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Year : 2023, Volume : 68, Issue : 3
First page : ( 1417) Last page : ( 1421)
Print ISSN : 0424-2513. Online ISSN : 0976-4666. Published online : 2023 September 18.
Article DOI : 10.46852/0424-2513.3.2023.9

Prediction of the exchange rate of India considering a causal relationship with crude oil consumption

Pundhir Shiv Kumar Singh1,*, Yadav Seemant Kumar1, Agrawal Anand Mohan2

1Institute of Business Management, GLA University, Mathura, Uttar Pradesh, India

2United University, Prayagraj, Uttar Pradesh, India

*Corresponding author: shiv.pundhir@gla.ac.in (ORCID ID: 0000-0001-6249-6943)

Online Published on 19 January, 2024.

Received:  17  May,  2023; :  19  August,  2023; Accepted:  31  August,  2023.

Abstract

A good prediction of the exchange rate is crucial in the economy. Exchange rate variations highly affect the economy of a country. Over time, so many methods were adopted to predict exchange rates. In this work, data on crude oil consumption and exchange rate from 1992 to 2021 is analyzed. The causal relationship between the exchange rate of India with crude oil consumption of India is tested using the Granger Causality test. To predict the exchange rate of India a causal model is developed. The crude oil consumption of India is predicted for 2020 to 2040 using HoltWinters and ARIMA models. Since the forecast accuracy of the ARIMA model is found good so the ARIMA model is used to predict the crude oil consumption of India. Predicted values of the crude oil consumption of India for the years 2030 and 2040 are found 6.018 and 7.261 million barrels per day respectively. These values are used for the regression model to predict the exchange rate of India. So the exchange rate of India for 2030 and 2040 will be 82.57 and 96.33 US dollars respectively.

Highlights

• A causal relationship is found between the exchange rate and crude oil consumption of India.

• The regression model is developed to predict the exchange rate of India.

• Crude oil consumption is predicted using the ARIMA method.

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Keywords

Exchange Rate, Crude oil consumption, Causal relationship, Forecasting, ARIMA.

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