20102010

Russia no no Model with co- scewness and co-kurtosis

R2 __ __ 23%

Kazakhstan no no Model with co- scewness and co-kurtosis

R2 __ __ 46%

The best modelThe best model 2006-20072006-2007 2008-20092008-2009 20102010

Russia Model with downside βiD Estrada with τ=0

Model with downside βiD HR with τ=0

Three – factor model with beta, co-kurtosis, and co-

scewness

R2 12% 27% 28%

Kazakhstan Model wih Gain-Loss Spread

Model with downside βiD HR with τ=0

Model wih Gain-Loss Spread

R2 63% 42% 87%

The best model in The best model in downside downside

frameworkframework2006-20072006-2007 2008-20092008-2009 20102010

Russia Model with downside βiD Estrada with τ=0

Model with downside βiD HR with τ=0

Model with downside HR with τ=0

R2 12% 27% 26%

Kazakhstan Model wih Gain-Loss Spread

Model with downside βiD HR with τ=0

Model wih Gain-Loss Spread

R2 63% 42% 87%

Conclusion

1. The explanatory power does improve in terms of a higher coefficient of determination if the traditional CAPM beta coefficient is replaced by one-sided risk measures.

2. The zero rate of return benchmark, the models display better explanatory power.

3. The downside beta specification of Harlow and Rao (1989) proves to be more efficient in explaining cross-sectional return variations than that of Estrada (2007).

4. Gain-Loss Spread is the best measure of risk among analyzed factors in downside constructions for Kazakhstan market.

5. refutation the hypothesis that the inclusion of higher-order moments may better explain cross-sectional return variations

What next? Future InvestigationsWe postulate that the differences in results are related to the underlying firm characteristics of the companies in the two indices taking into account investors’ expectations and size of companies using portfolio formation via ranking by BV/MV and size of companies

Thank you for your attention!