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Global financial crisis and dynamics of Indian stock market.

Introduction

Financial system is an integral part of any economy in the modern world which is beneficial for the transfer of funds from surplus entities--savers to deficient entities-investors. Equity market is one of the important ingredients of this system. Vast evidence is available in literature about positive impact of stock market developments on economic growth (see Levine and Zervos (1996), Levine (2002), Nieuwerburgh et al., (2006), Enisan and Olufisayo (2009)).

The stock market indices are one of the principal indicators of the economic activities. The movement of stock market indices presents the future economic outlook. A falling stock index reflects the dampening of the investment climate while a rising stock index indicates more confidence and soundness of the economy. The latter attracts more investment demand on stocks. Rising investment on stocks raises stock prices and generates profits. When crisis affects the real activities, it affects the stock market, as profit expectation on financial investments would be lower. If financial investment would be affected, its impact would be felt on the real investment, as real investment would not increase. Once the real sector activity lessens, that would affect the entire economy, Thus, it is mainly the expectation of the investors mainly works affecting both the financial and real investment in the economy.

These markets are very much sensitive to national and international events and react immediately. The financial turmoil that originated from the US housing market caused the market values of listed firms to plummet in stock markets all over the world. The stock market activity is one of the principal activities in the corporate world among the chain of activities, which got affected due to the financial crisis. The objective of this paper is to measure the impact of the global financial crisis on the dynamics of the Indian Stock market.

Global Financial Crisis: Theoretical Backdrop

The current global financial crisis is rooted in the subprime crisis which surfaced in the United States of America. During the boom years, mortgage brokers attracted by the big commissions, encouraged buyers with poor credit to accept housing mortgages with little or no down payment and without credit checks. A combination of low interest rates and large inflow of foreign funds during the booming years helped the banks to create easy credit conditions for many years. Banks lent money on the assumption that housing prices would continue to rise. Also the real estate bubble encouraged the demand for houses as financial assets. Banks and financial institutions later repackaged these debts with other high-risk debts and sold them to world- wide investors creating financial instruments called CDOs or Collateralized Debt Obligations (Sadhu2008). In this way risk was passed on multifold through derivatives trade (Prasad and Reddy (2009)). Due to surplus inventory of houses and increase in interest rates, there was decline in housing prices in the year 2006-2007 that resulted into an increased defaults and foreclosure activities that collapsed the housing activity (Sengupta (2008)). The impact of this meltdown was felt globally because CDOs were sold worldwide and situation became more depriciative because some Wall Street banks had borrowed 40 times more than they were worth (Occasional Paper Series, Rajya Sabha Secretariat, NewDelhi, June, 2009). The banks and investment firms that had bought billion of dollars worth of securities based on mortgages were in trouble now. This turmoil started in mid 2007 exacerbated substantially since August 2008 (Mohan, 2009). But when Lehman Brothers and other important financial institutions failed in September 2008, the impact could be seen clearly on the global economy (Satish and Nayia (2012))

In nutshell, the global financial crisis originated in the financial sector of the advanced economies, beginning with sup-prime mortgage problem and the meltdown of mortgage backed securities in the US accelerated with the collapse of banking institutions such as Fortis in Europe, Merril Lynch, Lehman Brothers, Fannie Mac, Freddie Mac and Washington Mutual in US and quickly spreading to affect financial institutions in Europe, has its roots in a combination of factors. (Queensly et al., (2012)).

Impact on India

The impact of the global crisis has been transmitted to the Indian economy through three distinct channels, namely: the financial sector, exports and exchange rates. The other significant channel of impact is the fall in business and consumer confidence leading to decrease in investment and consumer demand (Rajiv (2011)).

