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Lakshmanasamy, T.
- Performance of Commercial Banks in India : DEA Measurement and Determinants of Technical Efficiency
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1 Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
1 Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
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International Journal of Banking, Risk and Insurance, Vol 9, No 2 (2021), Pagination: 42-52Abstract
As commercial entities, commercial banks are expected to maintain profitability in the face of stiff competition, while serving the mandatory government priorities and policy commitments, and central bank regulations. The performance of commercial banks crucially depends on their technical efficiency of realising the full potential output, which the banks invariably do not. This paper measures the technical efficiency of 94 public, private, foreign, and small financial commercial banks in India in 2019 using the data envelopment analysis (DEA) method; the determinants of technical efficiency are analysed by applying the Tobit regression method. The estimated technical efficiency scores of public sector banks are below average and private banks do little better than average, while the technical efficiency of foreign banks varies widely. The Tobit estimates show that capital adequacy and return on assets positively influence technical efficiency, while bank size reduces the technical efficiency of the banks. The managerial quality, bank profitability, and diversification are irrelevant to the technical efficiency levels of commercial banks. The results suggest that the performance of commercial banks in India may be improved by choosing a proper input-output mix and an appropriate scale size.Keywords
Commercial Bank Performance, Potential Output, Technical Efficiency, DEA Estimation, Determinants, Tobit Regression.
References
- Aigner, D. J., & Chu, S. F. (1968). On estimating the industry production function. American Economic Review, 58(4), 826-839.
- Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37.
- Assaf, A. G., Barros, C. P., & Matousek, R. (2011). Technical efficiency in Saudi Banks. Expert Systems with Applications, 38(5), 5781-5786.
- Ataullah, A., & Le, H. (2006). Economic reforms and bank efficiency in developing countries: The case of the Indian banking industry. Applied Financial Economics, 16(9), 653-663.
- Beck, T. (2006). Creating an efficient financial system: Challenges in a global economy. Policy Research Working Paper, World Bank.
- Beck, T., Demirguc-Kunt, A., & Levine, R. (2003). Law, endowments, and finance. Journal of Financial Economics, 70(2), 137-181.
- Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and review. European Journal of Operational Research, 98(2), 175-212.
- Berger, A. N., Leusner, J. H., & Mingo, J. H. (1997). The efficiency of bank branches. Journal of Monetary Economics, 40(1), 141-162.
- Bhattacharyya, A., Lovell, C. A. K., & Sahay, P. (1997). The impact of liberalization on the productive efficiency of Indian commercial banks. European Journal of Operational Research, 98(2), 332-345.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
- Coelli, T., Prasada Rao, D. S., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Springer.
- Dimpy, S. K., & Gulati, R. (2010). Assessing the effect of ownership on the efficiency of Indian domestic banks. IUP Journal of Bank Management, 9(3), 76-104.
- Dwivedi, A. K., & Charyulu, D. K. (2012). Efficiency of Indian banking industry in the post-reform era. International Journal of Business Continuity and Risk Management, 3(3), 234-243.
- Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, 120(3), 253-281.
- Kumar, S. S., & Gulati, R. (2008). An examination of technical, pure technical and scale efficiency in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33-69.
- Lakshmanasamy, T. (2018). Are commercial banks in India technically efficient? A multiple output - multiple input panel stochastic ray frontier function estimation. WEALTH – International Journal of Money, Banking and Finance, 7(3), 4-16.
- Lakshmanasamy, T., & Shanmugam, K. R. (2001). Production frontiers and efficiency measures: An analysis of the banking sector in India. Asian-African Journal of Economics and Econometrics, 1(2), 211-228.
- Lakshmanasamy, T., & Shanmugam, K. R. (2003). Technical efficiency measures for single and multiple output technologies. In P. Thangavel (Ed.), Algorithms and Artificial Systems (pp. 277-295). Chennai: Allied Publishers.
- Meeusen, W., & van den Broeck, V. (1977). Efficiency estimation from Cobb Douglas production function with composed error. International Economic Review, 18(2), 435-444.
- Ray, S. C., & Ram Mohan, T. T. (2004). Comparing performance of public and private sector banks – A revenue maximization efficiency approach. Economic and Political Weekly, 39(12), 127-176.
- San, O. T., Theng, L. Y., & Heng, T. B. (2011). A comparison on efficiency of domestic and foreign banks in Malaysia: A DEA approach. Business Management Dynamics, 1(4), 33-49.
- Sealey, C. W., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. Journal of Finance, 32(4), 1251-1266.
- Singh, D., & Fida, B. (2015). Technical efficiency and its determinants: An empirical study on banking sector of Oman. Problems and Perspectives in Management, 13(1), 168-175.
