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Falguni H. Pandya Faculty member Centre for Management Studies Dharmsinh Desai University Contact No.- +91 9913879766 Email: fhpandya@gmail.com |
Hardik S Shah Faculty member Centre for Management Studies Dharmsinh Desai University Contact No.- +91 8511169690 Email: shah_hardik07@yahoo.in |
Falguni H. Pandya
Ms. Falguni H.Pandya is a faculty member with Centre for Management Studies, Dharmsinh Desai University, Nadiad, Gujarat in the Finance Area. Her areas of interest include Financial Management, Risk Management, Security Analysis and Portfolio Management and International Financial Management. Her research papers have been published in national level referred journals and in the form of national and international conference proceedings.
Postal Address
B.No. 45, Gayatri Nagar Society, Nr. G E B, Maktampur Road, Bharuch-392012
Hardik S Shah
Mr. Hardik S Shah is a faculty member with Centre for Management Studies, Dharmsinh Desai University, Nadiad, Gujarat in the Finance Area. His areas of interest include Financial Management, Strategic Finance Management, Taxation, Accounting and Management of Financial Services. He is pursuing research in the area of Initial Public Offering.
Postal Address
Faculty,Centre for Management Studies (MBA Department)
Dharmsinh Desai University
College Road, Nadiad-387001, Gujarat, India
The study attempts to find out the market efficiency of Indian and US stock market. For that two main indices of India namely BSE Sensex and CNX Nifty are studied along with US-Dow Jones for a various time brackets between 1993 to 2013. Various tests like K-S Test, Runs test, Auto correlation test, t-test and all descriptive statistics with JB test are conducted for all three indices for a time bracket of 1993-2013, 1993-1995(normal), 1996-2000 (Before DotCom Bubble), 2000-2002 (Dotcom Bubble), 2002-2007 (Before Credit Crisis), 2007-2009 (Credit Crisis) and 2009-2013 (After Credit Crisis). The research summarizes that Indian stock market is not weak form efficient in all periods however; from year 2002 onwards stock market exhibits some signs of efficiency.
Key Words: - Market Efficiency, Indices
Emerging markets usually do not have strict standards in accounting and finance regulations and the level of market efficiency as that of advanced economies of the world. However, emerging economy like India and others have physical financial infrastructure including banks, stock exchange, money market and capital market. In addition, investment in emerging markets are sought by investors to tap the market efficiency and thus to earn higher return. However, investments in emerging markets come with higher risk due to political instability, domestic infrastructure problems, currency volatility and limited equity opportunities. However, despite the stage of the different market, it is impossible to ‘beat’ the market because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information. In other words, as per Efficient Market Hypothesis (EMH), stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. Efficient market hypothesis is stated-weak form, semi strong form and strong form of efficiency on three common forms.
Malliaris and Urrutia (1992) analyzed possible causal relationship among national stock markets around the October 1987 stock market crash. The paper summarized that lead-lag relationship do not exists before and after market crash. The result provided empirical evidence of the passive role played by Tokyo but fails to confirm leading role played by New York or non-Japanese Asian markets during 1987 crash. Schwert (1989) analyzed behavior of stock return volatility using daily data from 1985 to 1988 and found that on October, 19 there was a largest percentage change in value of the market and stock market volatility jumped during and after market crash. However, it was also observed that it returned to normal level more rapidly than any other historical crises. Research by Yüksel (2002) on Istanbul Stock Exchange during the Russian crisis in 1998 says that the comparison of the relationship during the crisis period to those during pre and post crisis period proved that there was a structural change regarding the price impact of the trading volume. Chancharoenchai and Dibooglu (2006) examined the volatility spillovers in Southeast Asian emerging markets in the context of the mid-1997 financial crisis, and found spillover effects between various equity markets. The paper summarized that the sudden fallout in Thailand seems to have played an important role in the variation in excess returns in other Southeast Asian markets and it supports the idea of the “Asian Contagion” suggested that the crisis started in the Thailand and spread over to other financial markets. Olowe (2009) found that in the Nigerian Stock market returns show persistence in volatility and clustering and asymmetric properties.
Kazi et al (2011) investigated the contagion effect between stock market of US and sixteen OECD countries due to global financial crisis and it found existence of contagion effect between US and 16 OECD countries. Boyer et al (2006) provided empirical evidence that market crises are speeded globally. Authors categorized the stock markets of emerging economies in to two parts: those that are eligible for purchase by foreigners and those that are not. Research proved that greater co-movement during high volatility period, especially for accessible stock index returns indicates that crises spread through the asset holding of the international investors rather than through changes in fundamentals. A novel approach was adopted by Herrero et al (2008) as they applied event study methodology to analyze whether five south East Asian countries which devalued in 1997 namely Indonesia, Korea, Malaysia, Philippines and Thailand. The study was based on the assumption that the expectation of devaluation should help the stock of exporting firms outperform those of non-performing firms and the study could find evidence to support this hypothesis, however at different degrees depending upon the country.
Study by Salman et al (2012) attempted the short term relationship between Karachi Stock Exchange, Bombay Stock Exchange, Hong Kong Stock Exchange, and Tokyo Stock Exchange after the financial crisis of 2007. It concluded that 1. Tokyo and Karachi Stock Exchange, 2. Hong Kong and Bombay, 3. Karachi and Bombay (before and after crisis) 4. Tokyo and Bombay have no short term relationship with each other; while Tokyo and Hong Kong stock exchange have short term relationship with each other after the financial crisis of 2007. Research by Dhal (2009) derives some crucial insights from the multivariate co integration analysis of stock prices indices for the global markets of the US, UK, Japan, Hong Kong, Singapore and India. Result says that these markets shared single co integration relationship and the Indian market played a key role and the analysis showed that global crisis could not have been associated with the breakdown of the long-run relationships among the markets. Ghosh (2006) tried to address India’s existence from the effect of financial crisis and analyzed that India’s success can be attributed to key policy decisions namely devaluation, IMF, partial liberalization of the domestic financial sector and graduation opening of the external sector. In a similar track paved by Ghosh (2006); Study by Jeyanthi et al (2012) summarized that there was no short term as well as long term negative impact of financial crisis on the Indian stock exchange. Further, the paper found that the Indian stock market was unaffected by the global financial crisis. Study by Patel et al (2011) confirmed the existence of weak form of efficiency in Indian market for a time period of 2004 to 2011. Jethwani, Achuthan (2013) investigated weak form efficiency of the Indian market during, before and after financial crisis and the result shows that Indian market is not weak form efficient in all period and after 2002 it behaves in a more efficient way. This confirms with the earlier study conducted by Patel et al (2011).
