"in the text books as well as proposes various solution to the problem link up withthe LSE3.7 Modeling- Data selectionDaily indices of five major countries USA (Dow Jones), Japan (Nikkei), UK (Londonstock exchange), Hongkong (Hanseng) and Singapore (STI) along with BSE Sensexare considered for analysis. These countries are chosen because of their relativeimportance depending on their trade relation with India. The following reasons canbe attributed for their selection.? USA, the largest financial market in the world also with highest Marketcapitalization.? UK is one of the strongest European markets.? Japan other than being a strong market, play the most significant role inAsian Economy.? China and specifically Singapore have been chosen for their growing traderelation with India Patro and Wald (2010) mentioned that Closing value of each of the above stockth th market for the period 10 December to 12 December 2006 are collected from„historical prices? of Yahoo Finance Above period is chosen because during mid ofthe period Sensex touches its bottom before reviving so both contraction andrevival phase of recessionary cycle can be taken into account.40 3.7.1 Sample size The researcher has considered all the companies (2231) that are enlisted andregistered in the London stock exchange since 2006. Panel data and time seriesdata are considered for analysis.3.8 Justification behind data selectionResearcher has considered the Time series data so that the auto correlation amongdifferent stock prices where the movement of different stock prices over the sameand different year may vary over time. The longitudinal data are avoidedconsidering the different time frame and lack of variety in the homogenous group.The reason for choosing two different kind of data is one will deliver the lag effectwith respect to time and another ( panel data ) will provide the impact of thecontagion effect. 3.9 Methodology and Empirical frameworkFollowing methodology is employed for the purpose of quantitative analysis andconclusion? Summary statistics of the return series employed to understand the returnand volatility.? Correlation matrix of actual indices/prices as well as return series to examinethe degree of interdependency amongst them.? Augmented Dickey-Fuller test to check the stationary status.41 ? Akaike criterion (AIC) for selecting optimum lags for the purpose of runningVector Auto regression.? VAR model for implementing Variance Decomposition of the return series?Exponential smoothing to find out the trend of the LSE stock market in thelast 5 years (Considering everyday indices).Chapter-4Result and Discussion4.1IntroductionDuring the research present situation of the LSE is determined with respect to theits openingand closing stock price, high and low value ofeveryday stock position.Co movement of the stock Exchange with respect to different organisation stockisalso taken into consideration during the discussion part.42 Fig no-3 Time series plot of LSE june2006 to Nov 2012(Source : Analysed by reseracher through E-views 8.0 output file )FindingsRef to the fig no-3 the time series analysis of the LSE since June 2006illustrates the overall merket trend of the stock market . Volatility , suddent marketups and downare reflected in certain portion of the graph. For instance the graphexplains thelogic of extreme high(Dec, 2007 ) and low(march , 2009) by theeffect global recession andeconomic down turn in the entire global stock market.43 Fig no-4 Probability density function of the opening price since June 2006(Source: Analysed by researcher through STATA output file)Findings Ref to the Fig no-4, the normal distribution curve of the opening price ofthe LSE since June 2006 is illustrated with the help of probability density function.The chi – square test result (337.607 with a p value 0.000< 0.01) indicates thatthere is an association exist between the all these time series data with each other.So it can be inferred that all these opening of the stock market are dependent witheach other.44 Interpretation: Ref to the table no-4 (mentioned below) mean of 967 with a median 894.5 reflectsthat out of valid 1766 observation there is aextreme low is observed at 368.75 .Deb et al. (2003) illustrated that the standard deviation 314.07 indicates that thedeviation of the movement of different stock is quite less compare to the mean.Skewness (0.836882) of the 1766 observation indicates that majority of the stockproduces skewness which is <1.there they are not all leptokurtic.Ex kurtosis resultillustrate that majority of the stock are less than less than one with a value 0.431Table no-4 Summary Statistics, using the observations 05/12/12 - 12/12/10for the variable Opening Price (1766 valid observations) Mean Median Minimum Maximum967.044 894.500 368.750 2000.00 Std. Dev. C.V. Skewness Ex. kurtosis314.057 0.324760 0.836882 0.431089(Source : Analysed by researcher through SPSS 12 Output file)45 Fig no-5 Probability density function of the closing price since June 2006(Source: Analysed by researcher through SPSS 12 output file)Findings Ref to the figure no 4, probability distribution function indicates theclosing price of the market since June 2006. The chi – square test result (343.623with a p value 0.000< 0.01) indicates that there is an association exist between theall these time series data with each other. So it can be inferred that all theseopening of the stock market are dependent with each other.InterpretationRef to the fig no-5 (mentioned below) mean of 967.38 with a median 894.5 reflectsthat out of valid 1766 observation there is aextreme low is observed at 368.75 .The standard deviation 312.46 indicates that the deviation of the movement ofdifferent stock is quite less compare to the mean. Lower value of Skewness of the1766 observation also indicates that majority of the stock produces a skewnesswhich is <1.therefore they are not all leptokurtic in nature.Ex kurtosis result46 illustrate that majority of the stock are less than less than one with a value0.05.However as par the emerging market return are normally distributed even inthe developed market returns from the stock is found to be either leptokuritic orplatykuritic in nature. Coutant et al. (2009) explained there should be need toorganize a autocorrelation and non parametric test once it is found that there isstrong deviation from the normality. However according Melick and Thomas, (2007,p 110),” normality can still be assumed for the sake of statistical analysis as thenumber of observation size is large enough”. Ait-Shalia et.al (2009) opined andargued that non parametric test is still recommended even if the normality test isrejected during the assessment of probability density function.47 "