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\title{Does Stock Market Volatility in International Market Affect Volatility in Indian Market?}
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             \author[1]{Dr. Nilam  Panchal}

             \affil[1]{  Gujarat University}

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\date{\small \em Received: 10 December 2016 Accepted: 3 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


Introduction: Volatility in the prices of stock market creates an atmosphere of uncertainty and hampers productive investment. Foreign market changes are considered as one of the factors affecting the Indian stock market. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets.Purpose: The purpose of paper is to investigate the nature and characteristics of stock market volatility in India compare to foreign markets. The study establishes relationship in the movements of the Indian Stock Market in comparison to its international counterparts. Methodology: To study the effect on Indian stock market (SENSEX) with the change in the other foreign market indices, 13 major foreign stock exchanges has been selected based on the Market Capitalization.

\end{abstract}


\keywords{volatility, stock market, indices.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
resently, the fluctuations in the Indian market are attributed heavily to cross border capital flows in the form of FDI, FII and to reaction of Indian market to global market cues. In this context, understanding the relationship and influence of various exchanges on each other is very important. This study that compares global exchanges which are from different geo politico-socio-economic areas. With the cross border movements of capital like never before in the form of FDI and FII, coupled with the easing of restrictions bringing various stock exchanges at par in terms of system and regulations, it can be assumed reasonably that a particular stock exchange will have some impact on other exchanges. The main objective of this study is to capture the trends, similarities and patterns in the activities and movements of the Indian Stock Market in comparison to its international counterparts. The aim is to help the investors (current Author: Gujarat University. e-mail: nilamcpanchal@gmail.com and potential) understand the impact of important happenings on the Indian Stock exchange. This is especially relevant in the current scenario when the financial markets across the globe are getting integrated into one big market and the impact of one exchange on the other exchanges. In other words, the intention is to test the hypothesis, 'whether various stock exchanges globally have any impact on each other' or they are correlated in any way with regard to their movements and, if so, to what extent. Arising out of the main hypothesis is the question -given the above context: What impact would the result have on the understanding that international diversification of investment is desirable and profitable with regard to both risk and return 
\section[{a) Objectives of Study}]{a) Objectives of Study}\par
? To study the volatility of Indian stock market with changes in the value of the other foreign markets. ? To identify the market indices that significantly affects the volatility of the Indian stock market. 
\section[{b) Need for the study}]{b) Need for the study}\par
This study would thus help investors, analysts and other stakeholders in finding a relation between the volatility in Indian stock market and changes in foreign market and would thus help in making informed decisions. Risk averse and risk neutral investors may shy away from the market with frequent and sharp price movements. Investors will also get idea about how will Indian market behave with respect to particular change in any particular market. The study will enable the investors, analysts and other stakeholders of the economy to make better decisions based on the findings of the report. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
Michel felder Richard A., Pandya Saurin(2005) analyzed the volatility of stock returns and predictability for seven emerging markets for six countries (India, Hong Kong, South Korea, Malaysia, Singapore, Taiwan) and compared them with the mature markets of Japan and the US. The made use of skewness, excessive kurtosis, EGARCH and SKED models for analysis. It was found that emerging markets have higher volatility but lower persistence of shocks than in the mature markets. It was also found that the impact of non-trading days on P Rajwani Shegorika and Mukherjee Jaydeep (2013) investigated the linkages between Indian stock markets with other Asian stock markets namely, Hong Kong, Indonesia, Japan, South Korea, Malaysia, Taiwan and China. They used the daily data of the stock market indices of these countries and analyzed using units root test and Gregory and Hansen Cointegration technique. The results suggested that the Indian stock markets are not integrated with any of the Asian markets either individually or collectively, and thus lead to a conclusion that Indian markets are not sensitive to the dynamics in these markets in the long run.\par