The most immediate effect of the crisis has been an outflow of foreign institutional investment from the equity market. There is a serious concern about the likely impact on the economy because of the heavy foreign exchange outflows in the wake of sustained selling by Foreign Institutional Investors in the stock markets and withdrawal of funds by others. The crisis resulted in net outflow of $ 10.1billion from the equity and debt markets in India till October 22, 2008 (Kundu 2008). There is even the prospect of emergence of deficit in the balance of payments in the near future (Prasad and Reddy (2009).

In addition, massive selling by Foreign Institutional Investors and conversion of their holdings from rupees to dollars for repatriation has resulted in the rupee depreciating sharply against the dollar. Between January 1 and November 16, 2008, the Reserve Bank of India (RBI) reference rate for the rupee plunged from Rs.39.20 per dollar to Rs.50.18. This crisis has sharply contract the demand for exports adversely affecting the country's growth prospects. In Capital market new investment through public issues were on hold.

Impact on Indian Stock Market

In India, the stock market has undergone significant transformations with the liberalization measures. The Bombay Stock Exchange (BSE) of India has emerged as one of the largest stock exchange in the world in terms of number of listed companies, comprising many large, medium-sized and small firms. The inflow of foreign capital has made a crucial contribution to the growth of the stock market. The financial turmoil affected the stock markets even in India. Mostly all the industrial sectors experienced a consistent low in their stock prices. The IT sector has been badly hit. Nearly half of the IT sector firms' revenues come from banking and financial institutions. With the effect of financial crises, IT companies are not able to enhance their business with these investment banks, and, in turn, started retrenching their employees. The combination of a rapid sell off by financial institutions and the prospect of economic slowdown have pulled down the stocks and commodities market. Foreign institutional investors pulled out close to $ 11 billion from India, dragging the capital market down with it (Lakshman 2008). Stock prices have fallen by 60per cent. India's stock market index--Sensex-touched above 21,000 marks in the month of January, 2008 and has plunged below 10,000 during October 2008. Hence, a current study is made to examine the effects of global financial crisis and present scenario of Indian Stock Market aftermath of crisis.

Dynamics of Indian Stock Market

The emergence of informational efficient financial markets is an important facet of any country's economic modernization, with far-reaching implication for its macroeconomic stability and performance. Thus, it is in the interest of the economy to achieve efficiency in the dynamics of the stock markets. Return is the greatest factor that induces the investors to invest money in stock market. Return means the profit earned as a result of rise in share prices. Return helps the investor to compare the benefits available in the alternative investment avenue. Descriptive statistics are used to analyse the return of the various indices.

Volatility refers to the amount of uncertainty or risk with regard to changes in a security's value. A higher volatility means that a security's value can potentially be spread over a larger range of values. This means that the price of the security can change radically over a period of time--in either direction. A lower volatility means that a security's value does not fluctuate severely, but changes in value at a steady pace over a period of time. High volatility is likely to occur at times of market stress caused by major economic and political events, record crude oil prices, and military conflicts. On the other hand, low volatility, which generally occurs in quiet markets, can potentially offer better prices for buyers.

In a stock market, return and volatility are two prime indicators of trading activity, jointly determined by the same market dynamics and may contain valuable information about a security. Based on this, the current paper is an attempt to study the joint dynamics of stock prices and volatility to improve the understanding of the microstructure of Indian stock market. The current study further contributes to the literature because it examines the impact of global financial crisis on the Indian stock market. The remainder of the paper is as follows. Section II reviews the literature. In section III, the methodology and data employed are presented. In section IV, the key results from the empirical investigation are reported and in section V conclusions are drawn.

II. Review of Literature

Basically, the U.S. Subprime credit crisis started with huge defaults by Subprime borrowers in the mortgage markets. The credit crisis led to massive losses or even bankruptcy among financial institutions and companies which hold large portfolio with mortgage-backed securities. This is consistent with the evidence provided by Longstaff (2010) of the contagion across markets from the credit crisis. Therefore, researchers gain interest to study about the U.S. Subprime credit crisis in their stock market analysis. Besides, various researches were conducted to study on stock market with large shocks due to global event or crisis such as the crash of 1987 (Choudhry 1996, Law 2006), the Asian crisis (Chakrabarti and Roll 2002, Holden et al 2005, Law 2006, Leeves 2007 and Karunanayake et al 2010), the September 11th terrorist attacks (Charles and Darne 2006 and Nikkinen et al 2008).

