"F-tests of zero restrictions:All lags of Opening_PriceF(6, 1744) = 1.7144 [0.1138]All lags of Closing_Price F(6, 1744) = 338.29 [0.0000]All vars, lag 6 F(2, 1744) = 1.3187 [0.2677]Equation 2: Closing_PriceCoefficient Std. Error t-ratio p-value const 2.38211 1.84584 1.2905 0.19704 Opening_Price_1 -0.0370019 0.0307916 -1.2017 0.22965 Opening_Price_2 -0.0746812 0.0308817 -2.4183 0.01570 **0.0578989 0.0308877 1.8745 0.06103 *Opening_Price_3Opening_Price_4 -0.0519313 0.0309074 -1.6802 0.09309 *Opening_Price_5 -0.00419939 0.0309182 -0.1358 0.89198 0.00884045 0.022323 0.3960 0.69214 Opening_Price_6Closing_Price_1 0.803944 0.0272814 29.4686 <0.00001 ***Closing_Price_2 0.11219 0.0398701 2.8139 0.00495 ***-0.020883 0.0399442 -0.5228 0.60118 Closing_Price_3Closing_Price_4 -0.0125953 0.0399248 -0.3155 0.75244 Closing_Price_5 0.00991507 0.0398946 0.2485 0.80375 Closing_Price_6 0.0219887 0.0390103 0.5637 0.57306 0.0577651 0.0173004 3.3390 0.00086 ***HighLow 0.125603 0.0199722 6.2889 <0.00001 ***Volume 1.3279e-06 1.3934e-07 9.5299 <0.00001 ***Mean dependent var968.5670S.D. dependent var312.3311Sum squared resid950939.7S.E. of regression23.35088 0.994458 0.994410R-squared Adjusted R-squaredF(15, 1744)20863.40P-value(F)0.000000rho -0.036230Durbin-Watson2.072374F-tests of zero restrictions:All lags of Opening_Price F (6, 1744) = 2.3709 [0.0276]All lags of Closing_Price F(6, 1744) = 160.85 [0.0000]All vars, lag 6 F(2, 1744) =0.631 [0.5322]56 For the system as a wholeNull hypothesis: the longest lag is 5Alternative hypothesis: the longest lag is 6Likelihood ratio test: Chi-square (4) = 5.97149Findings Given the fact that VAR shows association of the return series of thestock indices along with the different opening, closing and specific highs and lows ofthe market over a span of 6 years. Variance decomposition technique has beenincorporated to measure the shock absorption ability of the market due to someunforeseen occurrence or cause. Variance decomposition of the return seriesattempt to capture the magnitude of impact that occurs due to other variables.Once a shock is introduced by the error term, variance decomposition measures thecontribution of each variable.Considering a six period variance LSE can cause avery insignificant impact around 0.5% to 0.8% variation to other factors (likeopening and closing price of the stock and respective highs and lows of themarkets) over a period of time. But variance decomposition of opening price showsthat it is responsible for 5.04% variation of return series of LSE. Also Variancedecomposition of closing price has shown as high as 13.79% variation with LSEindices. This can be treated as a proxy measure of direction of causality ofassociation. Thus During the recessionary period (2007– 2009) major stockmarkets outside UK (Nikkei, Dow Jones, Hanseng, STI and BSE) exerts significantpressure on LSE.57 Fig no-10 Exponential smoothing plot of LSE since June 2006 to Nov 2012(a comparison between original and smoothed value)(Source: Analysed by researcher through E-Views 8.0)Findings Ref to the fig no-10 exponential smoothing techniques is used by theResearcher to assess the trend of LSE. Blue colour graph indicates the forecast ofthe market against (the red colour) the original value. The market was absolutelyvolatile during the time frame between mid of 2005 to end of 2007. After thatmarket has absorbed the shock. Simultaneously the value of alpha (the parameterscontrolling the internal factors within the organisation is assumed to be 0.4 ( beta58 =0.6).The overall smoothed value graph has inadequatelyoverlapped on theoriginal value.Fig no-11 Market capitalization -Original volume with the forecast trend (Source: Analysed by researcher through E-Views 8.0 output file)Table no-8Summary Statistics, using the observations 1 – 2221 company’s market cap between Oct, 2011 to Nov2012(Missing values were skipped)Variable Mean Median Minimum Maximum59 Nov12_Value__ 4.12552e+007 469999. 0.00500000 3.17813e+009Oct_12_Value__ 4.39804e+007 461304. 0.211650 3.73507e+009Sep_12_Value__ 4.35551e+007 597199. 4.00000 4.13661e+009Aug_12Value__ 4.81989e+007 640060. 0.0316500 4.39619e+009July_12Value__ 4.05407e+007 503655. 0.818400 3.99819e+009June_12Value__ 4.46718e+007 369979. 0.291100 3.94969e+009May_12Value__ 3.91505e+007 367317. 0.650650 3.29381e+009April_12Value__ 3.51689e+007 348193. 0.612500 3.23668e+009Mar_12Value__ 4.58034e+007 498267. 0.108250 4.32114e+009Feb_12Value__ 4.70376e+007 548711. 1.98000 3.46792e+009Jan_12Value__ 3.13529e+007 314105. 0.626100 2.99100e+009Dec_11Value__ 4.16029e+007 360513. 0.00930000 4.16500e+009Nov_11Value__ 4.16029e+007 360513. 0.00930000 4.16500e+009Oct_11Value__ 4.06895e+007 346874. 