Mukherjee Paramitaand Bose Suchismita (2005) examined whether the Indian stock market moves with other markets in Asia and the United States. They analyzed the daily data of the indices of these markets and used tools like group wise and pair wise co-integration and Granger-casuality tests. In the period of research from 1999 to 2004, it was found that on a daily basis the Indian index is most highly correlated with the Singapore STI index, and is also very highly correlated with the stock indices of Malaysia, South Korea, Taiwan and Thailand, while, the least correlation is observed with the US S\&P500 index. The degree of integration that is found to be not very high implies that the nature of integration with emerging Asian markets does not yet warranty any immediate concern for India regarding possible crisis and also shows that there is still much scope for investors for reaping benefits of portfolio diversification, by investing in Indian markets.\par
Sarkar, Amitava and Chakrabarti, Gagari and Sen, Chitrakalpa (2008) investigated the volatility of Indian Stock Market with other foreign markets. It used SENSEX as Indian Stock Index and Dow Jones, FTSE, BVSP, MerVal and JKSE for overseas indices. It was found that impact of developed countries, particularly US market has been quite prominent. As Brazilian and Argentine economies are quite similar to India's, their impact was mild. Evidences of regional contagion were also observed as Jakarta Stock Exchange transmitted its volatility to SENSEX. This has strong implication for the investors as well as policy implications as it highlights the extent of exposure and also the vulnerability of Indian stock market to the world.\par
Tripathi, Vanita and Sethi, Shruti (2012)examined the short run and long run inter linkages of the Indian stock market with those of Advanced emerging markets viz. Brazil, Hungary, Taiwan, Mexico, Poland and South Africa. They analyzed the daily data from 1992 to 2009 using Johansen co-integration test and Granger's causality test. It was found that the short run and long run inter linkages of the Indian stock market with these markets has increased over the study period. Liberalization policies adopted by these nations, increasing economic relations, rapid information group could be the plausible reasons behind such results.\par
Mukherjee Debjiban (2007) captured the trends, similarities and patterns in the activities and movements of the Indian Stock Market in comparison to its international counterparts in the context of globalization and the subsequent integration of the global markets. The data of 5 global and 2 Indian indices were collected for a period of 12 years from 1995; and this period was divided into 4 smaller periods. Comparative analysis was then carried out both on qualitative and quantitative parameters. It was found that the markets have started to integrate and Indian market is no exception, especially after 2002-03.\par
S.  {\ref Bordoloi and Shankar Shiv (2008)} tried to develop alternative models from the ARCH/ GARCH family to model the Indian equity markets. The equity market was represented by the two widely traded stock exchanges in India -BSE and NSE. Two stock indices, from each of the exchanges are selected for empirical analysis. The sample was taken for a period of almost 7 years. The stock returns are found to have possessed the asymmetrical property. The Threshold GARCH (TARCH) models were found to have explained the volatilities better for both the BSE Indices and S\&P-CNX 500, while the Exponential GARCH (EGARCH) model is found to be superior for the S\&P CNX-Nifty. Statistical tests in frequency domain were also conducted to test whether the volatilities for all the indices move in tandem or not, and it was found that the volatilities for all the indices move in tandem.\par
Sabur Mollah and Asma Mobarek (2009) tried to find out the time-varying risk return relationship and the persistence of shocks to volatility within GARCH framework both in developed and emerging markets. They used nonlinear ARCH and GARCH-family models for testing the volatility both in developed and emerging markets. The empirical results reported high risk-return and predictable nature of emerging markets compared to developed markets. The findings of the paper suggest that there is a long-term persistence shock in emerging markets compared to developed markets.\par
Mobarek Asma and Li Michelle (2014) in their paper suggested that the company-specific factors played a more crucial role in the Asia-Pacific countries than what was evident in the European and Latin-American countries. The time-varying weighting methodology was used to determine whether the volatility function was due to country-specific factors. The results showed that the influence of the common factors was significantly enhanced during the period of sub-prime financial crisis.\par
