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Does Financial Frictions Matter for Monetary Policy Transmission in India?

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India’s Contemporary Macroeconomic Themes

Part of the book series: India Studies in Business and Economics ((ISBE))

Abstract

In the context of the adoption of flexible inflation targeting regime in India since 2016, it is necessary to understand the effectiveness of monetary transmission mechanism. The paper investigates if there are any asymmetries in the monetary transmission during different regimes and verifies the role of financial frictions in such asymmetries, if it exists. Using Markov-Switching Vector Autoregression (MS-VAR) models, our results suggest that there are asymmetries in the monetary transmission mechanism during highly volatile and low-volatile regimes with respect to its effects on both output and inflation. It also finds that financial frictions do influence the extent and effectiveness of the policy transmission process in India. From a policy perspective, while the Reserve Bank of India (RBI) may continue to target inflation during high-volatile regimes, it could have output growth as an additional target during the low-volatile regimes.

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Notes

  1. 1.

    The speed and strength of the MTM could vary from country to country depending upon the financial and liquidity conditions, stage of domestic and the global business cycle, fiscal positions and the health of both banking and non-banking sectors.

  2. 2.

    See Sect. 15.2 for more details.

  3. 3.

    Literature has mentioned various factors like less developed and fragmented financial market, financially excluded population, costly intermediation, and policy-driven market distortions for slow and weak MTM (see Acharya, 2017; Banerjee et al., 2018).

  4. 4.

    It is to be noted that these channels are not mutually exclusive and there is considerable feedback and interaction among these various channels.

  5. 5.

    It traces the impact of monetary policy on the supply of bank loanable fund. For example, a contractionary (expansionary) monetary policy can result in a fall (rise) in bank reserves/deposits which will lead banks to reduce (enhance) their credit creation/lending. As a result, investment of the bank-dependent borrowers will fall (rise) which later adversely (favorably) affect the economic activity.

  6. 6.

    It stresses on how shock to monetary policy leads to fall (rise) in the borrower’s financial position in terms of collateral, net worth and cash flow, which hampers(improves) their access to bank credit in the later stage.

  7. 7.

    In addition to it, a risk-taking channel of monetary policy (a part of credit channel) is mentioned in some literature, especially after the global financial crisis, which postulates that low interest rates lead to lending to riskier borrowers and lower risk premiums (Nicolo et al., 2010; Dell'Ariccia et al., 2010).

  8. 8.

    Exception is Banerjee et al. (2018), which studied the issue using DSGE framework (linear framework).

  9. 9.

    The details of these variables are discussed in the next section. Due to insufficient observations and the selected methods, we have not considered the additional variables to capture other channels like exchange rate channel, asset price channel, and expectation channel. This issue will be addressed in our future work.

  10. 10.

    The details of the financial frictions are explained in the next section.

  11. 11.

    This is because of the data on the monthly Weighted Average Lending Rate (WALR) on outstanding loan is only available from February 2012 in the data base of Reserve Bank of India.

  12. 12.

    As the historical data on CPI are not available prior to the year 2011, the same are spliced using the CPI for Industrial Workers (CPI-IW).

  13. 13.

    First, the hidden Markov chain is inferred from the expectation step for a given set of parameters. Then, the parameters are re-estimated in the maximization step. These two steps are repeated until parameters estimates are converged.

  14. 14.

    It depicts the relationship between endogenous variables and fundamental disturbances within a regime. It is conditional on the prevailing regime at the occurrence of shock and on the entire horizon length.

  15. 15.

    An alternative representation is obtained by mean switching specification in which a change in regime leads to an immediate adjustment in the dependent variables to new levels (one-time jump). Thus, given the Indian scenario, Markov-switching intercept specification seems to be more preferable because in it the means approach smoothly new levels after a regime shift.

  16. 16.

    By allowing all parameters to be time-varying leads to an estimation of a large number of parameters depending on the VAR structure, which consequently reduces the number of observations usable for the estimation of the regime dependent parameters.

  17. 17.

    The ordering of variables do matter in the cholesky decompositions while it does not have any relevance in GIRF.

  18. 18.

