With the collapse of the Bretton Woods Agreement and the resulting go-by given to fixed exchange rate system and the consequent increased flow of capital across national boundaries, determination of exchange rate—the price of one currency in terms of another—has become an increasingly complex affair. Exchange rate is supposed to be dependent on the demand and supply of foreign currency. However, the demand for and supply of foreign currency depend on a number of factors such as trade balance between two countries, inflation differentials, relative prices of capital, and volume and velocity of capital movement between the two countries. It is in view of this derived nature of the demand for and supply of foreign currency that the determination of exchange rate has become a complex phenomenon.

Yet, exchange rate movements over a long period cannot be ignored, for they impact these very macro-economic factors and, in turn, the very functioning of the sovereign economy. Indeed economists refer to exchange rates as the single most important price in an open economy that has tremendous influence on the monetary and fiscal decisions of countries across the globe. In view of these economic implications, Central banks often try to predict the exchange rate movement and exercise regulatory control over the exchange rate of domestic currency vis-à-vis the US Dollar and other important major currencies of the world. That aside, exchange rate forecast is a must for corporates who are actively engaged in international trade to hedge themselves against the forex risk.

Driven by these necessities, many have attempted to develop models—autoregressive integrated moving average, generalized autoregressive heteroscedastic models—using time series data for the last two decades to determine exchange rate movement and to model volatility in exchange rates. With regard to usage of historical time series data such as ex-post exchange rates to model exchange rate volatility, there is an argument that past information cannot be used to predict future price movements. Yet, many agencies continue to use ex-post data to predict future price movement, at least in the short run to hedge themselves. Against this backdrop, the authors, Kanchan Datta and Chandan Kumar Mukhopadhyay of the first article of the issue, “RBI Forecast Vs. GARCH-Based ARIMA Forecast for Indian Rupee-US Dollar Exchange Rate: A Comparison”, have made an attempt to estimate the parameters of ARIMA model based on ARCH, GARCH to determine exchange rates and then compared it with the RBI forecast error and the GARCH-based ARIMA forecast. Their study reveals that the ARCH/GARCH model developed by them for Indian Rupee-US dollar exchange rate is a good fit to well capture the volatility clustering. However, it could not provide efficient forecasting performance, compared to ARIMA model. It, however, provided a better forecasting performance than RBI.

Moving on from exchange rates to regulation of financial institutions, we have Neelam Dhanda and Shalu Rani assessing the financial health of Indian banking system in terms of its compliance with ‘Capital’ adequacy norms and presenting their findings in the article, “Basel I and Basel II Norms: Some Empirical Evidence for the Banks in India”. Their study revealed that the average Capital to risk weighted assets ratio of the Indian banking system stood at 10-12% during the period of 1998-99 to 2008-09. The consistency in the ratio as measured by co-efficient of variation was found to be highest in SBI group, while the lowest was noticed among foreign banks. The ANOVA-based analysis showed a significant difference in the capital adequacy ratio of Nationalized Banks, SBI group, Old Private Sector Banks, New Private Sector Banks and Foreign Banks. The study also revealed that the adoption of Basel II norms has not adversely affected the capital adequacy ratio among the Indian Banks.

Moving on to entrepreneurial quality and managerial competence of the prospective borrower makes a lot of difference to the prospects of an enterprise. As lending institutions, banks are highly concerned about the ability of the borrower in making a venture successful, for it is on it that the successful repayment of debt squarely rests. Against this backdrop, Tarak Nath Shaw, the author of the article “A Discriminant Model for Assessing Entrepreneurial Talent: A Case Study of Jharkhand” has developed a discriminant model using research data pertaining to certain identified traits of some ‘successful’ and ‘unsuccessful’ business entrepreneurs to screen the entrepreneurial stature of a prospective borrower hailing from Jharkhand, a state situated in eastern India. The model developed by the author is able to classify 96.2% of the population correctly as ‘successful’ or ‘unsuccessful’ entrepreneurs, and the value of Wilk’s lambda that stood at 0.176 suggests that the discriminatory power of the model is good. The author therefore suggests that the banks can use the model as a tool to assess entrepreneurs before providing financial assistance for picking up the right kind of borrowers.

Finally, we have Weinian Liao and Jagdish Handa, the authors of the last article—“Is the Modern Economy Heading Toward a Cashless and Checkless One? Evidence from the Payments System in Canada”—questioning whether the recent technological innovations in the payments systems of banks are going to eliminate cash and checks as the medium of payment. The authors, using the Canadian data from 1983-2001 and applying Gompertz and the Logistic S-curve growth analysis to model the future replacement of cash payments by non-cash mechanism, have concluded that the current technologies are likely to minimize the usage of checks, but currency will be used to a substantial extent.

-- GRK Murty
Consulting Editor