Pub Date : 2019-06-21DOI: 10.1177/0972652719846321
Suparna Pal, A. Chattopadhyay
The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multivariate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17
{"title":"‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects","authors":"Suparna Pal, A. Chattopadhyay","doi":"10.1177/0972652719846321","DOIUrl":"https://doi.org/10.1177/0972652719846321","url":null,"abstract":"The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multivariate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43480368","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 : 2019-06-18DOI: 10.1177/0972652719846312
Alexander Lubis, Constantinos Alexiou, J. Nellis
In this article, the relationship between innovations in the payment systems and financial intermediation is explored. By focusing on excess reserves and currency demand we provide evidence on the extant transmission mechanism. In this direction, a generalised method of moments (GMM) and vector error correction model (VECM) techniques are applied to a data set collated for Indonesia. We find that financial intermediation is affected by currency demand while we observe a limited role of excess reserves affecting financial intermediation. Credit card payments are found to have a statistically significant effect on currency demand, whereas debit card payments only influence financial intermediation in the long run. In addition, the real-time gross settlement (RTGS) exerts an upward pressure on excess reserves. The findings are of great importance as they provide support to policies that favour payment migration to an electronic platform, particularly that of card-based payment systems. JEL Classification: E42, E58, N25, G21
{"title":"Gauging the Impact of Payment System Innovations on Financial Intermediation: Novel Empirical Evidence from Indonesia","authors":"Alexander Lubis, Constantinos Alexiou, J. Nellis","doi":"10.1177/0972652719846312","DOIUrl":"https://doi.org/10.1177/0972652719846312","url":null,"abstract":"In this article, the relationship between innovations in the payment systems and financial intermediation is explored. By focusing on excess reserves and currency demand we provide evidence on the extant transmission mechanism. In this direction, a generalised method of moments (GMM) and vector error correction model (VECM) techniques are applied to a data set collated for Indonesia. We find that financial intermediation is affected by currency demand while we observe a limited role of excess reserves affecting financial intermediation. Credit card payments are found to have a statistically significant effect on currency demand, whereas debit card payments only influence financial intermediation in the long run. In addition, the real-time gross settlement (RTGS) exerts an upward pressure on excess reserves. The findings are of great importance as they provide support to policies that favour payment migration to an electronic platform, particularly that of card-based payment systems. JEL Classification: E42, E58, N25, G21","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41690691","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 : 2019-06-18DOI: 10.1177/0972652719846354
Shashank Bansal, M. Thenmozhi
This study examines the resource dependency and signalling role of independent directors from the perspective of institutional investor’s and also investigates if the presence of large blockholder moderates the signalling effect. This study uses the quasi-natural experiment to examine this relationship. The difference-in-difference (DiD) analysis of 5,298 firm observations covering 618 National Stock Exchange (NSE) listed Indian firms for the period 2001–2011 provides empirical evidence that board composition does matter to institutional investors. We find that non-compliant firms who adopted the board independence requirement experience a significant increase in institutional ownership relative to previously compliant firms. We also find that institutional investors have invested more in family-owned firms during post-mandate period compared to government-, private- and foreign-owned firms. Overall, this study contributes to the existing literature on resource dependency theory and signalling theory and shows that the board independence acts as a signal to institutional investors and decreases the agency cost and cost of monitoring. JEL Codes: G3, G11, G34, G38, G23
{"title":"Does Board Composition Matter to Institutional Investors?","authors":"Shashank Bansal, M. Thenmozhi","doi":"10.1177/0972652719846354","DOIUrl":"https://doi.org/10.1177/0972652719846354","url":null,"abstract":"This study examines the resource dependency and signalling role of independent directors from the perspective of institutional investor’s and also investigates if the presence of large blockholder moderates the signalling effect. This study uses the quasi-natural experiment to examine this relationship. The difference-in-difference (DiD) analysis of 5,298 firm observations covering 618 National Stock Exchange (NSE) listed Indian firms for the period 2001–2011 provides empirical evidence that board composition does matter to institutional investors. We find that non-compliant firms who adopted the board independence requirement experience a significant increase in institutional ownership relative to previously compliant firms. We also find that institutional investors have invested more in family-owned firms during post-mandate period compared to government-, private- and foreign-owned firms. Overall, this study contributes to the existing literature on resource dependency theory and signalling theory and shows that the board independence acts as a signal to institutional investors and decreases the agency cost and cost of monitoring. JEL Codes: G3, G11, G34, G38, G23","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48789937","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 : 2019-06-11DOI: 10.1177/0972652719846348
S. G. Deb
This article analyses downside risk of Indian equity mutual funds from 1999 to 2014 using a value at risk (VaR)-based approach. We use weekly return data of a sample of 349 equity mutual funds during the said period to estimate their weekly VaRs on a rolling basis using some parametric and non-parametric models. Moving average (MA), exponentially weighted MA and GARCH (1, 1) are the parametric models and historical simulation (HS) is the non-parametric model. We also carry out backtesting of the models using three popular approaches—two under the unconditional coverage approach, namely Jorion’s ‘Failure Rate’ approach and Kupiec’s proportion of ‘failures’ (POF) test, and one under the conditional coverage approach, namely the Christoffersen’s Independence test—to test the robustness of the VaR models. Our results show that Indian equity mutual funds exhibit considerable downside risk during the chosen period, in terms of the magnitude of the projected VaRs. Moreover, significant proportions of the funds ‘fail’ the predicted VaRs, particularly during times of crisis for some of the models, raising questions about their robustness in an investment setting in India. On the whole, both from failure proportion as well as backtesting perspective, the GARCH (1,1) seems to be the most robust of the models. JEL codes: G32, G15, G23
{"title":"A VaR-based Downside Risk Analysis of Indian Equity Mutual Funds in the Pre- and Post-global Financial Crisis Periods","authors":"S. G. Deb","doi":"10.1177/0972652719846348","DOIUrl":"https://doi.org/10.1177/0972652719846348","url":null,"abstract":"This article analyses downside risk of Indian equity mutual funds from 1999 to 2014 using a value at risk (VaR)-based approach. We use weekly return data of a sample of 349 equity mutual funds during the said period to estimate their weekly VaRs on a rolling basis using some parametric and non-parametric models. Moving average (MA), exponentially weighted MA and GARCH (1, 1) are the parametric models and historical simulation (HS) is the non-parametric model. We also carry out backtesting of the models using three popular approaches—two under the unconditional coverage approach, namely Jorion’s ‘Failure Rate’ approach and Kupiec’s proportion of ‘failures’ (POF) test, and one under the conditional coverage approach, namely the Christoffersen’s Independence test—to test the robustness of the VaR models. Our results show that Indian equity mutual funds exhibit considerable downside risk during the chosen period, in terms of the magnitude of the projected VaRs. Moreover, significant proportions of the funds ‘fail’ the predicted VaRs, particularly during times of crisis for some of the models, raising questions about their robustness in an investment setting in India. On the whole, both from failure proportion as well as backtesting perspective, the GARCH (1,1) seems to be the most robust of the models. JEL codes: G32, G15, G23","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47337750","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 : 2019-06-11DOI: 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：