Pub Date : 2019-06-11DOI: 10.1177/0972652719846323
Suraj Kumar, Krishna Prasanna
This study investigates the dynamic impact of global and regional liquidity along with volatility on the liquidity of emerging Asian equity markets. Further, we empirically disentangle the effects of volatility and liquidity. We find that the external liquidity factors have a higher impact on domestic liquidity as compared to volatility. The impact of global volatility shocks was witnessed only during the Global Financial Crisis. Global factors have a higher influence on developed markets such as Japan and Singapore, while regional factors have a higher influence on emerging markets. These results indicate that liquidity serves as the channel of regional integration in Asia. The findings of this study provide useful insights to cross-sections of stakeholders in the investment industry. JEL Classification: G15, F21, F36
{"title":"Global Financial Crisis: Dynamics of Liquidity Risk in Emerging Asia","authors":"Suraj Kumar, Krishna Prasanna","doi":"10.1177/0972652719846323","DOIUrl":"https://doi.org/10.1177/0972652719846323","url":null,"abstract":"This study investigates the dynamic impact of global and regional liquidity along with volatility on the liquidity of emerging Asian equity markets. Further, we empirically disentangle the effects of volatility and liquidity. We find that the external liquidity factors have a higher impact on domestic liquidity as compared to volatility. The impact of global volatility shocks was witnessed only during the Global Financial Crisis. Global factors have a higher influence on developed markets such as Japan and Singapore, while regional factors have a higher influence on emerging markets. These results indicate that liquidity serves as the channel of regional integration in Asia. The findings of this study provide useful insights to cross-sections of stakeholders in the investment industry. JEL Classification: G15, F21, F36","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"140 2","pages":"339 - 362"},"PeriodicalIF":1.5,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41299038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-10DOI: 10.1177/0972652719846308
Dilip Kumar
The study investigates the volatility transmission from developed markets (the United States [US], the United Kingdom [UK] and Japan) to the major Asian emerging markets (India, China, Malaysia, Thailand and Indonesia) during a period from 1996 to 2015. We make use of the opening, high, low and closing prices to estimate unbiased extreme value volatility estimator and implement heterogeneous autoregressive distributed lag (HAR-DL) framework to study the spillover effects. Based on time-varying spillover analysis, we observe sudden changes in the spillover effect during the periods of major crises. Initially, we find evidence of contagion during the period of global financial crisis of 2007–2009. However, after accounting for conditional heteroscedasticity, we observe a decline in the strength of volatility transmission from developed markets to the Asian emerging markets. Moreover, the initial evidence of contagion is not detectable anymore. We also test the economic significance of the findings by implementing three trading strategies based on risk averse and risk-taking investors that make use of the forecasted variance based on HAR-DL specification. Our findings indicate that substantial average annualised gains in returns can be earned based on the lagged volatility components of the USA and the UK. JEL Classification: C32, C58, G01, G15
{"title":"Structural Breaks in Volatility Transmission from Developed Markets to Major Asian Emerging Markets","authors":"Dilip Kumar","doi":"10.1177/0972652719846308","DOIUrl":"https://doi.org/10.1177/0972652719846308","url":null,"abstract":"The study investigates the volatility transmission from developed markets (the United States [US], the United Kingdom [UK] and Japan) to the major Asian emerging markets (India, China, Malaysia, Thailand and Indonesia) during a period from 1996 to 2015. We make use of the opening, high, low and closing prices to estimate unbiased extreme value volatility estimator and implement heterogeneous autoregressive distributed lag (HAR-DL) framework to study the spillover effects. Based on time-varying spillover analysis, we observe sudden changes in the spillover effect during the periods of major crises. Initially, we find evidence of contagion during the period of global financial crisis of 2007–2009. However, after accounting for conditional heteroscedasticity, we observe a decline in the strength of volatility transmission from developed markets to the Asian emerging markets. Moreover, the initial evidence of contagion is not detectable anymore. We also test the economic significance of the findings by implementing three trading strategies based on risk averse and risk-taking investors that make use of the forecasted variance based on HAR-DL specification. Our findings indicate that substantial average annualised gains in returns can be earned based on the lagged volatility components of the USA and the UK. JEL Classification: C32, C58, G01, G15","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"172 - 209"},"PeriodicalIF":1.5,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46744375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-07DOI: 10.1177/0972652719846318