Karmakar Madhusudan (2006) measured the volatility of daily stock return in the Indian stock market over the period from 1961 to 2005. The study reported an evidence of time varying volatility; periods of high and low volatility clustering were also found; also high persistence and predictability was observed in volatility. It was also observed that volatility responds asymmetrically for positive and negative shocks. The conditional volatility also showed a clear evidence of volatility shifting over the period under study.\par
Affaneh Ibrahim and Boldin Robert (2001) examined five regional emerging markets in terms of volatility, correlations and effects of day of the week, month of the year and seasonally. The regional markets studied were Egypt, Greece, Israel, Jordan and Turkey. Data were analysed for the five years from 1993-1998. One of the finding was that there was an improvement in the stability of the markets over the period as measured by the variance ratio; this was the case despite the relatively high volatility in the markets. Also, low correlations were evident among the markets using the return factor (percentage change in the index). Conversely, high correlations were found using the index level.\par
Aggarwal Reena, Inclan Carla, and Leal Ricardo (1999) examined shifts in volatility of emerging stock market returns and the events that are associated with the increased volatility. The period of study was 1985-1995. The large changes in volatility seem to be related to important country-specific political, social, and economic events. These events include the stock market scandal in India, the Mexican peso crisis, periods of hyperinflation in Latin America, and the Marcos-Aquino conflict in the Philippines.\par
Chang Hsiao-fen (2012) tried to compare the volatility in stock market returns prior and post global financial crisis of 2008. For the study analysis of the closing price of stock indexes of Europe, America, and Taiwan, which are EURO STOXX 50, S\&P 500, and TAIEX respectively are taken. Data was taken for a period of 6 years from 2005 to 2011. Taiwan's VIX was found to be evidently higher than America's and Europe's before the crisis. While after the crisis, Taiwan's VIX was mostly lower than America's and Europe's. 
\section[{III.}]{III.} 
\section[{Research Methodology}]{Research Methodology}\par
The study is descriptive in nature. Quantitative Research approach has been used. The research is based on secondary data of Indian Stock Exchange (Sensex) and other foreign stock exchanges. The data is taken for a period of 5 years (January 1st, 2010 to December 31st, 2014). Daily closing value of all the indices has been taken for analysis. Daily data has been as daily data would reflect proper volatility of the stock markets. Data analysis has been done using SPSS. Linear regression of BSE Sensex with foreign indices has been done. To study the effect on Indian stock market (SENSEX) with the change in the other foreign market indices, we have considered 13 foreign stock exchanges based upon market capitalization. Major indices the countries like Australia (AORD), Brazil (BOVESPA), Canada(S \& P/ TSX), China (SSECOMPOSITE), France (CAC40/FCHI), Hong Kong (HIS), Germany (DAX), Indonesia (JCI), Italy (FTSEMIB), Japan (NIKKEI225), Switzerland (SMI), United Kingdom (FTSE100), United States of America (DOWJONES).\par
IV. 
\section[{Hypothesis of Study}]{Hypothesis of Study}\par
H0: There is no significant correlation between Sensex and foreign market indices. H1: There is a significant correlation between Sensex and foreign market indices.\par
V. 
\section[{Analysis a) Comparison of BSE Sensex with all Ordinaries -AORD (Australia)}]{Analysis a) Comparison of BSE Sensex with all Ordinaries -AORD (Australia)}\par
From Table -1, it can be concluded that there is a correlation between AORD and Sensex Since R>0.05, H0 is rejected. R value of .805 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0. 648 shows that AORD causes 64.8\% variation in SENSEX. The unstandardized coefficient of 5.662 shows that when SENSEX moves by 1 unit, AORD moves by 5.662 units. Thus we can conclude that when the stock market in Australia goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{b) Comparison of BSE Sensex with BOVESPA (Brazil)}]{b) Comparison of BSE Sensex with BOVESPA (Brazil)}\par
There exists correlation between BOVESPA and Sensex. (Table -2 in appendix) Since R<0.05, H0 is rejected. R value of -.328 signifies a negative correlation which means both the indices would move in opposite directions. R2 value of 0. 107 shows that BOVESPA causes 10. 7\% variation in SENSEX which is quite low. The unstandardized coefficient of -0.145 shows that when SENSEX moves by 1 unit, AORD moves by 0.145 units in the other direction. Thus, it can be concluded that when the stock market in Brazil goes up, Indian stock market is expected to go down, and vice versa. 
