6.1 Characteristics of OSE
6.3.5 Contribution to research
Our tests contribute to previous research in the following ways:
i) Test for the presence of herding behavior in the Norwegian stock market using a market value weighted stock market return. In our research of literature we didn’t find this test to have been carried out previously for the Norwegian stock market.
ii) Test for directional asymmetry in different market states of the Norwegian stock market.
The empirical literature we have been through didn’t indicate that directional asymmetry has been tested earlier for Norway.
ii) A novel interpretation of a non-linear positive relation between and the stock market return as divergence of opinions and heterogeneity. In our research of literature we didn’t find this interpretation to have been precedently used in tests.
6.3.6 Conclusion
The cross sectional absolute deviation test series have delved into the dispersion of asset returns as a means for examining the suppression of own beliefs in favour of the market consensus in the backdrop of high and low market states with respect to trading volume and volatility.
The study set out to explore the case of nonlinearity in the relation between the cross sectional absolute deviation of asset returns and the stock market return.
We run our tests using the market value weighted cross sectional absolute deviation since we wanted big cap stocks to weight more than small cap stocks to mitigate the effects of
illiquidity and thin trading. We found convincing evidence of nonlinearity in the upper and lower half of stock returns including the highest and lowest deciles, in the highest and lowest decile of trading volume, in the lower half of trading volume and in the upper half, lower half and lowest decile of stock market volatility. Nonlinearity was consistently positive.
Directional asymmetry was discovered in the upper and lower states of stock market returns
118 including the highest and lowest decile and in the highest and lowest deciles of stock market volatility.
The implication of our findings is threefold. To begin with we didn’t find evidence of herding.
Secondly, we found evidence of dispersion of beliefs and heterogeneity. Thirdly, we found support for asymmetric effects in bull and bear markets.
Although is not a measure of herding or antiherding, the relation of the dispersion of returns and the market return can be used as an instrument for spotting convergence or divergence of opinions. The findings are illuminated in the context of other relevant
information such as the market states of trading volume, market volatility and market return.
Our research was contained to low frequency data and to CSAD as an indicator for spotting herding behavior. A subject for future exploration is herding behavior in the Norwegian stock market with the Patterson Sharma measure which captures the intraday herding behavior using higher frequency data. Future research could investigate in greater depth the relation between the dispersion of asset returns and stock returns with respect to dispersion of beliefs, sentiment risk and the optimistic hypothesis that the agents with the highest valuation of asset returns set the stock prices.
119 6.3.7 Testing the regression assumptions
Regression assumptions
Here we follow the presentation by Berry (1993, p. 12)
Regression assumption 1: Quantitativeness
All independent variables ( , ,…, ) are quantitative and the dependent variable, , is quantitative, and continuous and measured without error.
Our variables are quantitative and continuous and our data come from reliable sources.
Table 6-24: Descriptive statistics for the market value weighted stock return series
RM_MVW
Table 6-25: Descriptive statistics for cross sectional absolute deviation of returns in upper half, highest decile, lower half and lowest decile market return states
CSAD_RI_U_MVW CSAD_RI_U_D_MVW CSAD_RI_L_MVW CSAD_RI_L_D_MVW
Mean 0,013059 0,016993 0,012713 0,016369
Median 0,011389 0,014821 0,011421 0,014367
Maximum 0,053347 0,053202 0,042543 0,038110
Minimum 0,003662 0,006318 0,004930 0,007897
Std. Dev. 0,006123 0,008078 0,005404 0,006801
Skewness 2,559084 1,648819 1,782950 1,260313
Kurtosis 13,11376 6,302285 7,283961 3,913028
Jarque-Bera 3726,032 127,0471 899,676600 41,925200
Probability 0,000000 0,000000 0,000000 0,000000