K. Kulshrestha, S. Bhaduri
The article explores the relationship between volatility and liquidity, as there is a change in market capitalisation (cap). Using three regimes of volatility, identified by the threshold vector auto-regression method, the results show that volatility affects liquidity differently for the three volatility regimes during the two periods (crisis and post-crisis) of study. The results show that there is inconsistency in how volatility affects liquidity across the Indian large-, mid- and small-cap indices. JEL Classification: G1 G17
{"title":"The Joint Dynamics of Liquidity and Volatility Across Small- and Large- index Indian Funds","authors":"K. Kulshrestha, S. Bhaduri","doi":"10.1177/0972652719846318","DOIUrl":"https://doi.org/10.1177/0972652719846318","url":null,"abstract":"The article explores the relationship between volatility and liquidity, as there is a change in market capitalisation (cap). Using three regimes of volatility, identified by the threshold vector auto-regression method, the results show that volatility affects liquidity differently for the three volatility regimes during the two periods (crisis and post-crisis) of study. The results show that there is inconsistency in how volatility affects liquidity across the Indian large-, mid- and small-cap indices. JEL Classification: G1 G17","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S167 - S182"},"PeriodicalIF":1.5,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48798001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-07DOI: 10.1177/0972652719846304
Lucía Morales, B. Andréosso‐O'callaghan
The impact of Brexit and the election of Donald Trump as the 45th US president in the context of stock market reactions and economic policy uncertainty (EPU) within three key zones in ‘the Greater China Region’ (Hong Kong, Taiwan and China Mainland) are examined in this article. The chosen research period is from January 2014 to June 2017, and the EPU Index in the USA and the UK is used as a proxy to measure political uncertainty in two of the world major economies and how they impact on the Chinese stock market. The main contribution of the article can be found in the analysis of how stock market performance can be driven by policy-related uncertainty shocks in the international context. The results show that the stock markets in the ‘Greater China Region’ did not seem to react either to the uncertainty generated by Brexit or to the election of Donald Trump, implying that the Chinese stock markets appear to be quite resilient to the recent political events that have been disrupting the global economy. JEL codes: G58, G15, G18
{"title":"Challenges and Opportunities Brought to the Chinese Economy by Brexit and the New US Administration","authors":"Lucía Morales, B. Andréosso‐O'callaghan","doi":"10.1177/0972652719846304","DOIUrl":"https://doi.org/10.1177/0972652719846304","url":null,"abstract":"The impact of Brexit and the election of Donald Trump as the 45th US president in the context of stock market reactions and economic policy uncertainty (EPU) within three key zones in ‘the Greater China Region’ (Hong Kong, Taiwan and China Mainland) are examined in this article. The chosen research period is from January 2014 to June 2017, and the EPU Index in the USA and the UK is used as a proxy to measure political uncertainty in two of the world major economies and how they impact on the Chinese stock market. The main contribution of the article can be found in the analysis of how stock market performance can be driven by policy-related uncertainty shocks in the international context. The results show that the stock markets in the ‘Greater China Region’ did not seem to react either to the uncertainty generated by Brexit or to the election of Donald Trump, implying that the Chinese stock markets appear to be quite resilient to the recent political events that have been disrupting the global economy. JEL codes: G58, G15, G18","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"145 - 171"},"PeriodicalIF":1.5,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48785974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831562
