Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.
Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.
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