Objectives:
1. To examine the market efficiency level of selected stock markets and to test spillover effect of the financial crash for a period of 1993 to 2013 for Indian and US stock market.
2. To study and examine the extent of crisis at various periods for the given stock markets and to derive efficiency level of Indian stock market.
To study the above mentioned objectives, two main indices of India and one of US are selected as sample for the study. The daily closing prices of BSE SENSEX, CNX NIFTY 50 and DOW JONES of last 21 years is collected (1st January 1993-31st December 2013). The daily price values of the CNX Nifty 50 and BSE Sensex is collected from the website of NSE and BSE respectively. Dow Jones data are collected from yahoo finance. The period was further broken in to 1993-2013 (Total study period), 1993-1995 (Normal), 1996-2000 (Before Dot com Bubble), 2000-2002 (Dot com Bubble), 2002-2007 (Before Credit Crisis), 2007-2009 (Credit Crisis) and 2009-2013 (After Credit Crisis). Before conducting any test, it is required to test whether data are stationary or not and for that Augmented Dicky Fuller Test (ADF) was conducted.
Returns are calculated using logarithmic method and it was analyzed by using time series volatility. Further, to study the variability in stock prices, standard deviation is used as a proxy. The logarithmic method to calculate return is as follows:
Rt = In (It / It-1)* 100
Where Rt stands for return of the index and It indicate index value at time‘t’.
To compute standard deviation following standard formula was used.
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Data collection and interpretation
Augmented Dicky Fuller (ADF) test indicates that data are stationary at first difference.
Normality test
Many statistical tests (e.g. t-test) require that data are normally distributed and therefore it should always be checked if this assumption is satisfied. The null hypothesis is that the data is normally distributed and the alternative hypothesis is that the data is not normally distributed. The Kolmogorov-Smirnov test is used for sample of more than 2000. If p-value is less than 0.05, null hypothesis is rejected.
Here as p value is 0.00 for all three indices (table 1 of appendix A B and C), null hypothesis is rejected which means that data are not normally distributed. Hence, non-parametric tests are conducted.
It is a non-parametric test and is used to test weak form of efficiency of stock market. This test emphasizes on the direction of change and does not consider the change in value. The test ignores the type of distribution followed in time series and is used to check the randomness in time series under consideration. At 5% confidence level, if the observed value of z is 1.96, null hypothesis is accepted. Following are the null hypothesis to check efficiency of the market.
1. Indian Stock Market is weak form efficient market.
2. Indian Stock market follows random walk
Analysis for Descriptive Statistics:
To study the above mentioned objectives, one of the basic requirements is that data should be normal. Usually, main two parameters namely mean and variances are used to describe the distribution. For that skewness, kurtosis, Jarque-Bera test is applied to test the normality of the data. Jarque-Bera test is a goodness of fit test of whether the sample data have the skewness and kurtosis matching a normal distribution. In the data, when the skewness is zero, kurtosis is three and JB is zero, then it is perfectly normally distributed. Coefficient of variation shows volatility of the data.
SENSEX
Five (1993-2013,1993-1995, 2000-2002, 2002-2007, 2007-2009) out of seven periods under study has negative skewness, though the value is small; it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining two periods (1996-2000, 2009-2013) shows positive skewness. In case of Kurtosis, four period (1993-1995, 1996-2000, 2000-2002, 2007-2009) out of seven falls under Platykurtic distribution and other three periods (1993-2013, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Therefore, it can be said that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. For Jarque Bera Test, null hypothesis of random walk is rejected for all periods as value of JB is higher than zero. Coefficient of Variation in initial two periods (1993-2013, 1993-1995) is quite high compare to other five periods. The value of CV shows that gradually market has become less volatile. (Refer to Table No.2 of appendix A)
CNX NIFTY
Four (1993-2013, 2000-2002, 2002-2007, 2007-2009) out of seven periods under study has negative skewness, and it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining three periods (1993-1995, 1996-2000, 2009-2013) shows positive skewness. In case of Kurtosis, three period (1993-1995, 2000-2002, 2007-2009) out of seven falls under Platykurtic distribution and other four periods (1993-2013, 1996-2000, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Hence, it can be summarized that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. Jarque Bera test and coefficient of variation indicates same conclusion as that of Sensex. (Refer to Table No. 2 of Appendix B)
DOWJONES
Five (1993-2013,1993-1995, 1996-2000, 2000-2002, 2009-2013) out of seven periods under study has negative skewness, even if the value is small it indicates the tail on the left side is longer than the right side and the bulk of the values lies including median to the right side of the mean. Remaining two periods (2000-2007, 2007-2009) are having positive skewness. In case of Kurtosis, three period (1993-1995, 2000-2002, 2002-2007) out of seven falls under Platykurtic distribution and other four periods (1993-2013, 2002-2007, 2009-2013) falls under Leptokurtic distribution. Hence, we can say that distribution of data is not normal. So on the basis of descriptive statistics, null hypothesis of random walk is rejected. For Jarque Bera Test, null hypothesis of random walk is rejected for all periods as value of Jarque Bera is higher than zero. Coefficient of Variation is quite high in the overall period only. (Refer to Table No. 2 of Appendix C)
In summary, for Sensex except two periods (1993-1995, 2007-2009), remaining five periods are not normally distributed and which leads to rejection of the null hypothesis of random walk and similar result is found for CNX Nifty. While for the Dow Jones in all seven periods, the value of P is significant. That means the data is not normally distributed which leads to rejection of the null hypothesis of random walk for these periods.
