Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan

TL;DR

This paper applies ARIMA time series models to forecast yearly USD/KZT, EUR/KZT, and SGD/KZT exchange rates from 2006 to 2014, evaluating accuracy with MAE, MAPE, and RMSE.

Contribution

It demonstrates the use of ARIMA models for exchange rate forecasting specific to Kazakhstan's currency, with performance evaluation metrics.

Findings

ARIMA models provided accurate exchange rate forecasts.

Forecast errors were quantified using MAE, MAPE, and RMSE.

The approach offers a reliable method for currency rate prediction.

Abstract

This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper is to apply the ARIMA model for forecasting of yearly exchange rates of USD/KZT, EUR/KZT and SGD/KZT. The accuracy of the forecast is compared with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE).