Sector Return Vs Market Return

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provides the comparison of the sectoral returns of BSE with Market return BSE. at the same time checks out the volatility and its transmission to market returns

Transcript of Sector Return Vs Market Return

  • 1. Study Of Variance Of Sectoral Indices Return And Market Index Return Presented by: Venkata Vijay P
  • 2. Literature Review
    • Ilhan Meric
      • Global diversification of a sector returns vs diversification of markets
      • Sectors are inter-correlated
    • RVza Demirer
      • Sectoral inter-correlated more in market upside movement
      • Only finance sector had high correlation with market in downside movement
      • In US sectors are correlated to market to a higher extent in wide swings
  • 3. Literature Review
    • Farooq Malik
      • Sector movements and volatility transmission
      • Oil sector had major role in transmission of volatility
    • David G. McMillan
      • Usage of GARCH and ARIMA models
      • Better explanation of volatility
  • 4. Objective
    • To understand the variance of the sectoral indexes and market index in terms of returns
    • To understand the mutual influence between the any two sectoral index returns and on market index return
  • 5. Indices
    • Indices
      • Auto
      • Banking
      • Technology
      • Oil & Gas
      • Health
      • Consumer durables
      • Capital goods
      • FMCG
      • Metal
      • Power
      • Information
      • Realty
    • BSE 30 (sensex)
    • Why BSE indices
      • Regularly appropriated
      • Represent the sectoral returns
  • 6. Stages involved
    • Stage 1:
      • 3 rd December 2004 to 25 th February 2010
      • 10 indices data
    • Stage 2
      • 16 th November 2007 to 25 February 2010
      • 12 indices date (Power & Realty sectors)
  • 7. Model 1 (10 indices) Information and Health indice are excluded in step regression process Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson dimension0 8 .993 h .986 .985 .0048194 2.191 Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 8 (Constant) .000 .000 -1.573 .117 OilGas .234 .012 .274 20.083 .000 .293 3.417 Bankex .190 .009 .267 19.981 .000 .306 3.270 Teck .271 .013 .260 21.010 .000 .356 2.812 CapitalGoods .143 .012 .187 12.454 .000 .242 4.135 FMCG .093 .012 .078 8.084 .000 .582 1.719 Metal .047 .009 .075 5.043 .000 .245 4.078 Consumer -.036 .009 -.049 -4.119 .000 .381 2.627 Auto .045 .013 .048 3.455 .001 .285 3.504
  • 8. Model 2 (10 indices) Information and Health index are excluded in step regression process Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson dimension0 8 .993 h .986 .985 .0048092 2.178 Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 8 (Constant) .000 .000 -.662 .509 LnCapitalGoods .148 .012 .191 12.656 .000 .236 4.246 LnOilGas .230 .012 .269 19.648 .000 .288 3.476 LnTeck .269 .013 .259 20.900 .000 .352 2.840 LnBankex .192 .009 .269 20.187 .000 .305 3.284 LnFMCG .095 .012 .079 8.177 .000 .574 1.742 LnMetal .044 .009 .071 4.755 .000 .242 4.130 LnConsumer -.037 .009 -.050 -4.159 .000 .372 2.685 LnAuto .047 .013 .050 3.621 .000 .288 3.472
  • 9. Model 3 (12 indices) Auto, Information, power and realty indices are excluded in step regression process Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson dimension0 8 .995 h .991 .990 .0049564 2.311 Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 8 (Constant) .000 .000 -.945 .347 Bankex .199 .014 .278 14.400 .000 .227 4.414 OilGas .258 .016 .304 16.361 .000 .244 4.104 Teck .261 .016 .248 15.897 .000 .347 2.879 CapitalGoods .152 .018 .191 8.531 .000 .168 5.963 FMCG .110 .018 .078 6.236 .000 .533 1.876 Metal .064 .013 .104 4.783 .000 .178 5.617 HealthCare -.063 .021 -.045 -3.035 .003 .392 2.551 Consumer -.033 .014 -.043 -2.399 .018 .266 3.753
  • 10. Individual Index returns S.No: Industry Index R Square Standardized Coefficient of Variable Durbin Watson test Value 1 Auto 70.10% 0.838 2.016 2 OilGas 78.50% 0.886 2.236 3 CapitalGoods 78.20% 0.884 2.219 4 Teck 73.40% 0.857 1.985 5 Infotech 46.70% 0.683 1.898 6 HealthCare 50.30% 0.711 2.156 7 FMCG 42.20% 0.649 2.121 8 Bankex 77.90% 0.883 1.964 9 Metal 75.00% 0.866 2.438 10 Consumer 54.60% 0.739 2.254
  • 11. ARIMA (10 indices) Differenced first order auto regressive model analysis of sectoral index returns w.r.t market index return Model 1 Statistics ARIMA (0,1,0) Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared R-squared Statistics DF Sig. BSE-Model_1 10 .533 .046 36.102 18 .007 0 Model Statistics ARIMA (1,1,0) Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared R-squared Statistics DF Sig. BSE-Model_1 10 .557 .094 19.860 17 .281 0
  • 12. ARIMA (12 indices) Differenced first order auto regressive model analysis of sectoral index returns w.r.t market index return Model Statistics ARIMA (0,1,0) Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared R-squared Statistics DF Sig. BSE-Model_1 12 .567 .099 37.942 18 .004 0 Model Statistics ARIMA (1,1,0) Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared R-squared Statistics DF Sig. BSE-Model_1 12 .617 .203 24.106 17 .117 0
  • 13. ARIMA results
    • Inclusion of realty and power sector increases R square value
    • The ARIMA models also specify that the market returns can be predicted on the basis of current sectoral returns
  • 14. GARCH model 1 Coefficient Std. Error z-stat p-value const -0.000575526 0.000283067 -2.0332 0.04203 ** Auto 0.0462931 0.0124177 3.7280 0.00019 *** OilGas 0.239516 0.0117884 20.3180