A great body of literature have analysed the impact of US financial crisis (see Shekar Gopal (2008), Sam Gian (2008), S. Venkitaramanan (2008), Manohar M. Atreya (2008), John B. Taylor (2008), Carmen M. Reinhart (2008a), Atif R. Mian and Amir Sufi (2008), Carmen M. Reinhart (2008 b), Naude, W. A. (2009a), Stephany Griffith-Jones and Jose Antonio Ocampo (2009), Chong , (2011). Some researchers Morris (2008), Eichengreen et al. (2009) and Taylor (2009) have focused on the causes of the US financial crisis. The literature concentrated mostly on well--developed equity markets in the US and Europe, and do not pay much attention to other stock markets. From that respect, this article intends to study about the behavior of Indian stock market in term of the stock return and volatility with the onset of global financial crisis.

III. Data Base and Research Methodology

The current study empirically examines the following null hypothesis (H0): "Global financial crisis had no impact on the Indian stock market". Our alternative hypothesis (H1): "Global financial crisis had a negative impact on the Indian stock market". In order to test this hypothesis, time series data on Bombay Stock Exchange (BSE) Sensex has been used for analysis. Sensex a basket of 30 constituent stocks representing a sample of large, liquid and representative companies and it is regarded the pulse of the Indian stock market. The daily closing prices of the Sensex for the period January 2007 to December 2010 have been obtained from www.bseindia.com.

The full period of study is then divided into 3 different periods to allow behavior of stock market returns and volatility to be investigated in each sub-period. To know the pre-financial crisis effect, the period is taken from January 2007 to December 2007 and the post-financial crisis effect the period is taken from April 2009 to December 2010 and to know the impact of the financial crisis on the market return the period is taken only from January, 2008 to March 2009. Movements in BSE index (Sensex) has been used as the guiding factor in dividing the study period into sub periods as used in Satish and Nayia (2012). Thus the impact of the meltdown on the return and volatility of Indian stock market has been examined in the present study by comparing the empirical results for these three periods.

Methodology

Two main latent variables for this study are the stock market return and volatility. The daily stock returns are continuous rates of return, computed as log of ratio of present day's price to previous day's price (i.e. Rt = ln ([P.sub.t]/[P.sub.t-1]). Data are obtained from website of BSE (www.bseindia.com).

When time-series data is used in econometrical analysis, several preliminary statistical steps must be undertaken. These steps include descriptive statistics and unit root testing. Given the nature of time-series data, it is necessary to test the stationarity of each individual series. A stochastic process is said to be stationary, if its mean and variance are constant over time and the value of covariance between two time periods depends only on the distance or lag between the two time periods and not on the actual time at which covariance is computed. One way to tests for the existence of unit roots, and to determine the degree of differencing necessary to induce stationarity, is to apply the Augmented Dickey Fuller (ADF) tests. It consists of regressing the first difference of the series against a constant; the series lagged one period, the differenced series at n lag lengths and a time trend (Pindyck and Rubinfeld (1998)). The model used is as follows:

[DELTA][r.sub.t] = [alpha] + [n.summation over (i=1)] [beta]i [r.sub.t-1] + [[lambda].sub.t] + [pr.sub.t-1] + [[epsilon].sub.t] (A)

Where, t is the trend variable, taking values of 1,2 and so on. [Pr.sub.t-1] is the one period lagged value of the variable r. If the coefficient of p is significantly different from zero, then the hypothesis that r is non-stationary is rejected. The ADF test can be carried out with and without the constant and / or trend. One has also to choose the appropriate lag length. If a series is found to be non-stationary in level, one should difference the series until the stationarity is established. The results of ADF test determine the form in which the data should be used in any subsequent econometric analysis. Unit root test is done with E-views Software and results are discussed in table II.

