Relationship banking and the credit market in India: An empirical analysis

Relationship banking based on Okun's "customer credit markets" has important implications for monetary policy via the credit transmission channel. Studies of LDC credit markets from this point of view seem to be scanty and this paper attempts to address this lacuna. Relationship banking implies short-term disequilibrium in credit markets, suggesting the VECM (vector error-correction model) as an appropriate framework for analysis. We develop VECM models in the Indian context (for the period April 1991- December 2004 using monthly data) to analyse salient features of the credit market. An analysis of the ECMs (error-correction mechanisms) reveals that disequilibrium in the Indian credit market is adjusted via demand responses rather than supply responses, which is in accordance with the customer view of credit markets. Further light on the working of the model is obtained through the "generalized" impulse responses and "generalized" error decompositions (both of which are independent of the variable ordering). Our conclusions point towards firms using short-term credit as a liquidity buffer. This fact, together with the gradual adjustment exhibited by the "persistence profiles" provides substantive evidence in favour of "customer credit markets".customer credit markets, monetary policy, co-integration, impulse response, persistence profiles

"RELATIONSHIP BANKING" AND THE CREDIT MARKET IN INDIA : AN EMPIRICAL ANALYSIS

Relationship banking based on Okun's "customer credit markets" has important implications for monetary policy via the credit transmission channel. Studies of LDC credit markets from this point of view seem to be scanty and this paper attempts to address this lacuna. Relationship banking implies short-term disequilibrium in credit markets, suggesting the VECM (vector error-correction model) as an appropriate framework for analysis. We develop VECM models in the Indian context (for the period April 1991- December 2004 using monthly data) to analyse salient features of the credit market. An analysis of the ECMs (error-correction mechanisms) reveals that disequilibrium in the Indian credit market is adjusted via demand responses rather than supply responses, which is in accordance with the customer view of credit markets. Further light on the working of the model is obtained through the "generalized" impulse responses and "generalized" error decompositions (both of which are independent of the variable ordering). Our conclusions point towards firms using short-term credit as a liquidity buffer. This fact, together with the gradual adjustment exhibited by the "persistence profiles" provides substantive evidence in favour of "customer credit markets".

Forecasting interest rates: A Comparative assessment of some second generation non-linear model

Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success. We examine here four models which account for several specific features of real world asset prices such as non-stationarity and non-linearity. Our four candidate models are based respectively on wavelet analysis, mixed spectrum analysis, non-linear ARMA models with Fourier coefficients, and the Kalman filter. These models are applied to weekly data on interest rates in India, and their forecasting performance is evaluated vis-…-vis three GARCH models (GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)) as well as the random walk model. The Kalman filter model emerges at the top, with wavelet and mixed spectrum models also showing considerable promise.Interest rates, wavelets, mixed spectra, non-linear ARMA, Kalman filter, GARCH, Forecast encompassing

FORECASTING INTEREST RATES - A COMPARATIVE ASSESSMENT OF SOME SECOND GENERATION NON-LINEAR MODELS

Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success. We examine here four models which account for several specific features of real world asset prices such as non-stationarity and non-linearity. Our four candidate models are based respectively on wavelet analysis, mixed spectrum analysis, non-linear ARMA models with Fourier coefficients, and the Kalman filter. These models are applied to weekly data on interest rates in India, and their forecasting performance is evaluated vis--vis three GARCH models (GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)) as well as the random walk model. The Kalman filter model emerges at the top, with wavelet and mixed spectrum models also showing considerable promise.interest rates, wavelets, mixed spectra, non-linear ARMA, Kalman filter, GARCH, Forecast encompassing.

Financial liberalisation and monetary policy : the Indian evidence

Digitised version produced by the EUI Library and made available online in 2020

Measuring variability of monetary policy lags : a frequency domain approach

Digitised version produced by the EUI Library and made available online in 2020