\section[{c) Comparison of BSE Sensex with Toronto Stock}]{c) Comparison of BSE Sensex with Toronto Stock}\par
Exchange -TSX (Canada) Since R>0.05, H0 is rejected, hence there exists correlation between TSX and Sensex. (Table  {\ref 3} in Appendix) R value of .852 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.726 shows that TSX causes 72.6\% variation in SENSEX. The unstandardized coefficient of 2.343 shows that when SENSEX moves by 1 unit, TSX moves by 2.343 units. Thus, when the stock market in Canada goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{d) Comparison of BSE Sensex with FCHI (France)}]{d) Comparison of BSE Sensex with FCHI (France)}\par
H0 is rejected Since R>0.05. Hence there exists correlation between FCHI and Sensex.(Table-4 in appendix). R value of .763 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.583 shows that FCHI causes 58.3\% variation in SENSEX. The un standardized coefficient of 5.424 shows that when SENSEX moves by 1 unit, FCHI moves by 5.424 units. Thus, when the stock market in France goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{e) Comparison of BSE Sensex with DAX (Germany)}]{e) Comparison of BSE Sensex with DAX (Germany)}\par
There exists a correlation between DAX and Sensex (Table-5 in Appendix) Since R>0.05, H0 is rejected .R value of .836 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.698 shows that DAX causes 69.8\% variation in SENSEX. The unstandardized coefficient of 1.886 shows that when SENSEX moves by 1 unit, DAX moves by 1.886 units. Thus we can conclude that when the stock market in Germany goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{f) Comparison of BSE Sensex with HSI (Hong Kong)}]{f) Comparison of BSE Sensex with HSI (Hong Kong)}\par
Table \hyperref[tab_0]{-}6 in appendix signifies that Since R>0.05, H0 is rejected, hence there exists correlation between HSI and Sensex. R value of 0.702 signifies a positive correlation which means both the indices would move in the same direction. R2 value of 0.493 shows that HSI causes 49.3\% variation in SENSEX. The unstandardized coefficient of 1.250 shows that when SENSEX moves by 1 unit, HSI moves by 1.250 units. Thus it can concluded that when the stock market in Hong Kong goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{g) Comparison of BSE Sensex with JCI (Indonesia)}]{g) Comparison of BSE Sensex with JCI (Indonesia)}\par
R value of .695 signifies a positive correlation which means both the indices would move in the same direction (Table -7 in appendix).R2 value of 0.483 shows that JCI causes 48.3\% variation in SENSEX. The unstandardized coefficient of 2.930 shows that when SENSEX moves by 1 unit, JCI moves by 2.930 units. Thus it can be concluded that when the stock market in Indonesia goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{h) Comparison of BSE Sensex with FTSEMIB (Italy)}]{h) Comparison of BSE Sensex with FTSEMIB (Italy)}\par
Since R>0.05, H0 is rejected, hence there exists correlation between FTSEMIB and Sensex (Table -8 in appendix). R value of .430 signifies a positive correlation which means both the indices would move in the same direction.R2 value of 0.185 shows that FTSEMIB causes 18.5\% variation in SENSEX. The unstandardized coefficient of 0.466 shows that when SENSEX moves by 1 unit, FTSEMIB moves by 0.466 units. Thus we can conclude that when the stock market in Italy goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{i) Comparison of BSE Sensex with NIKKEI (Japan)}]{i) Comparison of BSE Sensex with NIKKEI (Japan)}\par
Since R>0.05, H0 is rejected, hence there exists correlation between NIKKEI and Sensex (Table -9 in appendix). R value of .801 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.641 shows that NIKKEI causes 64.1\% variation in SENSEX. The unstandardized coefficient of 0.893 shows that when SENSEX moves by 1 unit, NIKKEI moves by 0.893 units. Thus we can conclude that when the stock market in Japan goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{j) Comparison of BSE Sensex with SSE (China)}]{j) Comparison of BSE Sensex with SSE (China)}\par
Since R<0.05, H0 is rejected, hence there exists correlation between SSE and Sensex (Table -10 in appendix). R value of -0.276 signifies a negative correlation which means both the indices would move in opposite direction. R2 value of 0.076 shows that SSE causes 7.6\% variation in SENSEX which is very low. The un standardized coefficient of -2.410 shows that when SENSEX moves by 1 unit, SSE moves by -2.410 units in the opposite direction. Thus it can be said that when the stock market in China goes up, Indian stock market is expected to go down, and vice versa. 