Runs Test and analysis
For both the indices namely Sensex and Nifty, two (2007-2009, 2009-2013) out of seven periods the value of Z is insignificant at 5 % significance level and its value lies inside the interval of ±1.96, so null hypothesis of random walk is accepted. While in five (1993-2013, 1993-1995, 1996-2000, 2000-2002, and 2002-2007) out of seven periods, null hypothesis of random walk is rejected (Refer to Table No. 4 of Appendix A, B). While for the Dow Jones in four periods (1993-1995, 1996-2000, 2000-2002, 2009-2013) out of seven periods the value of Z is insignificant at 5 % significance level and its value lies inside the interval of ±1.96, so null hypothesis of random walk is accepted. While in three (1993-2013, 2002-2007, 2007-2009) out of seven periods, null hypothesis of random walk is rejected. (Table 4 of Appendix C).
Autocorrelation and analysis
Autocorrelation is the cross-correlation of a signal with itself as it describes the correlation between values of the process at different times as a function of the two times or of the time lag. For null hypothesis to be true, observed serial correlation should not be statistically significant i.e. should not be greater than three times the standard error of coefficient. In case of Ljung Box Q statistics, if the value of P <0.05 then it can be said that autocorrelation exist.
For all three indices namely Sensex, CNX Nifty and Dow Jones in all periods the value of P is significant which means that autocorrelation exists in the series. In addition to that, there is at least one lag where correlation coefficient is > 3*Standard error for all periods. Hence, existence of autocorrelation ultimately rejects the null hypothesis of random walk in all periods. (Table No. 6 of Appendix A, B and C).
Kruskal-Wallis test
Kruskal-Wallis test is a one-way analysis of variance by ranks and a non-parametric method to test whether samples originate from the same distribution. The method is used for comparing more than two samples that are independent or not related and it does not assume a normal distribution of the residuals. The null hypothesis are.
H0: All Three Stock market indices follow the random walk for the entire period i.e. from year 1993 to 2013.
H0: Random walk follows among all three indices before the credit crisis i.e. from year 2002 to 2007.
H0: Random walk follows among all three indices during the credit crisis i.e. from year 2007 to 2009.
H0: Random walk follows among all three indices after the credit crisis i.e. from year 2009 to 2013.
Table A:- Kruskal Wallis For The Entire Period
| Test Statisticsa,b | |
| RETURN | |
| Chi-Square | 1.072 |
| df | 2 |
| Asymp. Sig. | .585 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: INDICES | |
Table B:- Kruskal Wallis For The Period 2002-2007
| Test Statisticsa,b | |
| RETURN | |
| Chi-Square | 26.098 |
| df | 2 |
| Asymp. Sig. | .000 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: INDICES | |
Table C:- Kruskal Wallis For The Period 2007-2009
| Test Statisticsa,b | |
| RETURN | |
| Chi-Square | .049 |
| df | 2 |
| Asymp. Sig. | .976 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: INDICES | |
Table D:- Kruskal Wallis For The Period 2009-2013
| Test Statisticsa,b | |
| RETURN | |
| Chi-Square | 1.357 |
| df | 2 |
| Asymp. Sig. | .507 |
| a. Kruskal Wallis Test | |
| b. Grouping Variable: INDICES | |
If the P value is small, it rejects the idea that the difference is due to random sampling. For the entire period the p-value is 0.585, for the period of 2002-2007, the p-value is 0.000, for the period of 2007-2009, the p-value is 0.976 and for the period of 2009-2013, the p-value is 0.507
Consequently, it can be concluded that only before the period of credit crisis, the difference is not due to random sampling. Random walk follows between all three indices for entire period of 21 years as well as during the credit crisis and after the crisis period. A random walk is defined by the fact that price changes are independent of each other.
This study investigates the market efficiency of selected three stock markets. In addition to that, this research analyses that whether the crisis period alters the conclusion of efficiency of the stock market. The daily return series of SENSEX, S&P CNX Nifty and Dow Jones for a period of 1993 to 2013 is considered for the study. The research summarizes that Indian stock market is not weak form efficient in all periods however, from year 2002 onwards stock market exhibits some signs of efficiency. Further, all three independent samples are compared and is concluded that it follows random walk during and after the crisis period but not before the crisis period.
1. Boyer Brian H., Kumagai Tomomi and Yuan Kathy (2006), “How Do Crises Spread? Evidence from Accessible and Inaccessible Stock Indices”, The Journal of Finance, Vol. 61, No. 2 , pp. 957-1003
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3. Dhal Sarat (2009), “ Global Crisis and the Integration of India's Stock Market”, Journal of Economic Integration, Vol. 24, No. 4, pp. 778-805
4. Ghosh Arunabha (2006), “Pathways Through Financial Crisis: India”, Global Governance, Vol. 12, No. 4, pp. 413-429
5. Herrero Alicia García, Gyntelberg Jacob and Tesei Andrea (2008), “The Asian crisis: what did local stock markets expect?” BIS Working Papers No 261
6. Jethwani Kinjal, Achuthan Sarla (2013), “Stock market efficiency and crisis: evidence from India” Asia-Pacific Finance and Accounting Review ISSN 2278-1838: Volume 1, No. 2, pp. 35-43.
7. Jeyanthi B. J. Queensly, William Albert S J, Kalavathy S. Titus (2012), “The Impact Of Global Financial Crisis On Indian Stock Markets”, International Journal Of Research In Commerce And Management, Vol. 3, No. 2, pp. 71-76.
8. Kazi Irfan Akbar, Guesmi Khaled and Kaabia Olfa (2011), “Contagion Effect of Financial Crisis on OECD Stock Markets”, Discussion Paper No. 2011-15. Accessed from http://www.economics-ejournal.org/economics/discussionpapers/2011-15 as on 12-12-2013.