    We have also performed the simple VAR analysis and the results are almost similar and can be available from authors. We have performed granger causality test to detect more exogenous variables and accordingly the ordering of the VAR is fixed based on the results. The results can be obtained from authors.

  19. 19.

    The results of lag selections can be available from the authors.

  20. 20.

    The results of BDS test confirmed the presence of non-linear structure in the selected series. Thus, this method is applied to check the regime specific behavior of the selected variables.

  21. 21.

    The MS-VAR model is extremely computationally intensive as an increasing the number of lags lead to a substantial increase in parameters. Due to small number of observations, the MS-VAR model with lag 2 cannot estimate the parameters across regimes.

  22. 22.

    After considering financial fricitons1, we have also got similar conclusions regarding the regimes.

  23. 23.

    See Sect. 15.4.2 for their description.

References

  • Acharya, V. V. (2017). Monetary transmission in India: Why is it important and why hasn’t it worked well. Reserve Bank of India Bulletin, 71, 7–16.

    Google Scholar 

  • Aleem, A. (2010). Transmission mechanism of monetary policy in India. Journal of Asian Economics, 21, 186–197.

    Article  Google Scholar 

  • Al-Mashat, R. (2003). Monetary policy transmission in India: Selected issues and statistical appendix. Country Report No. 03/261, International Monetary Fund.

    Google Scholar 

  • Angeloni, I., & Ehrmann, M. (2003). Monetary transmission in the euro area: Early evidence. Economic Policy, 18, 469–501.

    Article  Google Scholar 

  • Aysun, U., Brady, R., & Honig, A. (2013). Financial frictions and the strength of monetary transmission. Journal of International Money and Finance, 32, 1097–1119.

    Article  Google Scholar 

  • Banerjee, S., Behera, H., Bordoloi, S., & Kumar, R. (2018). Role of financial frictions in monetary policy transmission in India (Working Paper (DEPR) 44/2018). Reserve Bank of India.

    Google Scholar 

  • Barnea, E., Landskroner, Y., & Sokoler, M. (2015). Monetary policy and financial stability in a banking economy: Transmission mechanism and policy trade-offs. Journal of Financial Stability, 18, 78–90.

    Article  Google Scholar 

  • Beck, T., Colciago, A., & Pfajfar, D. (2014). The role of financial intermediaries in monetary policy transmission. Journal of Economic Dynamics and Control, 43, 1–11.

    Article  Google Scholar 

  • Bemanke, B., & Gertler, M. (1989). Agency costs, net worth, and business fluctuations. American Economic Review, 79, 14–31.

    Google Scholar 

  • Bhattacharya, R., Patnaik, I., & Shah, A. (2011). Monetary policy transmission in an emerging market setting (Working Paper WP/11/5). International Monetary Fund.

    Google Scholar 

  • Bhaumik, S. K., Dang, V., & Kutan, A. M. (2011). Implications of bank ownership for the credit channel of monetary policy transmission: Evidence from India. Journal of Banking and Finance, 35, 2418–2428.

    Article  Google Scholar 

  • Bhoi, B. K., Mitra, A. K., Singh, J. B., & Sivaramakrishnan, G. (2017). Effectiveness of alternative channels of monetary policy transmission: Some evidence for India. Macroeconomics and Finance in Emerging Market Economies, 10, 19–38.

    Article  Google Scholar 

  • Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235.

    Article  Google Scholar 

  • Brunnermeier, M. K., Eisenbach, T. M., & Sannikov, Y. (2012). Macroeconomics with financial frictions: A survey. National Bureau of Economic Research No. w18102. https://www.nber.org/papers/w18102.pdf

  • Brzoza-brzezina, M., & Kolasa, M. (2013). Bayesian evaluation of DSGE models with financial frictions. Journal of Money, Credit and Banking, 45, 1451–1476.

    Article  Google Scholar 

  • Carlstrom, C. T., & Fuerst, T. S. (1997). Agency costs, net worth, and business fluctuations: A computable general equilibrium analysis. The American Economic Review, 87, 893–910.

    Google Scholar 

  • Carranza, L., Galdon-Sanchez, J. E., & Gomez-Biscarri, J. (2010). Understanding the relationship between financial development and monetary policy. Review of International Economics, 18, 849–864.

