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This study provides a comprehensive overview of the prominent trends and thematic structure of the Journal of Emerging Market Finance ( JEMF). The article uses bibliometric methodology and in doing so, considers measures such as, but not limited to, h-index, annual publications and citation structure, total citations, citation per publication ratio, most productive authors, institutions and countries, and keyword analysis. The thematic structure of the journal is identified using bibliometric coupling analysis of JEMF articles. Findings suggest that there is an increasing trend in JEMF’s count of publication and citation per year. Researchers from India, UK and the USA are frequent contributors to the journal. Issues mostly addressed in the journal include bank penetration, stock price volatility, calendar anomalies, credit default swaps, market efficiency, asset pricing models, and enterprise risk management. This study will be useful for the readers to gain a quick snapshot of the leading trends of the journal and its recent areas of interest. Finally, the study’s findings would aid the editorial team in taking stock of the journal, its past trajectory, and the road ahead, keeping in view contemporary developments in financial markets in general and emerging markets in particular. JEL Codes: G01, G10, G20

Purpose – The purpose of this paper is to first, test for nonlinearity in Local Indian Exchange Traded Funds (ETFs) listed at NSE, India – NIFTYBEES, JUNIORBEES, BANKBEES, PSUBANKBEES, and INFRABEES – using a battery of nonlinearity tests; second, to ascertain, using both metric and topological approaches, the adequacy of appropriate AR-GARCH models when it comes to capturing all of the nonlinearity in Indian ETFs; and third, to test for chaos in Indian ETFs. Design/methodology/approach – To start with, a battery of tests such as and limited to McLeod Li test, Engle's LM test, Tsay F-test, Hinich Bispectrum Test and Hinich Bicorrelation test were employed to test for nonlinearity in Indian ETFs. Subsequently, the nature of nonlinearity in all the ETFs was systematically investigated by subjecting the ETF data sets to a metric (BDS test) and a topological test (close returns tests) at different stages of the model-building process. Finally, Lyapunov Exponent test was employed to test for chaos in Indian ETFs. Findings – Test outcomes pertaining to a battery of nonlinearity tests indicate prevalence of nonlinearity amidst all ETFs except for INFRABEES. BDS test outcomes at the different stages of the model-building process indicated high sensitivity of the test outcomes to choice of embedding dimension, threshold value and residual transformations. Close returns test outcomes indicated that, but for BANKBEES, all of the nonlinearity in Indian ETFs could be captured by appropriate GARCH models. Finally, chaos was found to be absent in any of the ETFs considered for this study. Practical implications – The collective take-way from this study is threefold in nature. First, in light of the many limitations of the BDS test, topological approaches such as close-returns test offer a better avenue to test for adequacy of AR-GARCH models in explaining the nature of nonlinearity in asset price movements. Second, adequacy of AR-GARCH models in capturing all of the nonlinearity in NIFTYBEES, JUNIORBEES, PSUBANKBEES, and INFRABEES, as indicated by close-returns test findings, is a reflection of multiplicative nature of nonlinearity in these five ETFs. Third, persistence of nonlinearity in AR-GARCH filtered standardized residuals of BANKBEES, coupled with the absence of chaos in any of the ETFs considered for this study, brings to light the possibility of existence of additive nonlinearity in conjunction with multiplicative nonlinearity. Originality/value – This is possibly the first study that systematically investigates the nature of nonlinearity in Indian ETFs and ascertains the adequacy of AR-GARCH models when it comes to capturing all of the nonlinearity in Indian ETFs using a topological approach.

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