Pami Dua, Ritu Suri
This article examines interlinkages between four major exchange rates, namely, USD–INR, EUR–INR, GBP–INR and JPY–INR in terms of returns and volatility spillovers using a vector autoregressive-multivariate GARCH–BEKK framework. In addition, we analyse the impact of RBI intervention on the returns, volatility and covariance of these exchange rates. The study finds significant bidirectional causality-in-mean and causality-in-variance between all four exchange rates. The estimation results suggest that RBI intervention in the form of net purchase of dollars leads to depreciation of INR vis-à-vis USD, EUR, GBP and JPY. Furthermore, we find that RBI intervention not only significantly affects the volatility of INR vis-à-vis USD, EUR and GBP but also explains significant amount of covariance between USD–INR and the other three exchange rates. JEL Classification: C32, G15, E58, F31
{"title":"Interlinkages Between USD–INR, EUR–INR, GBP–INR and JPY–INR Exchange Rate Markets and the Impact of RBI Intervention","authors":"Pami Dua, Ritu Suri","doi":"10.1177/0972652719831562","DOIUrl":"https://doi.org/10.1177/0972652719831562","url":null,"abstract":"This article examines interlinkages between four major exchange rates, namely, USD–INR, EUR–INR, GBP–INR and JPY–INR in terms of returns and volatility spillovers using a vector autoregressive-multivariate GARCH–BEKK framework. In addition, we analyse the impact of RBI intervention on the returns, volatility and covariance of these exchange rates. The study finds significant bidirectional causality-in-mean and causality-in-variance between all four exchange rates. The estimation results suggest that RBI intervention in the form of net purchase of dollars leads to depreciation of INR vis-à-vis USD, EUR, GBP and JPY. Furthermore, we find that RBI intervention not only significantly affects the volatility of INR vis-à-vis USD, EUR and GBP but also explains significant amount of covariance between USD–INR and the other three exchange rates. JEL Classification: C32, G15, E58, F31","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S102 - S136"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42386737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831556
L. Ramana
Pricing of initial public offerings (IPOs) has received considerable attention from the perspective of asymmetric information theories, among others. Specific aspects of emerging markets have been incorporated into models to explain the varying degrees of underpricing. Using three features that are deemed to be important for such economies, that is, principal–principal conflicts, disclosure norms and legitimacy of the top management, and two different classes of investors, institutional and retail, two frameworks have been designed to explain the expected levels of underpricing under various pair-wise combinations of these parameters. The state of the secondary market, which is an important determinant of the decision to go public, is incorporated into the framework. JEL Classifications: G3, G14, G15, G18
{"title":"Perspective on Underpricing of IPOs in Emerging Economies","authors":"L. Ramana","doi":"10.1177/0972652719831556","DOIUrl":"https://doi.org/10.1177/0972652719831556","url":null,"abstract":"Pricing of initial public offerings (IPOs) has received considerable attention from the perspective of asymmetric information theories, among others. Specific aspects of emerging markets have been incorporated into models to explain the varying degrees of underpricing. Using three features that are deemed to be important for such economies, that is, principal–principal conflicts, disclosure norms and legitimacy of the top management, and two different classes of investors, institutional and retail, two frameworks have been designed to explain the expected levels of underpricing under various pair-wise combinations of these parameters. The state of the secondary market, which is an important determinant of the decision to go public, is incorporated into the framework. JEL Classifications: G3, G14, G15, G18","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S101 - S87"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44002484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831564
Dheeraj Misra, Sushma Vishnani, Ankit Mehrotra