9. Malliaris A. G. and Urrutia Jorge L. (1992), “The International Crash of October 1987: Causality Tests”, The Journal of Financial and Quantitative Analysis, Vol. 27, No. 3, pp.353-364
10. Olowe Rufus Ayodeji (2009), “Stock Return Volatility, Global Financial Crisis And The Monthly Seasonaleffect On The Nigerian Stock Exchange” African Review of Money Finance and Banking, pp. 73-107
11. Patel Nikunj R., Patel Bhavesh K and Ranpura Darshan (2011), “Testing Weak Form Market Efficiency Of Indian Stock Markets”, Ss International Journal Of Business And Management Research, Vol. 1, No. 3, pp. 1-28.
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Websites
ü en.wikipedia.org
ü finance.yahoo.com
ü money.cnn.com
ü www.slideshare.net
Table A:-ADF for BSE
| ADF Test Statistic | -33.55698 | 1% Critical Value* | -3.4348 | |
| 5% Critical Value | -2.8626 | |||
| 10% Critical Value | -2.5674 | |||
| *MacKinnon critical values for rejection of hypothesis of a unit root. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(CLOSE,2) | ||||
| Method: Least Squares | ||||
| Date: 12/19/13 Time: 16:44 | ||||
| Sample(adjusted): 1/11/1993 5/04/2012 | ||||
| Included observations: 5040 after adjusting endpoints | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(CLOSE(-1)) | -1.009154 | 0.030073 | -33.55698 | 0.0000 |
| D(CLOSE(-1),2) | 0.082824 | 0.026836 | 3.086319 | 0.0020 |
| D(CLOSE(-2),2) | 0.078042 | 0.023238 | 3.358451 | 0.0008 |
| D(CLOSE(-3),2) | 0.054351 | 0.019199 | 2.830950 | 0.0047 |
| D(CLOSE(-4),2) | 0.046042 | 0.014088 | 3.268078 | 0.0011 |
| C | 3.389471 | 2.206431 | 1.536179 | 0.1246 |
| R-squared | 0.464436 | Mean dependent var | -0.008262 | |
| Adjusted R-squared | 0.463904 | S.D. dependent var | 213.7032 | |
| S.E. of regression | 156.4704 | Akaike info criterion | 12.94480 | |
| Sum squared resid | 1.23E+08 | Schwarz criterion | 12.95257 | |
| Log likelihood | -32614.90 | F-statistic | 873.0882 | |
| Durbin-Watson stat | 2.004013 | Prob(F-statistic) | 0.000000 | |
Table B:- ADF for NSE
| ADF Test Statistic | -33.34988 | 1% Critical Value* | -3.4348 | |
| 5% Critical Value | -2.8626 | |||
| 10% Critical Value | -2.5674 | |||
| *MacKinnon critical values for rejection of hypothesis of a unit root. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(CLOSE,2) | ||||
| Method: Least Squares | ||||
| Date: 12/19/13 Time: 16:50 | ||||
| Sample(adjusted): 1/11/1993 6/08/2012 | ||||
| Included observations: 5065 after adjusting endpoints | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(CLOSE(-1)) | -1.004996 | 0.030135 | -33.34988 | 0.0000 |
| D(CLOSE(-1),2) | 0.067270 | 0.026919 | 2.499027 | 0.0125 |
| D(CLOSE(-2),2) | 0.069741 | 0.023318 | 2.990804 | 0.0028 |
| D(CLOSE(-3),2) | 0.047503 | 0.019269 | 2.465217 | 0.0137 |
| D(CLOSE(-4),2) | 0.041637 | 0.014057 | 2.962037 | 0.0031 |
| C | 0.998543 | 0.663774 | 1.504342 | 0.1326 |
| R-squared | 0.469785 | Mean dependent var | -0.004859 | |
| Adjusted R-squared | 0.469261 | S.D. dependent var | 64.77475 | |
| S.E. of regression | 47.18961 | Akaike info criterion | 10.54741 | |
| Sum squared resid | 11265683 | Schwarz criterion | 10.55514 | |
| Log likelihood | -26705.31 | F-statistic | 896.4813 | |
| Durbin-Watson stat | 2.003658 | Prob(F-statistic) | 0.000000 | |
Appendix A BSE Sensex
Table 1:-Test for Normality:
| Tests of Normality | |||
| Kolmogorov-Smirnova | |||
| Statistic | df | Sig. | |
| BSE | .051 | 5147 | .000 |
| a. Lilliefors Significance Correction | |||
Table 2:-Descriptive statistics of different period
| N | TIME PERIOD | MEAN | STD. DEV. | COE. OF VAR. | SKEWNESS | KURTOSIS | JORQUE BERRA |
| 5045 | 1993-2013 | 0.040 | 1.638 | 40.689 | -0.099 | 4.967 | 5194.549 |
| 676 | 1993-1995 | 0.030 | 1.533 | 51.099 | -0.012 | 1.560 | 68.574 |
| 1025 | 1996-2000 | 0.052 | 1.767 | 33.964 | 0.032 | 1.869 | 149.383 |
| 645 | 2000-2002 | -0.091 | 1.725 | -19.050 | -0.410 | 2.139 | 141.015 |
| 1272 | 2002-2007 | 0.150 | 1.398 | 9.339 | -0.768 | 6.650 | 2468.519 |
| 346 | 2007-2009 | -0.207 | 2.677 | -12.960 | -0.149 | 1.037 | 16.764 |
| 1243 | 2009-2013 | 0.063 | 1.380 | 21.828 | 1.152 | 15.550 | 12798.870 |
Table 3:-Descriptive statistics of each year