Sum 9,088844 2,379073 8,835315 2,291699
Sum Sq. Dev. 0,026056 0,009071 0,020265 0,006429
N 696 140 695 140
Table 6-26: Descriptive statistics for cross sectional absolute deviation of returns in upper half, highest decile, lower half and lowest decile trading volume states
CSAD_RI_V_U_MVW CSAD_RI_V_U_D_MVW CSAD_RI_V_L_MVW CSAD_RI_V_L_D_EW
Mean 0,013936 0,014692 0,011752 0,009978
Median 0,012274 0,012989 0,010531 0,009195
Maximum 0,053347 0,046411 0,038557 0,026140
Minimum 0,004930 0,006168 0,003662 0,003662
Std. Dev. 0,006324 0,005990 0,004875 0,003661
Skewness 2,295717 1,962841 1,953402 2,067982
Kurtosis 11,050890 9,258677 8,333432 9,054990
Jarque-Bera 2584,103000 318,395200 1218,378 313,653100
Probability 0,000000 0,000000 0,000000 0,000000
Sum 10,062080 2,056817 7,862084 1,396868
Sum Sq. Dev. 0,028834 0,004988 0,015872 0,001863
N 722 140 669 140
Table 6-27: Descriptive statistics for cross sectional absolute deviation of returns in upper half, highest decile, lower half and lowest decile stocks’ return cross sectional volatility states
120
CSAD_RI_SD_U_MVW CSAD_RI_SD_U_D_MVW CSAD_RI_SD_L_MVW CSAD_RI_SD_L_D_MVW
Mean 0,014548 0,019511 0,011221 0,009945
Median 0,012777 0,017318 0,010631 0,00949
Maximum 0,053347 0,053347 0,046411 0,019782
Minimum 0,005320 0,005578 0,003662 0,00493
Std. Dev. 0,006938 0,009901 0,003613 0,002951
Skewness 1,789009 1,066971 2,126123 0,967316
Kurtosis 7,657291 4,065808 16,307750 3,866539
Jarque-Bera 1000,285000 33,189660 5652,024000 26,21318
Probability 0,000000 0,000000 0,000000 0,000002
Sum 10,125380 2,731484 7,798781 1,392297
Sum Sq. Dev. 0,033456 0,013627 0,009059 0,001211
N 696 140 695 140
Where
RM_MVW is a time series of market value weighted market returns based on163 Norwegian stocks.
CSAD_RI_U_MVW is a time series of the cross sectional absolute deviation of stock returns in the upper half of daily observations of market return.
CSAD_RI_U_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the highest decile of daily observations of market return.
CSAD_RI_L_MVW is a time series of the cross sectional absolute deviation of stock returns in the lower half of daily observations of market return.
CSAD_RI_L_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the lowest decile of daily observations of market return.
CSAD_RI_V_U_MVW is a time series of the cross sectional absolute deviation of stock returns in the upper half of daily observations for market trading volume.
CSAD_RI_V_U_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the highest decile of daily observations for market trading volume.
CSAD_RI_V_L_MVW is a time series of the cross sectional absolute deviation of stock returns in the lower half of daily observations for market trading volume.
CSAD_RI_V_L_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the lowest decile of daily observations for market trading volume
CSAD_RI_SD_U_MVW is a time series of the cross sectional absolute deviation of stock returns in the upper half of daily observations of cross sectional market volatility.
CSAD_RI_SD_U_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the highest decile of daily observations of cross sectional market volatility.
CSAD_RI_SD_L_MVW is a time series of the cross sectional absolute deviation of stock returns in the lower half of daily observations of cross sectional market volatility.
CSAD_RI_SD_L_D_MVW is a time series of the cross sectional absolute deviation of stock returns in the lowest decile of daily observations of cross sectional market volatility.
121 Kurtosis is a descriptive statistic for fat tails which shows the probability for extreme events (in finance called “black swans”). When kurtosis is greater than 3 the variable does not follow a normal distribution. The Jarque-Bera test is based on the ratio of kurtosis and skewness.
From the above tables we see that none of the variables used in our regression equations is normally distributed. This is in line with the stylized fact that asset returns do not follow a normal distribution (Cont 2001).
Regression assumption 2 Variance All variables have some variance.
The descriptive statistics show that our variables have nonzero variance.
122 Regression assumption 3 Multicollinearity
There is not an exact linear relationship between two or more of the independent variables, i.e. there is not perfect multicollinearity.