- Sharma D., Sharma, A. K., & Barua, M. K. (2012). Predicting the probabilities of most and least efficient banks: Data envelopment analysis and logit regression approach. Asia Pacific Journal of Finance and Risk Management, 3(3), 987-1000.
- Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with data envelopment analysis. Journal of Banking and Finance, 9(2), 297-315.
- Timmer, C. P. (1971). Using a probabilistic frontier production function to measure technical efficiency. Journal of Political Economy, 79(4), 776-794.
- Spillover Effects in External and Domestic Markets : GARCH Estimation of Crude Oil Price, Exchange Rate, and Stock Price Volatilities
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Authors
Affiliations
1 Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
1 Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
Source
International Journal of Banking, Risk and Insurance, Vol 10, No 1 (2022), Pagination: 19-28Abstract
Macroeconomic stability is crucial not only for economic growth but also for the living standards and investments in a market economy. The macroeconomic variables like crude oil price, gold price, exchange rate, inflation and stock returns are highly correlated to each other and are highly volatile, and the volatility in one market spills over to other markets. This paper analyses the dynamic causality between crude oil price, exchange rate and BSE Sensex and their volatilities in India. The daily data on macro variables for 14 years between January 2006 to March 2019 is used in the GARCH estimation of causal effects of volatility spillovers. The GARCH estimates show that the volatility and volatility spillover of one market cause volatility and volatility spillovers in other markets in India. The crude oil price and exchange rate volatility and volatility spillovers cause volatility in BSE Sensex. The volatility in BSE Sensex is highly overdone by internal shocks of the stock market itself.Keywords
Oil Price, Exchange Rate, Stock Market, Volatility, Causal Effect, GARCH Estimation.References
- Ali, R., Mangla, I. U., Rehman, R. U., Xue, W., Naseem, M. A., & Ahmad, M. I. (2020). Exchange rate, gold price, and stock market Nexus: A quantile regression approach. Risks, 8(3), 1-16.
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
- Ghosh, S. (2011). Examining crude oil price – Exchange rate Nexus for India during the period of extreme oil price volatility. Applied Energy, 88(5), 1886-1889.
- Hussain, Md. M., Hussain, H., & Atif, M. (2019). Relationship between equity returns, exchange rate and crude oil price: Evidence from India. Think India, 22(4), 7520-7527.
- Jain, A., & Biswal, P. C. (2016). Dynamic linkages among oil price, gold price, exchange rate, and stock market in India. Resources Policy, 49, 179-185.
- Mishra, S., & Debasish, S. S. (2016). Analysis of volatility spillover between oil price and exchange rate in India: GARCH approach. SCMS Journal of Indian Management, 13(4), 95-102.
- Mohanamani, P., Preethi, & Latha, S. L. (2018). Dynamic linkage between gold, oil, exchange rate and stock market returns: Evidence from India. International Journal of Pure and Applied Mathematics, 119(17), 2567-2580.
- Phillips. P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrica, 75(2), 335-346.
- Sahu, T. N., Bandopadhyay, K., & Mondal, D. (2014). Crude oil price, exchange rate and emerging stock market: Evidence from India. Jurnal Pengurusan (UKM Journal of Management), 82, 75-87.
- Sathyanarayana, S. S. (2018). Volatility in crude oil prices and its impact on Indian stock market: Evidence from BSE Sensex. SDMIMD Journal of Management, 9(1), 65-76.
- Sujit, K. S., & Rajesh Kumar, B. (2011). Study on dynamic relationship among gold price, oil price, exchange rate and stock market returns. International Journal of Applied Business and Economic Research, 9(2), 145-165.
- Ugurlu, E. (2014). Modelling volatility: Evidence from the Bucharest Stock Exchange. Journal of Applied Economic Sciences Quarterly, 9(4), 718-720.
- The Effect of FDI on Domestic Investment and Economic Growth: Vector Autoregression Estimation of Causal Effects
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Authors
Affiliations
1 ICSSR Senior Fellow and Formerly Professor, Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
1 ICSSR Senior Fellow and Formerly Professor, Department of Econometrics, University of Madras, Chennai, Tamil Nadu, IN
Source
International Journal of Banking, Risk and Insurance, Vol 10, No 2 (2022), Pagination: 48-60Abstract
There is evidence that foreign direct investment promotes growth in developing economies. At the same time, economic development attracts FDI. Further, FDI inflows may induce investment by national investors. To analyse the effect of FDI inflows on economic growth and domestic investment in developing countries, this paper has applied the vector autoregressive model for five Asian countries – India, Malaysia, Pakistan, Sri Lanka, and Thailand – for the period 1980-2020. In the VAR framework, the relationship between GDP, FDI, exports, infrastructure, and population growth are estimated endogenously by taking two-period lags of each of these variables. The estimated VAR results show that there is a positive impact of FDI on growth in these economies, except Pakistan, and the infrastructure facility is an important factor for attracting FDI. The impact of FDI inflows on domestic investment in India is significantly positive, with a more-than-two-fold increase in investment by the national investors.Keywords
FDI Inflows, Economic Growth, Domestic Investment, Causality, VAR Estimation JEL Classification: B23, C13, C32, E22, F21, F23,F43, G15References
- Agrawal, P., Gokarn, S. V., Mishra, V., Parikh, K. S., & Sen, K. (2000). Policy regimes and industrial competitiveness: A comparative study of South Asia & India. UK: Palgrave Macmillan.