| N | TIME | MEAN | STD. DEV. | CO. OF VAR. | SKEWNESS | KURTOSIS | JORQUE BERRA |
| 214 | 1993 | 0.129 | 1.864 | 14.467 | -0.442 | 1.153 | 18.820 |
| 231 | 1994 | 0.069 | 1.435 | 20.716 | 0.577 | 1.783 | 43.419 |
| 231 | 1995 | -0.101 | 1.261 | -12.502 | 0.115 | 0.385 | 1.934 |
| 238 | 1996 | -0.003 | 1.522 | -443.773 | 0.504 | 0.974 | 19.508 |
| 246 | 1997 | 0.069 | 1.638 | 23.622 | -0.309 | 4.315 | 194.741 |
| 244 | 1998 | -0.074 | 1.908 | -25.821 | -0.046 | 1.306 | 17.413 |
| 248 | 1999 | 0.199 | 1.815 | 9.118 | 0.053 | 1.492 | 23.121 |
| 250 | 2000 | -0.093 | 2.204 | -23.822 | -0.243 | 0.946 | 11.787 |
| 248 | 2001 | -0.079 | 1.719 | -21.655 | -0.462 | 1.679 | 37.976 |
| 251 | 2002 | 0.014 | 1.102 | 79.903 | 0.142 | 1.494 | 24.175 |
| 254 | 2003 | 0.216 | 1.166 | 5.410 | -0.190 | 0.133 | 1.712 |
| 254 | 2004 | 0.048 | 1.610 | 33.266 | -1.554 | 13.748 | 2102.653 |
| 251 | 2005 | 0.141 | 1.080 | 7.682 | -0.442 | 0.405 | 9.890 |
| 250 | 2006 | 0.153 | 1.627 | 10.616 | -0.485 | 3.002 | 103.683 |
| 249 | 2007 | 0.155 | 1.544 | 9.954 | -0.210 | 1.455 | 23.786 |
| 246 | 2008 | -0.302 | 2.853 | -9.442 | -0.069 | 0.904 | 8.567 |
| 243 | 2009 | 0.244 | 2.186 | 8.950 | 1.313 | 10.922 | 1277.570 |
| 252 | 2010 | 0.064 | 1.006 | 15.782 | -0.254 | 0.650 | 7.149 |
| 247 | 2011 | -0.115 | 1.321 | -11.529 | 0.279 | 0.058 | 3.241 |
| 251 | 2012 | 0.091 | 0.919 | 10.082 | 0.088 | 0.623 | 4.389 |
| 250 | 2013 | 0.034 | 1.096 | 31.865 | -0.111 | 1.429 | 21.779 |
Table 4:-Runs test
| TIME PERIOD | TEST VALUE a | CASES<TEST VALUE | CASES>=TEST VALUE | TOTAL CASES | NUMBER OF RUNS | Z | P VALUE |
| 1993-2013 | 0.0402 | 2454 | 2591 | 5045 | 2299 | -6.274 | 0 |
| 1993-1995 | 0.03 | 339 | 336 | 675 | 270 | -5.277 | 0 |
| 1996-2000 | 0.052 | 521 | 504 | 1025 | 480 | -2.086 | 0.037 |
| 2000-2002 | -0.0905 | 306 | 339 | 645 | 289 | -2.659 | 0.008 |
| 2002-2007 | 0.1496 | 602 | 670 | 1272 | 593 | -2.373 | 0.018 |
| 2007-2009 | -0.2065 | 173 | 173 | 346 | 160 | -1.507 | 0.132 |
| 2009-2013 | 0.07 | 621 | 622 | 1243 | 599 | -1.334 | 0.182 |
Table 5:-T- Test
| PERIOD | T | DF | SIGNIFICANCE | MEAN DIFF | LOWER | UPPER |
| 1993-2013 | 1.746 | 5044 | 0.081 | 0.040 | -0.005 | 0.085 |
| 1993-1995 | 0.509 | 674 | 0.611 | 0.030 | -0.086 | 0.146 |
| 1996-2000 | 0.943 | 1024 | 0.346 | 0.052 | -0.056 | 0.160 |
| 2000-2002 | -1.333 | 644 | 0.183 | -0.091 | -0.224 | 0.043 |
| 2002-2007 | 3.819 | 1271 | 0.000 | 0.150 | 0.073 | 0.227 |
| 2007-2009 | -1.435 | 345 | 0.152 | -0.207 | -0.490 | 0.077 |
| 2009-2013 | 1.615 | 1242 | 0.107 | 0.063 | -0.014 | 0.140 |
Table 6:-Auto correlation test
| PERIOD | LAGS | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1993-2013 | AC | -0.434 | -0.077 | 0.003 | 0.024 | -0.003 | -0.046 | 0.023 | 0.009 | 0.003 | 0.024 |
| STD. ERROR | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | |
| Q Stat | 951.428 | 981.670 | 981.722 | 984.716 | 984.757 | 995.237 | 997.999 | 998.447 | 998.495 | 1001.000 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1993-1995 | AC | -0.276 | -0.224 | -0.031 | -0.007 | 0.064 | -0.024 | 0.032 | -0.053 | -0.017 | 0.098 |
| STD. ERROR | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | |
| Q Stat | 51.485 | 85.393 | 86.055 | 86.087 | 88.840 | 89.219 | 89.899 | 91.836 | 92.024 | 98.604 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1996-2000 | AC | -0.485 | -0.034 | 0.023 | 0.036 | -0.021 | -0.076 | 0.064 | -0.021 | 0.025 | 0.020 |
| STD. ERROR | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | |
| Q Stat | 242.010 | 243.201 | 243.724 | 245.087 | 245.529 | 251.549 | 255.763 | 256.199 | 256.844 | 257.273 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2000-2002 | AC | -0.419 | -0.066 | -0.053 | 0.063 | -0.014 | -0.026 | -0.005 | 0.017 | 0.045 | -0.025 |
| STD. ERROR | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | |
| Q Stat | 113.744 | 116.556 | 118.387 | 120.937 | 121.061 | 121.508 | 121.525 | 121.710 | 123.009 | 123.429 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2002-2007 | AC | -0.420 | -0.139 | 0.031 | 0.067 | -0.009 | -0.039 | -0.009 | -0.010 | 0.010 | 0.073 |
| STD. ERROR | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | |
| Q Stat | 224.359 | 248.858 | 250.069 | 255.753 | 255.853 | 257.822 | 257.924 | 258.065 | 258.181 | 265.090 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2007-2009 | AC | -0.445 | -0.055 | 0.060 | -0.104 | 0.037 | -0.036 | -0.016 | 0.152 | -0.082 | -0.032 |
| STD. ERROR | 0.054 | 0.054 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | |
| Q Stat | 68.893 | 69.946 | 71.190 | 74.952 | 75.440 | 75.907 | 75.993 | 84.186 | 86.575 | 86.939 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2009-2013 | AC | 0.455 | -0.037 | -0.025 | 0.037 | -0.021 | -0.035 | 0.046 | -0.033 | 0.047 | -0.004 |
| STD. ERROR | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | |
| Q Stat | 257.482 | 259.186 | 259.976 | 261.651 | 262.191 | 263.700 | 266.295 | 267.692 | 270.476 | 270.499 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Appendix B:- CNX Nifty NSE
Table 1:- Test for Normality
| Tests of Normality | |||
| Kolmogorov-Smirnova | |||
| Statistic | df | Sig. | |
| NSE | .054 | 5172 | .000 |
| a. Lilliefors Significance Correction | |||
Table 2:- Descriptive statistics of different periods
| N | TIME PERIOD | MEAN | STD. DEV. | COF. OF VAR. | SKEWNESS | KURTOSIS | JORQUE BERRA |
| 5070 | 1993-2013 | 0.040 | 1.624 | 40.323 | -0.129 | 6.097 | 7865.758 |
| 679 | 1993-1995 | 0.029 | 1.469 | 50.243 | 0.043 | 1.078 | 33.095 |
| 1047 | 1996-2000 | 0.054 | 1.761 | 32.488 | 0.161 | 3.207 | 453.170 |
| 645 | 2000-2002 | -0.080 | 1.606 | 19.995 | -0.353 | 2.565 | 190.204 |
| 1272 | 2002-2007 | 0.143 | 1.464 | 10.227 | -0.962 | 7.728 | 3361.353 |
| 346 | 2007-2009 | -0.193 | 2.619 | 13.534 | -0.345 | 1.785 | 52.800 |
| 1243 | 2009-2013 | 0.061 | 1.381 | 22.702 | 1.210 | 16.200 | 13895.013 |
Table 3:- Descriptive statistics of each year
| N | PERIOD | MEAN | STD. DEV. | COF. OF VAR. | SKEWNESS | KURTOSIS | JORQUE BERRA |
| 213 | 1993 | 0.158 | 1.744 | 11.046 | -0.347 | 0.365 | 5.457 |
| 230 | 1994 | 0.055 | 1.400 | 25.612 | 0.634 | 1.961 | 52.260 |
| 236 | 1995 | -0.112 | 1.242 | -11.130 | -0.106 | 0.752 | 5.996 |
| 250 | 1996 | -0.004 | 1.526 | -365.632 | 0.696 | 1.103 | 32.820 |
| 244 | 1997 | 0.075 | 1.798 | 24.010 | 0.058 | 7.564 | 581.841 |
| 250 | 1998 | -0.080 | 1.777 | -22.280 | -0.094 | 1.617 | 27.594 |
| 254 | 1999 | 0.203 | 1.837 | 9.056 | 0.045 | 2.247 | 53.510 |
| 250 | 2000 | -0.063 | 2.002 | -31.591 | -0.105 | 1.491 | 23.620 |
| 248 | 2001 | -0.071 | 1.630 | -22.900 | -0.462 | 2.262 | 61.672 |
| 251 | 2002 | 0.013 | 1.061 | 83.184 | 0.078 | 1.457 | 22.463 |
| 254 | 2003 | 0.213 | 1.232 | 5.777 | -0.337 | 0.470 | 7.134 |
| 254 | 2004 | 0.040 | 1.763 | 44.122 | -1.802 | 14.397 | 2331.090 |
| 251 | 2005 | 0.124 | 1.114 | 9.017 | -0.517 | 0.592 | 14.831 |
| 250 | 2006 | 0.134 | 1.650 | 12.305 | -0.620 | 2.731 | 93.720 |
| 249 | 2007 | 0.175 | 1.601 | 9.130 | -0.258 | 1.558 | 27.955 |
| 246 | 2008 | -0.297 | 2.808 | -9.467 | -0.283 | 1.688 | 32.505 |
| 243 | 2009 | 0.232 | 2.143 | 9.233 | 1.508 | 12.621 | 1704.950 |
| 252 | 2010 | 0.066 | 1.024 | 15.634 | -0.277 | 0.670 | 7.939 |
| 247 | 2011 | -0.114 | 1.321 | -11.548 | 0.270 | 0.057 | 3.045 |
| 251 | 2012 | 0.097 | 0.955 | 9.801 | 0.076 | 0.662 | 4.818 |
| 250 | 2013 | 0.026 | 1.138 | 43.529 | -0.116 | 1.514 | 24.447 |
Table 4:- Runs test
| TIME PERIOD | TEST VALUE a | CASES<TEST VALUE | CASES>=TEST VALUE | TOTAL CASES | NUMBER OF RUNS | Z | P VALUE |
| 1993-2013 | 0.0403 | 2483 | 2587 | 5070 | 2293 | -6.799 | 0 |
| 1993-1995 | 0.0293 | 346 | 332 | 678 | 261 | -6.064 | 0 |
| 1996-2000 | 0.0542 | 537 | 510 | 1047 | 469 | -3.413 | 0.001 |
| 2000-2002 | -0.0803 | 308 | 337 | 645 | 283 | -3.147 | 0.002 |
| 2002-2007 | 0.1432 | 593 | 679 | 1272 | 591 | -2.429 | 0.015 |
| 2007-2009 | -0.1935 | 164 | 182 | 346 | 168 | -0.597 | 0.55 |
| 2009-2013 | 0.05 | 621 | 622 | 1243 | 607 | -0.88 | 0.379 |
Table 5:- T-test
| PERIOD | T | DF | SIDNIFICANCE | MEAN DIFF | LOWER | UPPER |
| 1993-2013 | 1.766 | 5069 | 0.077 | 0.040 | -0.004 | 0.085 |
| 1993-1995 | 0.519 | 677 | 0.604 | 0.029 | -0.082 | 0.140 |
| 1996-2000 | 0.996 | 1046 | 0.319 | 0.054 | -0.053 | 0.161 |
| 2000-2002 | -1.27 | 644 | 0.204 | -0.080 | -0.205 | 0.044 |
| 2002-2007 | 3.487 | 1271 | 0.001 | 0.143 | 0.063 | 0.224 |
| 2007-2009 | -1.374 | 345 | 0.170 | -0.193 | -0.470 | 0.083 |