Our model is set up as a quadratic regression equation. In a strict sense the relation between and doesn’t violate the assumption of no multicollinearity (Gujarati 2003 pp. 343-344). However, since and are functionally related, the coefficients of and
are highly correlated, which makes the standard errors of the estimators bigger and increases the risk of type II errors.
According to Gujarati (2003 p. 359) multicollinearity cannot be tested but can be measured.
One commonly used measure is the variance inflating factor (VIF).
Table 6-28:VIF measurement
Model Percentile Uncentered VIF
123 Testing the CSAD regressions in EViews with VIF we get centered and uncentered values.
Centered VIF has no ability to discover collinearity involving the intercept (Gross 2003 p.
304). Uncentered VIF values are above 10. According to Gross this shows collinearity involving the intercept. Gross suggests in cases with high uncentered VIF removing the intercept. This would lead to the advantage of lower standard errors and higher t-values for the coefficients of and . The disadvantage of this method is that one forces the regression line to go through the origo instead of going through the line that minimizes least squares. Moreover there is a risk that econometric packages are not calibrated for calculating regression equations without intercepts (Eisenhower 2003). For instance, preliminary tests showed that after removing the intercept, 10 out of 12 models we used in our tests, receive negative R-square values (EViews 7 users Guide II 2009 p. 13 and Hayashi 2000 p. 21).
Another issue is that one needs a theoretical justification for assuming the intercept equal to zero.
Because of the above issues we let the intercept remain in the model.
124 Regression assumption 4: Mean of the error term
At each set of values for the independent variables, ,
| (i.e. the conditional expected mean value of the error term is zero). The conditional and the unconditional expectations are equal when the error term is independent from the regressors by the law of iterated expectations (Bailey 2005 p.
59).
At this point we assume that the conditional and the unconditional expected mean of the error term are equal. This postulation is tested under assumptions 5 and 6.
We test the null hypothesis that the unconditional expected mean of the residual is zero with the Jarque-Bera test in EViews.
Table 6-29: Jarque-Bera test of the expected mean of the residual
Model Percentile t-statistic
[Probability]
The Jarque-Bera tests show that we can’t reject the null hypothesis that the unconditional expected mean of the residual is 0.
125 Regression assumption 5: Correlation with the error term
For each , (i.e., each independent variable is uncorrelated with the error term).
This assumption is tested together with the regression assumption 6.
Regression assumption 6: Variance of the error term
At each set of values for the independent variables, ,
| where is a constant (i.e., the conditional variance of the error term is constant); this is known as the assumption of homoscedasticity.
To test assumptions 5 and 6 we used the White-test statistic (1980) which is a test of the null hypothesis of no correlation of the explanatory variables with the residual and no
heteroscedasticity (EViews 7 2009 User Guide II, pp. 163-165).
Table 6-30: Homoscedasticity test
Model Percentile F-statistic Obs*R-squared Scaled explained SS
“Obs” in the table above stands for observations.
The White test statistics show that the models in Table 6-30 don’t pass White’s test.
126 Since the models in Table 6-30 don’t pass the White test we want to examine if this is due to covariance between the regressors and the residuals. The null hypothesis is that the covariance between the regressors and the residuals is zero.
Table 6-31: Covariance test of regressors with the residuals
Model Percentile Regressors Covariance
regressor with residuals
t-statistics probability N
M - 9 Highest decile | |
All models in Table 6-31 pass this test. We conclude that assumption 5 of zero covariance between the regressors and the error term is fulfilled while assumption 6 of homoscedasticity is not. The remedy to the violation of assumption 6 was a correction of the regression results using the Newey–West estimators. That means that the standard errors and as a consequence the t-values were adjusted to account for heteroscedasticity (EViews 7 2009 User Guide II, pp. 32-33).
127 Regression assumption 7: Autocorrelation.
For any two observations, ( ,…, ) and ( , ,…, ), ( ) (i.e., error terms for different observations are uncorrelated); this assumption is known as lack of autocorrelation.