This study aims at analysing the impact of co-skewness and co-kurtosis on the returns of the Indian stocks by incorporating co-skewness and co-kurtosis in the traditional capital asset pricing model (CAPM) of Sharpe, in a three-factor model of Fama and French and in a four-factor model of Carhart. The results of the study show that co-skewness and co-kurtosis have significant impact on the returns of the Indian stock. However, the impact of co-skewness is higher than co-kurtosis. JEL Classification: G11, G12
{"title":"Four-moment CAPM Model: Evidence from the Indian Stock Market","authors":"Dheeraj Misra, Sushma Vishnani, Ankit Mehrotra","doi":"10.1177/0972652719831564","DOIUrl":"https://doi.org/10.1177/0972652719831564","url":null,"abstract":"This study aims at analysing the impact of co-skewness and co-kurtosis on the returns of the Indian stocks by incorporating co-skewness and co-kurtosis in the traditional capital asset pricing model (CAPM) of Sharpe, in a three-factor model of Fama and French and in a four-factor model of Carhart. The results of the study show that co-skewness and co-kurtosis have significant impact on the returns of the Indian stock. However, the impact of co-skewness is higher than co-kurtosis. JEL Classification: G11, G12","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S137 - S166"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47039413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831549
Harshita, Shveta Singh, Surendra S. Yadav
Covering 20 years (1995–2015), the article ascertains the presence of the month-of-the-year effect in the Indian stock market, for the raw returns series as well as after adjusting for non-linearities of the market. Whether the effect is the same for portfolios of different sizes and values is also ascertained. The threshold generalised autoregressive conditionally heteroskedastic (TGARCH) model is employed to address non-linearity. The results suggest the presence of higher returns in November/December at the index level. Further, only firms with a size smaller than the average exhibit seasonality in the form of the April/May and November/December effect. The value-sorted portfolios exhibit weaker evidence of the December effect. Tax-loss selling, window dressing and behavioural aspects seem to provide the explanation. JEL Classification: C58, G14
{"title":"Unique Calendar Effects in the Indian Stock Market: Evidence and Explanations","authors":"Harshita, Shveta Singh, Surendra S. Yadav","doi":"10.1177/0972652719831549","DOIUrl":"https://doi.org/10.1177/0972652719831549","url":null,"abstract":"Covering 20 years (1995–2015), the article ascertains the presence of the month-of-the-year effect in the Indian stock market, for the raw returns series as well as after adjusting for non-linearities of the market. Whether the effect is the same for portfolios of different sizes and values is also ascertained. The threshold generalised autoregressive conditionally heteroskedastic (TGARCH) model is employed to address non-linearity. The results suggest the presence of higher returns in November/December at the index level. Further, only firms with a size smaller than the average exhibit seasonality in the form of the April/May and November/December effect. The value-sorted portfolios exhibit weaker evidence of the December effect. Tax-loss selling, window dressing and behavioural aspects seem to provide the explanation. JEL Classification: C58, G14","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S35 - S58"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44254848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831546
Samaresh Bardhan, Rajesh Sharma, Vivek Mukherjee
The article investigates role of bank-specific factors on non-performing assets (NPAs) in Indian banking system in a panel threshold framework (Hansen, 1999, Journal of Econometrics, 93(2), 345–368), using an unbalanced panel of 82 scheduled commercial banks over the period of 1995–1996 to 2010–2011. We consider capital to risk-weighted assets ratio (CRAR) and credit growth as alternative threshold variables (and regime dependent) along with relevant bank-specific variables treated as regime independent. Findings reveal that CRAR exerts negative and significant impact on NPAs once it reaches a critical threshold. Possible implication is that banks extend less risky loans in a high CRAR regime than in low CRAR regime that helps reduce NPAs. With credit growth as threshold as well as regime dependent, we observe statistically significant non-linear effect of credit growth on NPAs. Beyond threshold, credit growth exerts significant negative effect on NPAs that may imply that banks extend good quality loans. However, we cannot rule out the possibility of evidence of ‘ever-greening hypothesis’ of bad debts in Indian banking, that is, banks just roll over previous bad debts into fresh performing loans. JEL Classification: G21, G28, C13, C33