\section[{k) Comparison of BSE Sensex with SMI (Switzerland)}]{k) Comparison of BSE Sensex with SMI (Switzerland)}\par
Since R>0.05, H0 is rejected, hence there exists correlation between SMI and Sensex (Table -11 in appendix). R value of .832 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.693 shows that SMI causes 69.3\% variation in SENSEX. The unstandardized coefficient of 2.405shows that when SENSEX moves by 1 unit, SMI moves by 2.405 units. Thus we can conclude that when the stock market in Switzerland goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{l) Comparison of BSE Sensex with DOW JONES -DJI (United States of America)}]{l) Comparison of BSE Sensex with DOW JONES -DJI (United States of America)}\par
Since R>0.05, H0 is rejected, hence there exists correlation between DJI and Sensex (Table -12 in appendix). R value of .792 signifies a highly positive correlation which means both the indices would move in the same direction. R2 value of 0.628 shows that DJI causes 62.8\% variation in SENSEX. The unstandardized coefficient of 1.063 shows that when SENSEX moves by 1 unit, DJI moves by 1.063 units. Thus we can conclude that when the stock market in USA goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{m) Comparison of BSE Sensex with FTSE (United}]{m) Comparison of BSE Sensex with FTSE (United}\par
Kingdom) Since R>0.05, H0 is rejected, hence there exists correlation between FTSE and Sensex (Table -13 in appendix). R value of. 745 signifies a positive correlation which means both the indices would move in t same direction. R2 value of 0.555 shows that FTSE causes 55.5\% variation in SENSEX.\par
The un standardized coefficient of 4.278shows that when SENSEX moves by 1 unit, FTSE moves by 4.278 units. Thus we can conclude that when the stock market in UK goes up, Indian stock market is also expected to go up, and vice versa. 
\section[{VI.}]{VI.} 
\section[{Conclusion}]{Conclusion}\par
The above analysis reveals that Sensex is highly correlated with Australian (AORD), Canadian (TSX), French (FCHI), German (DAX), Japanese (NIKKEI), Swiss (SMI) and American (DJI) markets. A slight change in these markets causes a significant effect on Indian markets. It can be seen that Sensex is negatively correlated with Brazilian (BOVESPA) and Chinese (SSE) markets. So a change in these markets causes an opposite change in Sensex. The Toronto Stock Exchange (Canada) has the highest correlation, which shows that it causes maximum impact on the Indian market. It can also be observed that Indian stock market is significantly affected by the stock market of the developed countries.\par
references références referencias ANNEXURES \begin{figure}[htbp]
\noindent\textbf{-} \par 
\begin{longtable}{P{0.85\textwidth}}
1. Comparison of BSE Sensex with All Ordinaries -AORD (Australia) Descriptive Statistics Mean Std. Deviation Sensex 19565.4116 3012.06835 AORD 4806.187 428.3907 Sensex Pearson Correlation Sensex 1.000 AORD .805 Sig. (1-tailed) Sensex . AORD .000 N Sensex 1205 AORD 1205 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change Change df1 df2 N 1205 1205 AORD .805 1.000 .000 . 