It is now a well-known fact that financial return series exhibit strong conditional time varying volatility, volatility clustering, and volatility persistence. Researchers have introduced various models to explain and predict these patterns in volatility. The most successful empirical workhorse for modeling this characteristic of financial time series is Engle's (1982) Autoregressive Conditional Heteroscedasticity (ARCH) model and its extension, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of Bollerslev (1986).Therefore, this study also used this test with E-views Software while analyzing the impact of global financial crisis on stock return as well as volatility of Indian stock market.

Based on above logic, the study employs GARCH (1,1) as benchmark model to measure the persistence of return volatility. The specifications of GARCH model are presented below:

The first equation (eq 1), specify the conditional mean equation of GARCH (1,1) model.

[R.sub.t] = [alpha] + [beta] ['X.sub.t] + [[epsilon].sub.t] (eq.1)

[h.sub.t] = [omega] + [m.summation over (i=1)] [[alpha].sub.i] [s.sup.2.sub.t-i] + [n.summation over (j=1)] [[beta].sub.j] [h.sub.t-j] + [[epsilon].sub.t] (eq.2)

Equation 1 contains a regressand Rt, in current study, it is stock return, which depends on [alpha] stands for drift term, [X.sub.t] is / are exogenous variable(s) and [beta] is/are coefficient(s) of respective exogenous variable(s). Like other econometric models, [espilon]t is error term and subscription'^ is denoted for time series data. This equation is generally called conditional mean equation and is the foremost step for empirical analysis.

Equation 2 is called conditional variance equation, where ht is the conditional volatility, [alpha]i is the coefficient of ARCH term with order i to m and [beta]j is the coefficient of GARCH term with order j to n. The conditional volatility as defined in equation (2) is determined by three effects namely the intercept term given by [omega], the ARCH term expressed by [[alpha].sub.i][epsilon][2.sub.t-i] and the forecasted volatility from the previous period called GARCH component expressed by [beta]jht-j. Parameters [omega] and [alpha] should be higher than 0 and [beta] should be positive in order to ensure conditional variance ht to be non-negative. Besides this, it is necessary that [[alpha].sub.i]+[[beta].sub.j][less than or equal to]1, which secures covariance stationarity of conditional variance. A straightforward interpretation of the estimated coefficients in equation (2) is that the constant term is the long-term average volatility whereas [[alpha].sub.i] and [[beta].sub.j] represents how volatility is affected by current and past information respectively. Moreover, the size (magnitude) of parameters [[alpha].sub.i] and [[beta].sub.j] determines the short run dynamics of the resulting volatility time series. Large [[beta].sub.j] shows that information shocks to conditional variance take longer time to die out, thus, volatility persists for longer time periods. Large GARCH error coefficient indicates that volatility reacts quite intensely to market movements.

One of the beauties of GARCH type models is that explanatory or dummy variables can be added in conditional mean and variance equations according to the objective of the study. In order to ascertain the impact of financial crisis on the Indian stock market volatility we have run a GARCH (1,1) estimation using dummy variable. Dummy variable (Dt) takes a value of 1 for the daily returns of January 2008 to March 2009 defined as crisis period otherwise 0. If the coefficient of the dummy is statistically significant then the financial crisis has an impact on the stock market volatility. A significant positive co-efficient would indicate an increase in volatility, a significant negative co-efficient would indicate a decrease in volatility.