| 2009-2013 | 1.553 | 1242 | 0.121 | 0.061 | -0.016 | 0.138 |
Table 6:- Auto correlation test
| PERIOD | LAGS | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1993-2013 | AC | -0.433 | -0.084 | 0.009 | 0.018 | 0.013 | -0.053 | 0.024 | 0.005 | -0.004 | 0.042 |
| STD. ERROR | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | |
| Q Stat | 952.337 | 988.317 | 988.750 | 990.450 | 991.253 | 1005.383 | 1008.201 | 1008.328 | 1008.416 | 1017.488 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1993-1995 | AC | -0.242 | -0.268 | -0.004 | -0.006 | 0.018 | 0.003 | 0.057 | -0.099 | 0.019 | 0.100 |
| STD. ERROR | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | |
| Q Stat | 39.869 | 88.876 | 88.885 | 88.910 | 89.139 | 89.146 | 91.396 | 98.057 | 98.296 | 105.211 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1996-2000 | AC | -0.491 | -0.030 | 0.026 | 0.003 | 0.039 | -0.086 | 0.046 | -0.025 | 0.002 | 0.071 |
| STD. ERROR | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | |
| Q Stat | 253.300 | 254.300 | 254.990 | 255.000 | 256.600 | 264.460 | 266.640 | 267.300 | 267.300 | 272064.000 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2000-2002 | AC | -0.387 | -0.120 | -0.030 | 0.043 | 0.035 | -0.036 | -0.046 | 0.056 | 0.020 | -0.014 |
| STD. ERROR | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | |
| Q Stat | 96.830 | 106.900 | 107.510 | 108.710 | 109.510 | 110.360 | 111.770 | 113.820 | 114.080 | 114.220 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2002-2007 | AC | -0.406 | -0.150 | 0.037 | 0.069 | -0.017 | -0.028 | -0.012 | -0.011 | -0.002 | 0.089 |
| STD. ERROR | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | |
| Q Stat | 210.073 | 241.870 | 243.576 | 249.644 | 250.013 | 250.984 | 251.178 | 251.343 | 251.351 | 261.438 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2007-2009 | AC | -0.461 | -0.030 | 0.044 | -0.086 | 0.042 | -0.062 | 0.007 | 0.140 | -0.070 | -0.046 |
| STD. ERROR | 0.054 | 0.054 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | |
| Q Stat | 73.867 | 74.260 | 74.936 | 77.515 | 78.135 | 79.491 | 79.510 | 86.436 | 88.182 | 88.945 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2009-2013 | AC | -0.469 | -0.020 | -0.027 | 0.039 | -0.021 | -0.045 | 0.058 | -0.039 | 0.047 | 0.001 |
| STD. ERROR | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | |
| Q Stat | 273.528 | 274.037 | 274.950 | 276.821 | 277.361 | 279.904 | 284.134 | 286.079 | 288.879 | 288.880 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Appendix C:- Dow Jones
Table 1:- Test for Normality
| Tests of Normality | |||
| Kolmogorov-Smirnova | |||
| Statistic | df | Sig. | |
| DOW JONES | .080 | 5288 | .000 |
| a. Lilliefors Significance Correction | |||
Table 2:- Descriptive statistics of different periods
| N | TIME PERIOD | MEAN | STD. DEV. | CV | SKEW | KURT | JB |
| 5288 | 1993-2013 | 0.030 | 1.128 | 37.019 | -0.165 | 8.240 | 14985.608 |
| 756 | 1993-1995 | 0.058 | 0.598 | 10.370 | -0.361 | 1.758 | 113.743 |
| 1059 | 1996-2000 | 0.063 | 1.092 | 17.448 | -0.574 | 4.292 | 871.055 |
| 647 | 2000-2002 | -0.048 | 1.403 | -29.325 | -0.076 | 2.611 | 184.335 |
| 1274 | 2002-2007 | 0.051 | 0.835 | 16.411 | 0.216 | 2.717 | 401.955 |
| 355 | 2007-2009 | -0.170 | 2.269 | -13.318 | 0.262 | 3.526 | 187.931 |
| 1197 | 2009-2013 | 0.065 | 0.999 | 15.355 | -0.355 | 3.186 | 531.374 |
Table 3:- Descriptive statistics of each year
| N | YEAR | MEAN | STD. DEV. | CV | SKEW | KURT | JB |
| 252 | 1993 | 0.050 | 0.548 | 10.940 | -0.379 | 2.250 | 59.160 |
| 252 | 1994 | 0.008 | 0.688 | 81.942 | -0.331 | 1.324 | 22.993 |
| 252 | 1995 | 0.115 | 0.544 | 4.752 | -0.229 | 1.318 | 20.439 |
| 254 | 1996 | 0.091 | 0.755 | 8.298 | -0.593 | 1.776 | 48.264 |
| 253 | 1997 | 0.081 | 1.184 | 14.676 | -0.846 | 6.583 | 487.012 |
| 252 | 1998 | 0.059 | 1.256 | 21.203 | -0.548 | 4.274 | 204.394 |
| 252 | 1999 | 0.089 | 1.017 | 11.396 | 0.044 | -0.100 | 0.187 |
| 252 | 2000 | -0.025 | 1.309 | -51.813 | -0.283 | 1.738 | 35.093 |
| 248 | 2001 | -0.030 | 1.350 | -45.447 | -0.571 | 4.065 | 184.215 |
| 252 | 2002 | -0.073 | 1.604 | -22.023 | 0.494 | 1.234 | 26.229 |
| 252 | 2003 | 0.090 | 1.043 | 11.646 | 0.112 | 1.133 | 14.021 |
| 252 | 2004 | 0.012 | 0.683 | 55.536 | 0.010 | -0.115 | 0.143 |