1205 1205 Change Statistics F 1 .805 a .648 .648 1786.72562 .648 2218.688 1 1203 a. Predictors: (Constant), AORD Coefficients a Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0\% Confidence Interval Sig. F Change .000 for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -7646.146 579.992 -13.183 .000 -8784.055 -6508.237 AORD 5.662 .120 .805 47.103 .000 5.426 5.898 a. Dependent Variable: Sensex 2. Comparison of BSE Sensex with BOVESPA (Brazil 1 ) Correlations Sensex BOVESPA Pearson Correlation Sensex 1.000 -.328 BOVESPA -.328 1.000 Sig. (1-tailed) Sensex . .000 BOVESPA .000 . N Sensex 1184 1184 BOVESPA 1184 1184 Correlations Descriptive Statistics Mean Std. Deviation N Sensex 19559.0986 3005.65192 1184 BOVESPA 58914.42 6773.429 1184 Model Summary Mo del R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .328 a .107 .107 2840.97427 .107 142.121 1 1182 Coefficients a Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound -10650.520 540.057 -.000 -11710.090 -9590.950 5. Comparison of BSE Sensex with DAX (Germany) 4 Coefficients a 10. Comparison of BSE Sensex with SSE (China) 9 Model Summary Descriptive Statistics Mean Std. Deviation N Sensex 19597.6727 2994.40971 1196 DAX 7509.9877 1326.69535 1196 Model R R Square Adjusted R Square Std. Error of the Estimate Unstandardized Standardized 95.0\% Confidence Interval for Descriptive Statistics Change Statistics R Square Change F Change df1 df2 Model Coefficients Coefficients T B Sig. B Std. Error Beta Lower Bound Upper Bound Mean Std. Deviation N Sig. F Change 1 .695 a .483 .483 2166.04455 .483 1088.102 1 1164 .000 1 11057.0 526.828 20.988 .000 10023.402 12090.632 Sensex 19472.7277 2912.85357 1161 (Constant) 17 FTSEMIB .466 .028 .430 16.420 .000 .410 SSE 2427.7209 333.92015 1161 .522 .000 a. Predictors: (Constant), BOVESPA Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 28123.881 723.164 38.890 .000 26705.052 29542.710 BOVESPA -.145 .012 -.328 -11.921 .000 -.169 -.121 a. Dependent Variable: Sensex 3. Comparison of BSE Sensex with Toronto Stock Exchange -TSX (Canada) 2 Descriptive Statistics Mean Std. Deviation N Sensex 19554.6720 2991.88919 1193 TSX 12890.446 1087.6753 1193 Correlations Sensex TSX Pearson Correlation Sensex 1.000 .852 TSX .852 1.000 Sig. (1-tailed) Sensex . .000 TSX .000 . N Sensex 1193 1193 TSX 1193 1193 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .852 a .726 .725 1567.72577 .726 3150.373 1 1191 .000 a. Predictors: (Constant), TSX 1 (Constant) 19.72 Correlations a. Dependent Variable: Sensex Correlations a. Predictors: (Constant), JCI 1 TSX 2.343 .042 .852 56.12 8 .000 2.261 Sensex DAX 9. Comparison of BSE Sensex with NIKKEI (Japan) 8 Sensex SSE Coefficients a 2.425 a. Dependent Variable: Sensex 4. Comparison of BSE Sensex with FCHI (France) 3 Descriptive Statistics Mean Std. Deviation N Sensex 19618.3554 3009.98743 1196 FCHI 3788.8711 423.64204 1196 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .763 a .583 .583 1944.80587 .583 1668.495 1 1194 .000 a. Predictors: (Constant), FCHI Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -934.187 506.289 -1.845 .065 -1927.503 59.129 FCHI 5.424 .133 .763 40.847 .000 5.164 5.685 a. Dependent Variable: Sensex Correlations Sensex FCHI Pearson Correlation Sensex 1.000 .763 FCHI .763 1.000 Sig. (1-tailed) Sensex . .000 FCHI .000 . N Sensex 1196 1196 FCHI 1196 1196 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .836 a .698 .698 1646.00536 .698 2760.830 1 1194 .000 a. Predictors: (Constant), DAX Coefficients a Model Unstandardized Coefficients Standardize d Coefficients T Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 5435.328 273.705 19.858 .000 4898.331 5972.324 DAX 1.886 .036 .836 52.544 .000 1.815 1.956 a. Dependent Variable: Sensex 6. Comparison of BSE Sensex with HSI (Hong Kong) 5 Descriptive Statistics Mean Std. Deviation N Sensex 19563.9720 2998.16327 1196 HIS 21829.0050 1684.13767 1196 Pearson Correlation Sensex 1.000 .836 DAX .836 Unstandardized Standardized 95.0\% Confidence Interval for Sensex 1.000 -.276 Descriptive Statistics Pearson Correlation 1.000 Sig. (1-tailed) Sensex . .000 DAX .000 . N Sensex 1196 1196 