Next, the mean equation needs to be solved with ARMA representation, therefore, this study also focused on ARMA specification with the help of correlogram of return series for full period shown in figure.1. The return series is found to fit AR(1)(first-order autoregressive) process with the minimum AIC value compared to other ARMA process. In addition to that, other diagnostic tests are also considered to finalize the model for empirical analysis. Finally, the modified mean and variance equation depicting the influence of global financial crisis are as follows:

[R.sub.t] = [alpha] + [[beta].sub.1] [R.sub.t-1] + [Y.sub.t][D.sub.t]+[[epsilon].sub.t] (eq.3)

[h.sub.t] = [omega] + [m.summation over (i=1)] [a.sub.i] [epsilon][2.sub.t-i] + [n.summation over (j=1)] [b.sub.j] [h.sup.t-j] + [lambda][D.sub.t] + [e.sub.t] (eq.4)

Conditional mean equation 3 contains ARMA specification based explanatory variables along with a dummy variable (Dt) of CRISIS to ascertain the effect of financial crisis on the stock returns. In addition to that, to find the impact of crisis on volatility of the stock exchanges of India, the same dummy variable is included into conditional variance equation 4.

IV Empirical Results

This paper begins the empirical analysis by first investigating the descriptive statistics of return series. Table I provides the sample descriptive statistics, which provides important information regarding the behavior of Indian stock market return over the different sub-periods.

The daily mean returns for the whole period as well as the sub periods are almost zero, which goes well with the theory that market returns in the presence of large number of rational profit maximizers should be equally distributed among buyers and sellers (see Tsay (2005)). However, mean returns are comparatively more in precrisis period than during the crisis, even higher in the post crisis period. Moreover, stock market facing negative returns during the crisis period. Thus, daily returns in the Indian stock market declined sharply because of the subprime mortgage crisis in the U.S which suggests that financial meltdown did impact the Indian stock market returns. The standard deviation in returns, which is indicative of the unconditional variance is highest in crisis period and thus suggesting more volatility in the market during this period.

Further, the presence of skewness and excess kurtosis in the whole as well as in the sub-periods provide the evidence of the nature of departure from normality. However, negatively skewed return in pre-crisis and during the crisis period depicts that abnormally low return days occurred more frequently than abnormally high return days. In all the sub-periods, the coefficients of skewness and kurtosis are significantly different from 0 and 3 respectively which has led the return series to be characterized with asymmetric and non-normal. Risk averse nature of the traders in the market may be prominent cause for the asymmetric returns. It can also be verified from p value of Jarque-Bera test (highly significant) which confirms that the return series is not normally distributed. To have further understanding about market returns, graph of the return series (based on correllogram) is presented at Figure1. This graph also indictate that non- normality exists in the series as confirmed from statistical analysis and inertia of volatility clustering also prevails in the markets.

Also, the presence of unit root has been checked by using the Augmented Dickey Fuller (ADF) test and if the presence of unit root is confirmed then detrending or differencing of the series is required to make the data stationary. Results of ADF test are reported in table II. It is found that all 4 series of returns are stationary. In other words, the return series have no unit roots. Hence, the level form of returns will be used for further estimation throughout the analysis.

To meet the objectives of this study, GARCH model is used which is based on conditional mean and variance equations. The results of GRACH (1,1) model along with dummy variable for financial crisis is presented in table III. The results of mean equation of GARCH model confirms that past value of stock return predict the current stock return as the coefficient of AR(1) is significant. Coefficient of CRISIS (dummy) is significant and contains negative sign but its effect is negligible which only 0.3 percent is. It means there is very thin negative impact of global financial crisis on the stock return in India.

As far as conditional variance equation is concerned, the study finds parameters [alpha]i (ARCH) and [beta]j (GARCH) to be positive and significant in table III. It indicates that conditional variance is predominantly affected by lagged variance (volatility clustering), which implies that previous information shock significantly affects current returns. Volatility clustering suggests that movement in price variance once initiated tends to persist over the period and it steadily declines. Large [[beta].sub.j] shows that shocks to conditional variance take a long time to die out, thus volatility is persistent. The last coefficient of this model is concerned with recent global financial crisis which is also significant and has positive impact. This evidence confirms that recent financial crisis positively hit the volatility of stock return. The above discussion clearly suggests increased volatility of the Indian stock-return series during the crisis period. It might be due to the loss of confidence of domestic investors in the market because of continuous withdrawal of foreign institutional investments, particularly by U.S. institutional investors (Satish and Mahajan, 2012).

