Forecasting the INR/USD Exchange Rate: A BVAR Framework

Pami Dua, Rajiv Ranjan () and Deepika Goel ()
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Rajiv Ranjan: Reserve Bank of India
Deepika Goel: Aryabhatta College, University of Delhi

Chapter Chapter 8 in Macroeconometric Methods, 2023, pp 183-224 from Springer

Abstract: Abstract This paper uses vector autoregression and Bayesian vector autoregression techniques to forecast the Indian Re/US dollar exchange rate. It extends the Dua and Ranjan (2010, 2012) model by including the domestic–foreign differential of the rate of return in stock prices as well as global oil prices as determinants of the exchange rate in addition to monetary model fundamentals (i.e. differential in money supply, interest rate and inflation), forward premium, volatility of capital flows, order flows and central bank intervention. The estimation period is July 1996–January 2017, while an analysis of the out-of-sample forecasting performance is undertaken from February 2017 to January 2019. The main findings are as follows: (i) Granger causality tests reveal that the exchange rate is granger caused by all the determinants considered, including differential of the rate of return of stock prices and global oil prices. (ii) Forecast accuracy of the extended model that includes stock market information and global oil prices is somewhat better than Dua and Ranjan (2010, 2012) model, especially at the longer end. (iii) Bayesian vector autoregressive models generally outperform their corresponding VAR variants. (iv) Turning points are difficult to predict.

Keywords: Exchange rate; VAR and BVAR models; Forecasting; Stock price differential; Oil prices (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 F31 F47 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/978-981-19-7592-9_8

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