The null hypothesis is that there is not serial correlation. This is tested by means of the Breusch-Godfrey serial correlation Lagrange multiplier (LM) test.
Table 6-32: Breusch-Godfrey serial correlation LM test
Test Equation Percentile Lag F-statistic
[Prob.]
“Obs” in the table above stands for observations.
The test results show that we reject the null hypothesis of no autocorrelation for models M - 9, M - 10 (lower half), M - 11 , M - 12 , M - 13 , M - 14 (lower half) with 90 % or higher
confidence interval. That means that assumption 7 is not fulfilled for these models. The remedy to the violation of assumption 7 is the correction of the regression results using the Newey–West estimators. That means that the standard errors and as a consequence the t-values were adjusted to account for autocorrelation (EViews 7 2009 User Guide II, pp. 32-33).
128 Regression assumption 8: Distribution of the error term
At each set of values for the independent variables, the error term is normally distributed.
The null hypothesis: The standardized residuals are normally distributed.
Table 6-33: Jarque-Bera normality test
Test Equation Percentile Jarque-Bera
[Probability]
The test results show no-normal distribution of the residuals for all models tested in Table 6-33. A violation of assumption 8 is not as serious as the violation of assumption of
homoscedasticity and autocorrelation. Departure from normality does not impair inferences when the data set is large (Bhattacharyya and Johnson 1997, p. 359). Greene (2012, pp. 64-67) states that a normal distribution of the residual is not necessary for establishing results that allow statistical inference. This is because statistical inference can be based on the law of large numbers which concerns consistency and the central limit theorem which concerns the asymptotic distribution of the estimators.
129 Regression assumption 9: Stationarity.
The independent variables are stationary processes.
The null Hypothesis is that the time series have a unit root. This is assessed by means of the augmented Dickey-Fuller unit root test.
Table 6-34: Augmented Dickey-Fuller unit root test
Time Series N t-Statistic Prob.
1358 -37,49063 0,0000
1125 -2,831729 0,0542
The unit root tests show that we can reject the null hypothesis of a unit root at 1% significance level for and 10 % significance level for . So we consider the processes as
stationary.
130 6.4 Testing the equity premium puzzle
The equity premium puzzle is based on the empirical fact that the difference between return on a risky asset and a risk free asset is too great compared to what is assumed to be a normal level of risk aversion.
Let the price of an asset be (Cochrane 2005, p. 6):
[ ].
Dividing both sides by we get:
[ (
)] [ ] where is the gross return.
The Euler equations are:
[ ]
[ ] } [ ]
where is the stochastic discount factor, is the subjective discount factor, is the intertemporal marginal rate of substitution of consumption between periods and
is the risk premium,
[ ] [ ] [ ] [ ] [ ]
[ ] [ ]
[ ] [ ]
[ ] [ ]
[ ] | [ ] |
The last inequality is the HJ-bound (Cochrane 2005, p. 93).
For logarithmic utility functions the HJ-bound transforms to (Pennacchi 2008, p. 307):
| [ ]|
where stands for the coefficient of relative risk aversion
and for the volatility of consumption growth.
The HJ-bound can be traced in the [ ], space using the following equation:
[ [ ] [ ]] [ [ ] [ ]]
131 where is the transposed, is a vector of returns and is a
variance-covariance matrix. For the expression simplifies to:
[ [ ] [ ]]
|[ [ ] [ ]]
| |
[ ]
[ ]|
The HJ-bound can be used for predicting a quantified relation between the equity premium and the stochastic discount factor and can be used for testing CCAP-models against this prediction.
Consumption capital asset pricing models connect together consumption with asset returns.
They have certain common features such as an intertemporal utility function, a periodic utility function, a pricing kernel alias stochastic discount factor, a subjective discount factor which shows time preferences, a risk aversion coefficient and a relative risk aversion coefficient. In addition they make certain assumptions on the time separability of utility and the
homogeneity or heterogeneity of consumers.
132 6.4.1 Hypotheses
We tested the following hypothesis:
H - 13: The stochastic discount factor specified by the power utility preferences explains the equity premium and satisfies the HJ-bounds.