The results of some diagnostic tests are mentioned in the table IV. These results show that this model is free from autocorrelation as Q-Stat values up to 36 lags are insignificant. Moreover, the insignificant ARCH-LM test statistic the table shows that standardized residuals did not exhibit additional ARCH effect; hence signifying that variance equations are well specified.

Moreover, this study also attempts to estimate GARCH model separately for all the sub-periods (mentioned in previous section) to see the change in the value of coefficients and their implications on the stock return volatility and results are given in table V In table V, ARCH coefficient measuring volatility clustering is found to be significant in all the periods but value of ARCH coefficient is high in crisis period and which depicts the impact of global financial crisis on volatility. It reflects a lot of uncertainty in the market due to this financial crisis.

Further, GARCH coefficient which measures the impact of old news is also found to be significant in all the periods (see table V). However, value of coefficient is more in post-crisis period as compared to pre-crisis period suggesting the old news (global financial crisis) has impact on current volatility. In other words, shocks to conditional variance take longer time to die out, thus volatility is persistent and large GARCH error coefficient indicates that volatility reacts quite intensely to market movements. These findings are further supported by results of persistence measure, sum of ARCH and GRACH given in last column of table V In all the sub-periods, persistence measure is very close to unity suggested that persistence of shocks to volatility take long time times to decay.

V: Summary and Conclusion

As global events and stock market crash or crisis are an important element that can have great impact on the stock market, hence it is worthwhile for an extend analysis of stock markets during Subprime credit crisis. This crisis occurred in United States and later on spread in thunder storm way all over the world. Besides developed countries, developing countries also came into the grip of this devastating incidence which not only created fluctuations in financial markets but also made hampering impacts on real economies of developing countries. In this paper, an attempt has been made to study the impact of Global Financial Crisis on Indian stock market return and volatility. For this, the study has used the daily closing prices of the Sensex from the period January 2007 to December 2010. The current study hypothesized that the Indian stock market return and volatility would not be adversely affected by the financial crisis.

The descriptive statistics results indicate that the Indian stock market was affected by the global financial crisis. A study of this type would enable the investment community to have a clear knowledge about the financial crisis. The study further examined the effects of financial crisis on the market volatility using a GARCH model that captures the heteroskedasticity in returns that characterize stock market returns. The significant coefficient of dummy variable used in model clearly indicates that financial crisis has had significant impact market volatility. At the same time, there is very thin negative impact of global financial crisis on the stock return in India. Next, present study estimated the model separately for the pre and post financial crisis period and find that the ARCH and GARCH coefficients are significant in all the cases. However, impact of recent news (ARCH) on volatility is more in crisis period and impact of old news (GARCH) is more in post-crisis period. In other words, persistence of shocks to volatility takes long time times to die out, thus volatility is persistent.

In nutshell, the news about bankruptcy of Lehman Brothers following the Subprime crisis turned out to have exacerbated impact on stock market volatility but not on the stock returns in general. It may be due to the fact that capital market watchdog was monitoring the situation very closely. The clearing system has proved its ability to deal with stock market fluctuations. Financial stability in India has been achieved through perseverance of prudential policies which prevent in situations from excessive risk taking and financial markets from becoming extremely volatile and turbulent (Muthukumaran, T et al., 2011). Moreover, great savings habit among people, strong fundamentals, strong conservative and regulatory regime have also been contribute in saving Indian economy from going out of gear.

However, this meltdown in 2008 has raised some doubts about the adequacy and efficacy of present international financial system to manage the global crisis. Thus, present or existing mechanisms to manage the financial system in developing as well as developed countries need a strong vigilance of financial and monetary authorities of these countries.