- Agosin, M., & Machado, R. (2005). Foreign investment in developing countries: Does it crowd in domestic investment? Oxford Development Studies, 33(2), 149-162.
- Al Khatib, H. B., Altaleb, G. S., & Alokor, S. M. (2012). Economical determinants of domestic investment. European Scientifific Journal, 8(7), 330-357.
- Banga, R. (2008). Government policies and FDI inflows of Asian developing countries: Empirical evidence. In J. M. Fanelli & L. Squire (Eds.), Economic Reforms in Developing Countries: Reach, Range, Reason (pp. 117-146). Cheltenham: Edward Elgar Publishing.
- Blomstrom, M., & Persson, H. (1983). Foreign investment and spillover efficiency in an underdeveloped economy: Evidence from the Mexican manufacturing industry. World Development, 11(6), 493-501.
- Blonigen, B. A., & Piger, J. (2014). Determinants of foreign direct investment. Canadian Journal of Economics, 47(3), 775-812.
- Borensztein, E., de Gregorio, J., & Lee, J.-W. (1998). How does foreign direct investment affect economic growth. Journal of International Economics, 45(1), 115-135.
- Calderon, C., Loayza, N., & Serven, L. (2004). Greenfield foreign direct investment and mergers and acquisitions - Feedback and macroeconomic effects. Working Paper No. 3192, World Bank.
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.
- Engle, R., & Granger, C. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 35(2), 251-276.
- Fry, M. J. (1993). Foreign direct investment in a macroeconomic framework. Policy Research Working Paper No. 1141, International Economics Department, World Bank. Granger, C. W. (1988). Causality, cointegration, and control. Journal of Economic Dynamics and Control, 12(2-3), 551-559.
- Harms, P., & Meon, P.-G. (2018). Good and useless FDI: The growth effects of greenfifield investment and mergers and acquisitions. Review of International Economics, 26(1), 37-59.
- Harrison, A., & McMillan, M. S. (2003). Does direct foreign investment affect domestic credit constraints? Journal of International Economics, 61(1), 73-100.
- Herzer, D. (2012). How does foreign direct investment really affect developing countries? Review of International Economics, 20(2), 396-414.
- International Monetary Fund. (2005). India: Selected issues. IMF Country Report No: 05/87.
- Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration – With applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169-210.
- Kumar, P. (2012). Impact of FDI on export and growth: An Indian perspective. Management Insight: The Journal of Incisive Analysis, 8(1), 87-92.
- Lautier, M., & Moreaub, F. (2012). Domestic investment and FDI in developing countries: The missing link. Journal of Economic Development, 37(3), 1-23.
- Lumbila, K. N. (2005). What makes FDI work? A panel analysis of the growth effect of FDI in Africa. Africa Region Working Paper Series No. 80, World Bank. Masry, M. (2015). Does FDI really matter in developing countries? The case of Egypt. Research in World Economy, 6(4), 64-77.
- Miankhel, A. K., Thangavelu, S. M., & Kalirajan, K. (2009). Foreign direct investment, exports, and economic growth in South Asia and selected emerging countries: A multivariate VAR analysis. Australia: Crawford School of Public Policy, Australian National University.
- Ram, R., & Zhang, K. H. (2002). Foreign direct investment and economic growth: Evidence from cross-country data for 1990s. Economic Development and Cultural Change, 51(1), 205-215.
- Reddy, D. V., & Mohanty, B. (2007). Impact of foreign direct investment on the gross domestic product, exports and imports of four Asian countries. Delhi Business Review, 8(1), 1-20.
- Singh, J., Chadha, S., & Sharma, A. (2012). Role of FDI in India: An analytical study. Research Inventory: International Journal of Engineering and Research, 1(5), 34-42.
- Thangavelu, S. M., & Rajaguru, G. (2004). Is there an export or import led productivity growth in rapidly developing Asian countries? A multivariate VAR analysis. Applied Economics, 36(10), 1083-1093.