本文在面板阈值框架中调查了印度银行系统中银行特定因素对不良资产的作用（Hansen，1999，Journal of Econometrics，93（2），345-368），使用了1995-1996年至2010-2011年期间82家计划商业银行的不平衡面板。我们将资本与风险加权资产比率（CRAR）和信贷增长视为替代阈值变量（和制度相关），并将相关银行特定变量视为制度独立变量。研究结果表明，一旦达到临界阈值，CRAR就会对NPA产生负面和显著的影响。可能的含义是，银行在高CRAR制度下发放的贷款风险比低CRAR制度低，这有助于减少不良贷款。在以信贷增长为阈值和制度依赖的情况下，我们观察到信贷增长对NPA的非线性影响具有统计学意义。超过阈值，信贷增长对不良贷款产生了显著的负面影响，这可能意味着银行提供了高质量的贷款。然而，我们不能排除印度银行业存在坏账“不断绿化假说”的可能性，即银行只是将以前的坏账展期为新的表现良好的贷款。JEL分类：G21、G28、C13、C33
{"title":"Threshold Effect of Bank-specific Determinants of Non-performing Assets: An Application in Indian Banking","authors":"Samaresh Bardhan, Rajesh Sharma, Vivek Mukherjee","doi":"10.1177/0972652719831546","DOIUrl":"https://doi.org/10.1177/0972652719831546","url":null,"abstract":"The article investigates role of bank-specific factors on non-performing assets (NPAs) in Indian banking system in a panel threshold framework (Hansen, 1999, Journal of Econometrics, 93(2), 345–368), using an unbalanced panel of 82 scheduled commercial banks over the period of 1995–1996 to 2010–2011. We consider capital to risk-weighted assets ratio (CRAR) and credit growth as alternative threshold variables (and regime dependent) along with relevant bank-specific variables treated as regime independent. Findings reveal that CRAR exerts negative and significant impact on NPAs once it reaches a critical threshold. Possible implication is that banks extend less risky loans in a high CRAR regime than in low CRAR regime that helps reduce NPAs. With credit growth as threshold as well as regime dependent, we observe statistically significant non-linear effect of credit growth on NPAs. Beyond threshold, credit growth exerts significant negative effect on NPAs that may imply that banks extend good quality loans. However, we cannot rule out the possibility of evidence of ‘ever-greening hypothesis’ of bad debts in Indian banking, that is, banks just roll over previous bad debts into fresh performing loans. JEL Classification: G21, G28, C13, C33","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S1 - S34"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47997755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-09DOI: 10.1177/0972652719831550
S. Narend, M. Thenmozhi
We examine the influence of country exchange traded funds (ETFs) on the country’s stock market indices, irrespective of their underlying benchmark. A pooled ordinary least square (OLS) analysis of a sample of 28 India ETFs listed in the US, UK, Canada, France, Japan, Israel and Singapore reveals that India ETFs have a significant impact on the country’s stock indices. We also document reverse causal dynamics between country ETFs and the country’s stock indices. The results are robust even after controlling for global effects, stock market volatility, foreign institutional investor (FII) flows, foreign exchange rate and asset size of India ETFs. The findings of the study have implications for global investors and policymakers in both emerging and developed markets. Policymakers would find it compelling to monitor country ETFs’ fund flows into the underlying country, as withdrawal of country ETFs could have a cascading effect on the economy. JEL Classification: G11, G15, G23
{"title":"Do Country ETFs Influence Foreign Stock Market Index? Evidence from India ETFs","authors":"S. Narend, M. Thenmozhi","doi":"10.1177/0972652719831550","DOIUrl":"https://doi.org/10.1177/0972652719831550","url":null,"abstract":"We examine the influence of country exchange traded funds (ETFs) on the country’s stock market indices, irrespective of their underlying benchmark. A pooled ordinary least square (OLS) analysis of a sample of 28 India ETFs listed in the US, UK, Canada, France, Japan, Israel and Singapore reveals that India ETFs have a significant impact on the country’s stock indices. We also document reverse causal dynamics between country ETFs and the country’s stock indices. The results are robust even after controlling for global effects, stock market volatility, foreign institutional investor (FII) flows, foreign exchange rate and asset size of India ETFs. The findings of the study have implications for global investors and policymakers in both emerging and developed markets. Policymakers would find it compelling to monitor country ETFs’ fund flows into the underlying country, as withdrawal of country ETFs could have a cascading effect on the economy. JEL Classification: G11, G15, G23","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":"18 1","pages":"S59 - S86"},"PeriodicalIF":1.5,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719831550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46747651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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