The exchange rate is time series data that unstable complex and difficult to predict In tradition the forecasting in time series data is to use statistical method Generally speaking autoregressive integrated moving-average (ARIMA) model for forecasting in linear data is quite good Hence we use the sample data to establish the ARIMA model at first and derive the linear predictive values The mathematical financial model Cox-Ingersoll-Ross (CIR) model also be used to predict the exchange rate through the uncover interest rate parity (UIRP) Therefore second we use STRIPS bonds of U S and Japan to obtain the estimated CIR models to predict the exchange rate in our sample period Jan 2 2012 to Mar 30 2012 We use the moving window method to generate the estimated exchange rates Finally in order to measure the predictive power we calculate the root mean square error (RMSE) mean absolute error (MAE) and mean absolute percentage error (MAPE) of the forecasting models The empirical results show that the predictive power of the CIR model is significantly better than traditional ARIMA model

Date of Award | 2014 Aug 29 |
---|

Original language | English |
---|

Supervisor | Tse-Shih Wang (Supervisor) |
---|

Is Cox-Ingersoll-Ross Model a Good Predictor for Future U S /Japan Exchange Rate Movement?

雅芳, 林. (Author). 2014 Aug 29

Student thesis: Master's Thesis