DAX 1196 1196 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .702 a .493 .493 2135.61387 .493 1161.228 1 1194 .000 a. Predictors: (Constant), HIS Coefficients a Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -7722.911 803.124 -9.616 .000 -9298.603 -6147.219 HSI 1.250 .037 .702 34.077 .000 1.178 1.322 a. Dependent Variable: Sensex 7. Comparison Of BSE Sensex With JCI (Indonesia) 6 Descriptive Statistics Mean Std. Deviation N Sensex 19553.6870 3011.60795 1166 JCI 4067.2661 714.41182 1166 Model Coefficients Coefficients T Sig. B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 7635.968 366.818 20.817 .000 6916.269 8355.667 JCI 2.930 .089 .695 32.986 .000 2.756 3.104 a. Dependent Variable: Sensex 8. Comparison of BSE Sensex with FTSEMIB (Italy) 7 Descriptive Statistics Mean Std. Deviation N Sensex 19611.7085 2991.81506 1193 FTSEMIB 18353.678 2757.8049 1193 Correlations Sensex FTSEMIB Pearson Correlation Sensex 1.000 .430 FTSEMIB .430 1.000 Sig. (1-tailed) Sensex . .000 FTSEMIB .000 . N Sensex 1193 1193 FTSEMIB 1193 1193 Mean Std. Deviation N SSE -.276 1.000 Sensex 19591.5357 3028.87256 1170 NIKKEI 11493.9188 2715.60408 1170 Sensex . .000 Sig. (1-tailed) SSE .000 . N Sensex 1161 1161 SSE 1161 1161 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .276 a .076 .076 2800.68596 .076 95.777 1 1159 .000 a. Predictors: (Constant), SSE Coefficients a 7 29 25 Global Journal of Management and Business Research Volume XVII Issue III Version I Year ( ) 27 Global Journal of Management and Business Research Volume XVII Issue III Version I Year ( ) Volume XVII Issue III Version I Year ( ) Model Summary 1 .430 a .185 .184 2702.73619 .185 269.624 1 1191 .000 a. Predictors: (Constant), FTSEMIB Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .801 a .641 .641 1814.77839 .641 2088.337 1 1168 .000 a. Predictors: (Constant), NIKKEI Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 25323.614 603.473 41.963 .000 24139.592 26507.635 SSE -2.410 .246 -.276 -9.787 .000 -2.893 -1.927 a. Dependent Variable: Sensex 11. Comparison of BSE Sensex with SMI (Switzerland) 10 Descriptive Statistics Mean Std. Deviation N Global Journal of Management and Business Research Model R R Square Adjusted R Square Change Statistics Sensex 19587.5225 2996.28871 1194 Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change SMI 7044.335 1037.2480 1194\\
2 Table-3 Comparison of BSE Sensex with Toronto Stock Exchange -TSX (Canada)\end{longtable} \par
  {\small\itshape [Note: 4 Table -5 Comparison of BSE Sensex with DAX (Germany) 5 Table -6 Comparison of BSE Sensex with HSI (Hong Kong) 2017 B © 20 17 Global Journals Inc. (US) 2017 B © 20 17 Global Journals Inc. (US)]} 
\caption{\label{tab_0}Table - 8}\end{figure}
 			\footnote{B © 20 17 Global Journals Inc. (US) Does Stock Market Volatility in International Market Affect Volatility in Indian Market?} 			\footnote{© 20 17 Global Journals Inc. (US) Does Stock Market Volatility in International Market Affect Volatility in Indian Market?} 			\footnote{Table-2 Comparison of BSE Sensex with BOVESPA (Brazil 1 ) © 2017 Global Journals Inc. (US) 1 Does Stock Market Volatility in International Market Affect Volatility in Indian Market?} 			\footnote{Table -7 Comparison of BSE Sensex with JCI (Indonesia) © 2017 Global Journals Inc. (US) 1} 			\footnote{Table -9 Comparison of BSE Sensex with NIKKEI (Japan) © 2017 Global Journals Inc. (US) 1} 			\footnote{Table -10 Comparison of BSE Sensex with SSE (China) 10 Table-11 Comparison of BSE Sensex with SMI (Switzerland)} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
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