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Sarika Mahajan

Assistant Professor,

Lala Lajpat Rai Institute of

Management, Mumbai.

Table - I

Descriptive Statistics of Return Series

               Full Period    Pre-crisis

Mean             0.000390      0.001512
Median           0.001064      0.001380
Maximum          0.159900      0.048690
Minimum         -0.116044      -0.048328
Std. Dev.        0.020215      0.015461
Skewness         0.188056      -0.202127
Kurtosis         9.080018      4.395277
Jarque-Bera     1529.162 *    21.80559 *
Probability      0.000000      0.000018
Observations       989            248

                  Crisis      Post-crisis

Mean            -0.002426       0.001674
Median          -0.002244       0.001342
Maximum          0.079005       0.159900
Minimum         -0.116044      -0.060084
Std. Dev.        0.027937       0.015509
Skewness        -0.127350       2.445025
Kurtosis         3.772216       27.89976
Jarque-Bera    8.375073 **     11670.88 *
Probability      0.015184       0.000000
Observations       304            435

Note: *, **, denote for significant at 1%, 5%, level respectively.

Table - II

Unit Root Test of Stock Return Series

               ADF Statistic    Probability

Full period     -29.35401 *       0.0000
Pre-crisis      -14.68992 *       0.0000
Crisis          -16.27039 *       0.0000
Post-crisis     -20.21583 *       0.0000

Note: indicates significant at 1% level.

Table - III

Results of GARCH Model with Dummy

GARCH = C(4) + C(5) *RESID(-1)^2 + C(6)*GARCH(-1) + C(7) *DUMMY_C

Variable        Coefficient    Std. Error    z-Statistic    Prob.

C               0.001472 *      0.000477      3.087012     0.0020
DUMMY_C        -0.003019 **     0.001570      -1.923022    0.0445
AR(1)          0.059875 ***     0.036212      1.653455     0.0982

Variance Equation

C               4.84E-06 *      1.20E-06      4.033140     0.0001
RESID(-1)^2     0.111885 *      0.015463      7.235873     0.0000
GARCH(-1)       0.870036 *      0.015536      56.00018     0.0000
DUMMY_C         1.73E-05 *      6.10E-06      2.833910     0.0046

Note: *, **, *** denote for significant at 1%, 5%, 10%
level respectively.

Table - IV
Heteroskedasticity Tests

1. ARCH LM Test
F-statistic          0.920432      Prob. F(10,968)   0.5134

Obs*R-squared        9.221232           Prob.        0.5112
                                   Chi-Square(10)

2. Q-stat (up     Insignificants
to 36 lags)

Table - V

Results of GARCH (1,1) model estimation over different sub-periods

                         Mean Equation         Variance Equation

Variable                    Constant            ARCH ([alpha])

                     Coefficient    Prob.    Coefficient    Prob.

Full Period           0.001138 *    0.0068    0.121798 *    0.0000
Pre-Crisis Period    0.001709 ***   0.0702    0.088575 *    0.0014
Crisis Period         -0.001514     0.2749    0.135830 *    0.0048
Post Crisis Period   0.001277 **    0.0194    0.078034 *    0.0000

                       Variance Equation

Variable                  GARCH ([beta])       Volatility
                                               Persistence
                                             (ARCH + GARCH)
                                            ([alpha]+[beta])
                     Coefficient   Prob.

Full Period          0.878126 *    0.0000         0.99
Pre-Crisis Period    0.873787 *    0.0000         0.95
Crisis Period        0.814484 *    0.0000         0.94
Post Crisis Period   0.910927 *    0.0000         0.98

Note: *, **, *** denote for significant at 1%, 5%, 10%
level repectively.
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Author:Mahajan, Sarika
Publication:Abhigyan
Article Type:Statistical data
Geographic Code:9INDI
Date:Jan 1, 2014
Words:6003
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