    Article  Google Scholar 

  • Cesa-Bianchi, A., Thwaites, G., & Vicondoa, A. (2020). Monetary policy transmission in the United Kingdom: A high frequency identification approach. European Economic Review, 123, 103375.

    Article  Google Scholar 

  • Chang, J. C. D., Huang, C. J., & Chien, I. C. (2014). Cost channel of monetary policy: Financial frictions and external shocks. Emerging Markets Finance and Trade, 50, 138–152.

    Article  Google Scholar 

  • Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. The Quarterly Journal of Economics, 115, 147–180.

    Article  Google Scholar 

  • Cover, J. P. (1992). Asymmetric effects of positive and negative money-supply shocks. The Quarterly Journal of Economics, 107, 1261–1282.

    Article  Google Scholar 

  • Das, M. S. (2015). Monetary policy in India: Transmission to bank interest rates (IMF Working Papers 15/129). International Monetary Fund.

    Google Scholar 

  • Davoodi, M. H. R., Dixit, M. S., & Pinter, G. (2013). Monetary transmission mechanism in the East African Community: An empirical investigation (IMF Working Paper 13–39). Washington. DC.

    Google Scholar 

  • Dell’Ariccia, M. G., Marquez, M. R., Laeven, M. L. (2010). Monetary policy, leverage, and bank risk-taking (IMF Working Papers No. 10/276). International Monetary Fund.

    Google Scholar 

  • Ehrmann, M., Ellison, M., & Valla, N. (2003). Regime-dependent impulse response functions in a Markov-switching vector autoregression model. Economics Letters, 78, 295–299.

    Article  Google Scholar 

  • Elbourne, A., & de Haan, J. (2006). Financial structure and monetary policy transmission in transition countries. Journal of Comparative Economics, 34, 1–23.

    Article  Google Scholar 

  • Elbourne, A., & de Haan, J. (2009). Modelling monetary policy transmission in acceding countries: Vector autoregression versus structural vector autoregression. Emerging Markets Finance and Trade, 45, 4–20.

    Article  Google Scholar 

  • Gertler, M., & Kiyotaki, N. (2010). Financial intermediation and credit policy in business cycle analysis. In Handbook of Monetary Economics, 3, 547–599.

    Article  Google Scholar 

  • Guerrieri, L., & Iacoviello, M. (2017). Collateral constraints and macroeconomic asymmetries. Journal of Monetary Economics, 90, 28–49.

    Article  Google Scholar 

  • Hall, R. E. (2011). The high sensitivity of economic activity to financial frictions. The Economic Journal, 121, 351–378.

    Article  Google Scholar 

  • Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American Economic Review, 95, 739–764.

    Article  Google Scholar 

  • Isakova, A. (2008). Monetary policy efficiency in the economies of Central Asia. Czech Journal of Economics and Finance, 58, 525–553.

    Google Scholar 

  • Kapur, M., & Behera, H. K. (2012). Monetary transmission mechanism in India: A quarterly model (Reserve Bank of India Working Paper No-09).

    Google Scholar 

  • Khundrakpam, J. K. (2011). Credit channel of monetary transmission in India-how effective and long is the lag? (Working Paper (DEPR) 20/2011). Reserve Bank of India.

    Google Scholar 

  • Khundrakpam, J. K., & Jain, R. (2012). Monetary policy transmission in india: a peep inside the black box (RBI Working Paper No. WPS (DEPR): 11/2012).

    Google Scholar 

  • Kim, S. (1999). Do monetary policy shocks matter in the G-7 countries? Using common identifying assumptions about monetary policy across countries. Journal of International Economics, 48, 387–412.

    Article  Google Scholar 

  • Kiyotaki, N., & Moore, J. (1997). Credit cycles. Journal of Political Economy, 105, 211–248.

    Article  Google Scholar 

  • Lombardi, D., Siklos, P. L., & Xie, X. (2018). Monetary policy transmission in systemically important economies and China’s impact. Journal of Asian Economics, 59, 61–79.

    Article  Google Scholar 

  • Lombardo, G., & McAdam, P. (2012). Financial market frictions in a model of the euro area. Economic Modelling, 29, 2460–2485.

