Sector Return Vs Market Return

Study Of Variance Of Sectoral Indices Return And Market Index Return

Presented by: Venkata Vijay P

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

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

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

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

Stages involved

Stage 1: 3rd December 2004 to 25th February 2010 10 indices data

Stage 2 16th November 2007 to 25 February 2010 12 indices date (Power & Realty sectors)

Model 1 (10 indices)

Model

R R Square

Adjusted R

Square

Std. Error of the

Estimate Durbin-Watson

dimension0

8 .993h .986 .985 .0048194 2.191

Model

Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta

Toleranc

e 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

Information and Health indice are excluded in step regression process

Model 2 (10 indices)Model

R R Square

Adjusted R

Square

Std. Error of the


dimension0

8 .993h .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 .509LnCapitalGoods

.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

Information and Health index are excluded in step regression process

Model 3 (12 indices)Model

R R Square

Adjusted R

Square

Std. Error of the


dimension0

8 .995h .991 .990 .0049564 2.311

Auto, Information, power and realty indices are excluded in step regression process

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

Individual Index returns

S.No: Industry Index R Square

Standardized Coefficient of Variable

Durbin Watson test Value

1 Auto 70.10% 0.838 2.0162 OilGas 78.50% 0.886 2.236

3 CapitalGoods 78.20% 0.884 2.2194 Teck 73.40% 0.857 1.985

5 Infotech 46.70% 0.683 1.898

6 HealthCare 50.30% 0.711 2.1567 FMCG 42.20% 0.649 2.1218 Bankex 77.90% 0.883 1.9649 Metal 75.00% 0.866 2.438

10 Consumer 54.60% 0.739 2.254

ARIMA (10 indices)

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


Number of

Outliers

Stationary R-


BSE-Model_1 10 .557 .094 19.860 17 .281 0

Differenced first order auto regressive model analysis of sectoral index returns w.r.t market index return

ARIMA (12 indices)


Number of

Predictors


Number of

Outliers

Stationary R-


BSE-Model_1 12 .567 .099 37.942 18 .004 0

Differenced first order auto regressive model analysis of sectoral index returns w.r.t market index return


Number of

Predictors


Number of

Outliers

Stationary R-


BSE-Model_1 12 .617 .203 24.106 17 .117 0

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

GARCH model 1

Coefficient Std. Error z-stat p-valueconst -

0.000575526

0.000283067

-2.0332 0.04203 **

Auto 0.0462931 0.0124177 3.7280 0.00019 ***OilGas 0.239516 0.0117884 20.3180 <0.00001 ***CapitalGoods 0.14569 0.0111761 13.0359 <0.00001 ***Teck 0.247552 0.0268719 9.2123 <0.00001 ***Infotech 0.0145987 0.0188261 0.7754 0.43807HealthCare -

0.01880690.014133 -1.3307 0.18329

FMCG 0.0951465 0.0112282 8.4739 <0.00001 ***Bankex 0.1899 0.0091917

520.6599 <0.00001 ***

Metal 0.0504284 0.00908555

5.5504 <0.00001 ***

Consumer -0.0317635

0.00883564

-3.5949 0.00032 ***

alpha(0) 1.1038e-05

4.71899e-06

2.3391 0.01933 **

alpha(1) 0.129053 0.0726369 1.7767 0.07562 *beta(1) 0.373557 0.229661 1.6266 0.10383

GARCH Model 2

Coefficient Std. Error z-stat p-valueconst -

0.0008140610.00039846 -2.0430 0.04105 **

Auto 0.027777 0.0145655 1.9070 0.05652 *OilGas 0.264486 0.0153388 17.2430 <0.00001 ***CapitalGoods 0.163031 0.0209349 7.7875 <0.00001 ***Teck 0.225887 0.0329052 6.8648 <0.00001 ***Infortech 0.020244 0.0230149 0.8796 0.37907HealthCare -0.0592089 0.0186342 -3.1774 0.00149 ***FMCG 0.098316 0.0162795 6.0392 <0.00001 ***Bankex 0.20377 0.0132983 15.3229 <0.00001 ***Metal 0.0632153 0.0135674 4.6594 <0.00001 ***Consumer -0.0311538 0.0144295 -2.1590 0.03085 **Power -0.0116296 0.0254286 -0.4573 0.64742Realty -0.0087497 0.0101155 -0.8650 0.38705

alpha(0) 1.59062e-05 8.13827e-06 1.9545 0.05064 *alpha(1) 0.223835 0.15887 1.4089 0.15886beta(1) 1.5158e-012 0.447088 0.0000 1.00000

GARCH results

Since alpha(1) is high in model 1 and hence volatility in the previous period is going to be present in the current period returns also.

alpha(1) in model 2 is insignificant and hence volatility in the previous period is not going to effect in the current period returns volatility

Conclusion

Health and consumer index returns have negative relation

Information index Low correlation as individual Irrelevant in holistic picture

FMCG, Health, Information and consumer index return has low correlation w.r.t market index return

ARIMA One period lag provides better model fit Inclusion of power and realty increases R square

GARCH Inclusion of power and realty would

decrease the impact of volatility clustering in the current period

Technology, oil & gas, Capital goods and banking indices follows the market pattern, explains the market returns

Limitations of study

Periods are different across models BSE 30 might not represent real market

index 5 year weekly returns might not be

enough for the study

Reference

Demirer, R. z., & Lien, D. (2005). Correlation and return dispersion dynamics in Chinese markets. International review of financial analysis., 14(4), 477.

Groenewold, N., Kan Tang, S. H., & Wu, Y. (2008). The profitability of regression-based trading rules for the Shanghai stock market. International review of financial analysis., 17(2), 411.

Malik, F., & Ewing, B. T. (2009). Volatility transmission between oil prices and equity sector returns. International review of financial analysis., 18(3), 95.

McMillan, D. G., & Kambouroudis, D. (2009). Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. Int. Rev. Financ. Anal. International Review of Financial Analysis, 18(3), 117-124.

Meric, I., Ratner, M., & Meric, G. (2008). Co-movements of sector index returns in the world's major stock markets in bull and bear markets: Portfolio diversification implications. International review of financial analysis., 17(1), 156.

Appendix

Index Base Period Base Index Value Date of Launch Method of Calculation

BSE Auto

01 February, 1999

1000 23 August, 2004 Free-float market capitalization

BSE BANKEX

01 January, 2002 1000 23 June, 2003 Free-float market capitalization

BSE Capital Goods

01 February, 1999

1000 09 August, 1999 Launched on full market capitalization method and effective August 23, 2004, calculation method shifted to free-float market capitalization

BSE Consumer Durables

01 February, 1999

1000 09 Augus, 1999 Launched on full market capitalization method and effective August 23, 2004, calculation method shifted to free-float market capitalization

BSE FMCG

01 February, 1999


BSE Healthcare

01 February, 1999


BSE IT

01 February, 1999


BSE Metal

01 February, 1999


BSE Oil & Gas

01 February, 1999


BSE Power Index

03 January, 2005 1000 09 November, 2007

Free-float market capitalization

BSE Realty

2005 1000 09 July, 2007 Free-float market capitalization

Software used

PASW GRETL

Thank You

Sector Return Vs Market Return

Economy & Finance

Transcript of Sector Return Vs Market Return