| 252 | 2005 | -0.002 | 0.649 | -268.501 | -0.004 | 0.040 | 0.018 |
| 251 | 2006 | 0.060 | 0.622 | 10.338 | -0.110 | 1.239 | 16.550 |
| 251 | 2007 | 0.025 | 0.918 | 36.960 | -0.623 | 1.637 | 44.268 |
| 253 | 2008 | -0.163 | 2.381 | -14.583 | 0.227 | 3.822 | 156.163 |
| 252 | 2009 | 0.068 | 1.524 | 22.274 | 0.072 | 2.181 | 50.180 |
| 252 | 2010 | 0.041 | 1.018 | 24.541 | -0.177 | 2.162 | 50.415 |
| 252 | 2011 | 0.021 | 1.328 | 62.201 | -0.532 | 2.572 | 81.332 |
| 250 | 2012 | 0.028 | 0.743 | 26.513 | 0.027 | 0.916 | 8.774 |
| 252 | 2013 | 0.093 | 0.640 | 6.860 | -0.202 | 1.362 | 21.176 |
Table 4:- Runs test
| TIME PERIOD | TEST VALUE a | CASES<TEST VALUE | CASES>=TEST VALUE | TOTAL CASES | NUMBER OF RUNS | Z | P VALUE |
| 1993-2013 | 0.05 | 2644 | 2644 | 5288 | 2758 | 3.108 | 0.002 |
| 1993-1995 | 0.07 | 378 | 378 | 756 | 386 | 0.51 | 0.61 |
| 1996-2000 | 0.07 | 529 | 530 | 1059 | 520 | -0.646 | 0.519 |
| 2000-2002 | -0.05 | 323 | 324 | 647 | 332 | 0.59 | 0.555 |
| 2002-2007 | 0.06 | 637 | 637 | 1274 | 691 | 2.971 | 0.003 |
| 2007-2009 | -0.1 | 177 | 178 | 355 | 200 | 2.286 | 0.022 |
| 2009-2013 | 0.07 | 598 | 599 | 1197 | 619 | 1.128 | 0.259 |
Table 5:- T test
| PERIOD | T | DF | SIGNIFICANCE | MEAN DIFF | LOWER | UPPER |
| 1993-2013 | 1.964 | 5287 | 0.050 | 0.030 | 0.000 | 0.061 |
| 1993-1995 | 2.651 | 755 | 0.008 | 0.058 | 0.015 | 0.100 |
| 1996-2000 | 1.865 | 1058 | 0.062 | 0.063 | -0.003 | 0.128 |
| 2000-2002 | -0.867 | 646 | 0.386 | -0.048 | -0.156 | 0.060 |
| 2002-2007 | 2.175 | 1273 | 0.030 | 0.051 | 0.005 | 0.097 |
| 2007-2009 | -1.415 | 354 | 0.158 | -0.170 | -0.407 | 0.067 |
| 2009-2013 | 2.253 | 1196 | 0.024 | 0.065 | 0.008 | 0.122 |
Table 6:- AUTO CORRELATION TEST
| PERIOD | LAGS | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 1993-2013 | AC | -0.507 | -0.023 | 0.040 | 0.011 | -0.047 | 0.043 | -0.040 | 0.032 | -0.027 | 0.050 |
| STD. ERROR | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | |
| Q Stat | 1360.000 | 1360.000 | 1370.000 | 1370.000 | 1380.000 | 1390.000 | 1400.000 | 1410.000 | 1410.000 | 1420.000 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1993-1995 | AC | -0.485 | 0.022 | -0.039 | -0.017 | 0.028 | -0.019 | 0.015 | 0.004 | -0.024 | 0.020 |
| STD. ERROR | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | |
| Q Stat | 178.266 | 178.632 | 179.794 | 180.013 | 180.605 | 180.877 | 181.057 | 181.067 | 181.520 | 181.830 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 1996-2000 | AC | -0.473 | -0.014 | -0.047 | 0.052 | -0.033 | 0.037 | -0.037 | -0.008 | 0.000 | 0.107 |
| STD. ERROR | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | |
| Q Stat | 237.207 | 237.417 | 239.815 | 242.693 | 243.885 | 245.330 | 246.798 | 246.868 | 246.868 | 259.059 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2000-2002 | AC | -0.450 | -0.114 | 0.078 | 0.016 | -0.042 | -0.010 | 0.028 | -0.010 | 0.024 | 0.011 |
| STD. ERROR | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | |
| Q Stat | 131.445 | 139.832 | 143.842 | 144.003 | 145.165 | 145.232 | 145.747 | 145.812 | 146.186 | 146.261 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2002-2007 | AC | -0.569 | 0.099 | -0.062 | 0.092 | -0.097 | 0.088 | -0.098 | 0.082 | -0.062 | 0.045 |
| STD. ERROR | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | |
| Q Stat | 413.741 | 426.202 | 431.061 | 441.977 | 453.922 | 463.832 | 476.148 | 484.783 | 489.649 | 492.197 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2007-2009 | AC | -0.502 | -0.126 | 0.218 | -0.091 | -0.042 | 0.086 | -0.095 | 0.087 | -0.054 | 0.052 |
| STD. ERROR | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.053 | 0.052 | 0.052 | 0.052 | 0.052 | |
| Q Stat | 89.809 | 95.481 | 112.595 | 115.599 | 116.246 | 118.909 | 122.183 | 124.910 | 125.960 | 126.956 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
| 2009-2013 | AC | -0.558 | 0.123 | -0.124 | 0.118 | -0.096 | 0.030 | 0.018 | -0.019 | -0.009 | 0.019 |
| STD. ERROR | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | 0.029 | |
| Q Stat | 373.439 | 391.718 | 410.075 | 426.899 | 437.939 | 439.028 | 439.399 | 439.833 | 439.933 | 440.386 | |
| Prob | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