0.000300000 4.17645e+009Variable Std. Dev. C.V. Skewness Ex. kurtosisNov12_Value__ 2.12358e+008 5.14742 8.67269 87.6353Oct_12_Value__ 2.31857e+008 5.27182 9.51147 109.283Sep_12_Value__ 2.37595e+008 5.45503 9.79925 115.776Aug_12Value__ 2.47975e+008 5.14482 9.71933 118.775July_12Value__ 2.30938e+008 5.69646 10.1717 123.594June_12Value__ 2.48659e+008 5.56635 9.52298 106.093May_12Value__ 2.10459e+008 5.37563 9.48532 108.312April_12Value__ 2.02152e+008 5.74804 10.0028 117.476Mar_12Value__ 2.50071e+008 5.45965 9.68902 113.283Feb_12Value__ 2.40774e+008 5.11875 8.86769 92.4945Jan_12Value__ 1.79532e+008 5.72616 10.2021 122.237Dec_11Value__ 2.40840e+008 5.78903 10.4216 128.619Nov_11Value__ 2.40840e+008 5.78903 10.4216 128.61960 Oct_11Value__ 2.40432e+008 5.90893 10.4936 129.034Findings Ref to the table no-8 panel data are collected over the 2221 listed andregistered organisation in the LSE. These data collected over a time frame of oneyear (Oct-2011) to (Nov-2012). The cross sectional along with the time seriesmovement of the different organisation stock was assessed in terms mean, medianand standard deviation. Comparative analysis demonstrates that last six monthsthe mean value is comparatively more stable and showing sustainable growth. Interpretation Black and Scholes (2010) mentioned that though the standarddeviation indicates the level of standard error present in the series. Out of these2221 observation it is observed that almost all the observation of the last twelvemonths indicates the skewness that is more than 1. The result indicates that thatthe there is no leptokuritic present in the observed value. All the series (value ofthe market capitalization) of the different organisation is positively skewed. The Ex–Kurtosis over the span of last one year (last 12 months) for these entire 2231organisation registered in the London stock exchange indicates the value which isless than one. Further careful observation reveals that the value of the x- kurtosisis showing decline trend over the period of last 12 months.61 Chapter-5Conclusion and Recommendation5.1 Introduction The study show interdependence of stock market with the stock price of thedifferent organisation plays a pivotal role in co-movement of stock markets in thelast 5-6 years in LSE. More specifically variance decomposition of major parametersof the stock market return proves that they contribute significantly to the volatilityof LSE Sensex. For example opening and closing price of the indices contributemore than 13% of movement of LSE indices. Empirical findings also confirmed theexistence of long run relationships between LSE and other major indices taken intoconsideration. Variance auto regression analysis gave an indication that the impactof recession on LSE may not be static or short lived as the influence of globaldownturn is going to stay in the long run(Jondeau andRockinger, 2010).5.2 Validation of the objectiveThe present paper has illustrated the objectives mentioned in the chapter-1in anexplicit manner by using different statistical and econometric process. The presentresearch has disclosed important method of measuring, volatility, risk and expectedreturn from a particular stock from the stock market. The sample is extended withinthe time frame from June 2006 to Nov 2012.Ref to the objectives no 1 researcherhas utilized chi-square distribution to find the interdependencies between the stockmarket volume, its opening and closing prices of 2231 company?s stock. Market62 returns during time frame mentioned in the research is also assessed by measuringthe intercorrelationship ship between the opening and closing price through 95%confidence ellipse.Ref to the objective no-2 mentioned in the chapter-1 the paper has attempted toestimate the trend of the share price since 2006.Exponential smoothing processwas utilized to measure and examine the movement of the trend between theoriginal and smoothed value in stock marketRef to the objective no-3 mentioned in the chapter-1 the paper has attempted toassess the contribution of shocks due to the variation in the prices of stock onLondon stock exchange through Variance Decomposition process. Engle (2008)opined that VAR has conducted with the consideration of the unit root circle. Theinverse of the unit root cycle demonstrate the risk mitigation process. Bahra (2007)supported that time series behaviour of volatility; skewness and kurtosis aremeasured in the light of various macroeconomic risk factors. The opening andclosing price of the everyday stock, the specific highs and lows of the market andits impact on the times series behaviourof skewness are measured withthevariance decomposition process (Chandra, 2010).5.3 Limitation of the research ? While conducted such research, analysis is done based on the LSE itself. But aswe aware that the country?s stock market is interdependent with other market.So adequate thrust should be delivered in measuring the impact of those onthe LSE. Further co movement of the stock of different companies in different63 "