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A father-son team, both chartered accountants, have hit upon a statistical and mathematical model based on some key macro-economic factors of the United States and India that can forecast the rupee-dollar exchange rate with a great degree of accuracy, according to Vinay Kshirsagar, chief financial officer, Indian Register of Shipping, a ship classification society.
Vinay Kshirsagar and his son Omkar have picked seven quantifiable factors such as interest rate, inflation, growth rate, current account deficit (CAD) as a percentage of gross domestic product (GDP) and crude oil consumption, per capita income and foreign direct investment (FDI) as a percentage of GDP to develop a rupee-dollar forecasting model, which is often considered tricky by corporates, exporters and importers.
Each economic factor, says Vinay Kshirsagar, has a direct or inverse relationship with the exchange rate.
“We have plotted all the seven macro-economic factors from 1980 to 2016 and calculated their differentials and worked out a model from which mathematical exchange rate is calculated from 1980 to 2016,” said Vinay.
Regression
The data obtained from the mathematical model was used to perform regression and to compare results.
“The overall workability of regression model comes to 93 per cent correlation, and 90 per cent dependency on the exchange rate was observed. We forecast exchange rates of 2016 and 2017 on the basis of this model in 2015 which came true,” he claimed.
Based on this model, the Kshirsagars predict that the rupee-dollar exchange rate for 2018 will be in the range of ₹63-65 to the dollar.
The due converted their findings into a book in 2015, which was updated last year by refining the model using advanced statistical techniques such as exponential smoothing and moving average methods. Exponential smoothing is used for making some determination based on prior assumptions by the user, such as seasonality.
A moving average is a calculation to analyse data points by creating series of averages of different subsets of the full data set. The original model, along with results using the two new methods, was regressed again (by updating data up to 2016), which exhibited 96 per cent correlation with the exchange rate and 93 per cent dependency with standard error of about 3.5 per cent, explained Vinay.
Who will benefit?
The Kshirsagars’ reckon that their exchange-rate forecasting tool could help corporates, importers, exporters and others who have foreign currency exposure to decide appropriate hedging strategies and products to mitigate risks in a competitive business scenario.
Besides, it will help corporates plan strategy for long/short-term foreign currency borrowings, added Vinay.
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