    Article  Google Scholar 

  • Lungu, M. (2007). Is there a bank lending channel in southern African banking systems? African Development Review, 19, 432–468.

    Article  Google Scholar 

  • Ma, Y., & Lin, X. (2016). Financial development and the effectiveness of monetary policy. Journal of Banking and Finance, 68, 1–11.

    Article  Google Scholar 

  • Mahathanaseth, I., & Tauer, L. W. (2019). Monetary policy transmission through the bank lending channel in Thailand. Journal of Asian Economics, 60, 14–32.

    Article  Google Scholar 

  • Mishra, P., & Montiel, P. (2013). How effective is monetary transmission in low-income countries? A survey of the empirical evidence. Economic Systems, 37, 187–216.

    Article  Google Scholar 

  • Neaime, S. (2008). Monetary policy transmission and targeting mechanisms in the MENA region. In Economic Research forum working paper (No. 395), Cairo.

    Google Scholar 

  • Nicolo, G.D., Dell'Ariccia, G., Laeven, L., & Valencia, F. (2010). Monetary policy and bank risk taking. IMF Staff Position Notes 2010/09. International Monetary Fund.

    Google Scholar 

  • Ordonez, G. (2013). The asymmetric effects of financial frictions. Journal of Political Economy, 121, 844–895.

    Article  Google Scholar 

  • Ozdagli, A. K. (2018). Financial frictions and the stock price reaction to monetary policy. The Review of Financial Studies, 31, 3895–3936.

    Google Scholar 

  • Palek, J., & Schwanebeck, B. (2017). Financial frictions and optimal stabilization policy in a monetary union. Economic Modelling, 61, 462–477.

    Article  Google Scholar 

  • Pandit, B. L., & Vashisht, P. (2011). Monetary policy and credit demand in India and some EMEs. Indian council for research on international economic relations (No. 256, pp. 1–22). Working Paper.

    Google Scholar 

  • Pandit, B. L., Mittal, A., Roy, M., & Ghosh, S. (2006). Transmission of monetary policy and the bank lending channel: analysis and evidence for India. Development Research Group Study, 25, RBI.

    Google Scholar 

  • Quadrini, V. (2011). Financial frictions in macroeconomic fluctuations. FRB Richmond Economic Quarterly, 97, 209–254.

    Google Scholar 

  • Ramlogan, C. (2007). Analysis of monetary policy in small developing countries: An application to Trinidad and Tobago. The Journal of Developing Areas, 41, 79–91.

    Article  Google Scholar 

  • Reserve Bank of India. (2004). Report on Currency and Finance 2003–04. Reserve Bank of India.

    Google Scholar 

  • Saxegaard, M. (2006). Excess liquidity and effectiveness of monetary policy: Evidence from sub-Saharan Africa (IMF Working Paper 06/115). Washington. D.C.

    Google Scholar 

  • Singh, K., & Kalirajan, K. (2007). Monetary transmission in post-reform India: An evaluation. Journal of the Asia Pacific Economy, 12, 158–187.

    Article  Google Scholar 

  • Ziaei, S.M. (2009). Assess the long run effects of monetary policy on bank lending, foreign asset and liability in MENA countries. MPRA Paper 14331, Munich.

    Google Scholar 

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Acknowledgements

This work is not funded by any agency and was only self-initiated work. The earlier version of the paper appeared as BASE University Working Paper No. 03/2020, Bengaluru, India. Any errors and omissions in the paper are authors’ alone.

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Authors and Affiliations

  1. Xavier Institute of Management, XIM University, Bhubaneswar, India

    Ranjan Kumar Mohanty

  2. Dr. B.R. Ambedkar School of Economics (BASE) University, Bengaluru, India

    N. R. Bhanumurthy

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  1. Ranjan Kumar Mohanty
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Correspondence to N. R. Bhanumurthy .

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    D. K. Srivastava

  2. Madras School of Economics, Chennai, Tamil Nadu, India

    K. R. Shanmugam

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Mohanty, R.K., Bhanumurthy, N.R. (2023). Does Financial Frictions Matter for Monetary Policy Transmission in India?. In: Srivastava, D.K., Shanmugam, K.R. (eds) India’s Contemporary Macroeconomic Themes. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-99-5728-6_15

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