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The purpose of this paper is to understand the determinants of financial inclusion and the determinants of barriers to financial inclusion in India. Also, the purpose is…
The purpose of this paper is to understand the determinants of financial inclusion and the determinants of barriers to financial inclusion in India. Also, the purpose is to ascertain the determinants of informal financial activities in India.
The data have been collected from the Global Findex Database (Findex) 2017. Various measures of financial inclusion, namely, ownership formal accounts, use of accounts for saving and borrowing, ownership and use of the debit card are used. The independent variables used are: age, income, education and gender. Given the binary nature of dependent variables, this paper uses the Probit model to draw the inferences.
The results show that gender, age, education and income have a significant impact on the various measures of financial inclusion. Additionally, these factors have a significant impact on the informal saving and borrowing.
The given study uses the deferent measures of financial inclusion. An index of financial inclusion created using all the financial inclusion measures would be a better indicator of financial inclusion.
The results of this study would be useful for policymakers to identify the determinants and barriers of financial inclusion in India. The results show that policymakers should focus on the female population, in particular, and education and income enhancing measures, in general, to make financial inclusion more inclusive.
The study is the first of its kind to analyze financial inclusion in India using the Findex. Unlike previous studies, variables such as education and income are constructed more pragmatically. In particular, the study tries to understand the socio-economic determinants of financial inclusion measured as ownership of formal accounts, formal saving, formal credit, ownership of debit cards and use of debit cards. The study also analyzes the determinants of barriers to financial inclusion, savings (formal and informal) and borrowing (formal and informal).
The purpose of this paper is to estimate the relationship between stock prices and exchange rates of eight Asian countries. The analysis is based on methodologies that…
The purpose of this paper is to estimate the relationship between stock prices and exchange rates of eight Asian countries. The analysis is based on methodologies that possess the ability to provide a complete representation of data series from both time and frequency perspectives simultaneously. In addition, instead of limiting the analysis to focus on the conditional mean of the response variable y in the regression equation, the authors investigate the extremes of distribution to reveal a range of hidden relationships between these variables.
Given the limitations of classical methodology of Pearson correlation and least-squares regression, this study estimates the relationship between stock prices and exchange rates through wavelet correlation and cross-correlation to serve as a protocol for different traders who view the market with different time resolutions. In addition, quantile regression technique robust to heteroscedasticity, skewness and leptokurtosis is used to understand the relationship between stock prices and a specified quantile of the exchange rates.
In accordance with the portfolio balance effect, it is observed that stock prices and exchange rates are negatively correlated at all frequencies. In particular, the negative correlation grows with higher time scales (lower frequency intervals). The findings from quantile regression also suggest that the coefficients are more inclined to be negative when exchange rates are extremely high.
The paper contributes to the literature by focussing on the multi-scale relationship between stock prices and exchange rates. In addition, it also analyzes the relationship between stock prices and a specified quantile of the exchange rates.