Exchange rate forecasting using the ARIMA Model -India (original) (raw)
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An ARIMA analysis of the Indian Rupee/USD exchange rate in India
2019
This study uses annual time series data on the Indian Rupee / USD exchange rate from 1960 to 2017, to model and forecast exchange rates using the Box-Jenkins ARIMA technique. Diagnostic tests indicate that R is an I (1) variable. Based on Theil’s U, the study presents the ARIMA (0, 1, 6) model, the diagnostic tests further show that this model is quite stable and hence acceptable for forecasting the Indian Rupee / USD exchange rates. The selected optimal model the ARIMA (0, 1, 6) model shows that the Indian Rupee / USD exchange rate will appreciate over the period 2018 – 2022, after which it will depreciate slightly until 2027. The main policy prescription emanating from this study is that the Reserve Bank of India (RBI) should devalue the Rupee, firstly to restore the much needed exchange rate stability, secondly to encourage local manufacturing and thirdly to promote foreign capital inflows.
FORECASTING FOREIGN EXCHANGE (FOREX) RATES OF DIFFERENT COUNTRIES BASED ON THE CURRENCY OF INDIA
International Journal of All Research Education and Scientific Methods (IJARESM), 2021
In this paper the work contributed that forecasting the exchange rates between the behavior of daily exchange rates of USD American dollar, EUR European euro and AUD Australian currency with INR Indian currency data taken from 15 th May 2011-13 th May 2021 analyzed is collected from the official website (http://www.rbi.org.in) of Reserve Bank of India (RBI). This paper attempts to examine the performance of ARIMA model in forecasting the currencies traded in Indian foreign exchange markets by using ACF and PACF. Study the Forecasting value of next 5 years exchange rates of USD, EUR and AUD with based on INR currencies data from that investigates the behavior of daily exchange rates between USD/INR, EUR/INR and AUD/INR.
2021
The primary purpose of the study is to forecast the exchange rate of Indian Rupees against the US Dollar by combining the three univariate time series models i.e., ARMA/ARIMA, exponential smoothing model, Naïve and one non-linear multivariate model i.e., NARDL. For this purpose, the authors choose the monthly data of exchange rate and macro-economic fundamentals i.e., trade balance, federal reserves, money supply, GDP, inflation rate and interest rate over the period from January 2011 to December 2020. The data from January 2020 to December 2020 are held back for the purpose of in-sample forecasting. By applying all the models individually and combinedly, the NARDL model out performs other individual and combined models with the least MAPE value of 0.6653. It is the evidence that the Indian Rupee may forecast through non-linear analysis of macroeconomic fundamentals rather than single univariate models. The findings will be beneficial for the policy makers, FOREX market, traders, to...
International Journal of Research in Finance and Management, 2024
Prediction of exchange rates is an important task of traders and practitioners in the recent financial markets era. Many statistical and econometric models are used in the time series analysis and prediction of foreign exchange rates. This study investigates the behavior of weekly exchange rates of the Indian rupee (INR) against the US dollar (USD) using time series analysis. This study used the SARIMA model in forecasting the INR/USD by aggregating seasonal data patterns in the foreign exchange market. Weekly RBI reference exchange rates from June 2013 to June 2023 were considered for the analysis. Finally, a forecast for ninety days was calculated which showed a depreciation of the Indian rupee against the US Dollar. The study found that the SARIMA model can be a better forecasting ability in advance of the prediction of currency exchange rates and momentum in the currency market.
This paper examines the relevance of Covered Interest Rate Arbitrage Parity (CIRAP) doctrine in Indian foreign exchange (rupee/dollar) market and its 'efficiency' over the period 27 th April, 2012-27 th February, 2015. Univariate stochastic structure of weekly spot rate has been captured by ARIMA (1, 1, 0) process. This stochastic structure has been used to generate one-period ahead forecast () and four-period ahead forecast series. These forecasts are MMSE forecasts and 'Rational' by nature. Forward rates () is found to serve as the 'Unbiased predictor' of the spot rate implying that CIRAP does hold good in the market. Again absence of 'risk premium' testifies for the 'efficiency' of the Indian foreign exchange (rupee / dollar) market over the period of study.
Forecasting the exchange rates is both a challenging and important task for the modern traders, people working in the financial markets and general population across the globe. In this paper we will be utilizing the time series concepts to do an analysis and predict the daily exchange rates of the Indian Rupee (INR) against the United States Dollar (USD). This paper will investigate and compare different forecasting techniques like ARIMA, Holt-Winters simple exponential smoothing and Neural networks. Further, utilizing the above techniques investigate the behavior of daily exchange rates of the Indian Rupee (INR) against the United States Dollar. (Daily exchange rates from 19th November 2007 to 18th December 2017 were used for the analysis [1].
APPLICATION OF TIME SERIES MODELS IN FORECASTING EXCHANGE RATE(2).docx
The purpose of this study is to evaluate the impact of time series models in forecasting exchange rate. The study determined the time series model that is reliable for predicting the exchange rates of foreign currencies like Dollar, Great Britain Pound and Japanese Yen in Nigeria Naira by determining some salient features about the exchange rate data. The model performance indices were calculated and the graphical implications were also displayed. Together, this study has shown that the linear model and exponential smoothing model at any level of damping factor are not suitable for predicting the exchange rate of foreign currencies like Dollar Great Britain Pound and Japanese Yen in Nigeria Naira. While Naive Model and 5-PD Moving Average are the best for forecasting these but the choice of selection still depends on the model performance index put into use.