No abstract

This study applies the Iteratively Weighted Least Squares (IWLS) algorithm to a Smooth Transition Autoregressive (STAR) model with conditional variance. Monte Carlo simulations are performed to measure the performance of the algorithm, to compare its performance with the performances of established methods in the literature, and to see the effect of initial value selection method. Simulation results show that low bias and mean squared error are received for the slope parameter estimator from the IWLS algorithm when the real value of the slope parameter is low. In an empirical illustration, STAR-GARCH model is used to forecast daily US Dollar/Australian Dollar and FTSE Small Cap index returns. 1-day ahead out-of-sample forecast results show that forecast performance of the STAR-GARCH model improves with the IWLS algorithm and the model performs better that the benchmark model.

This study considers international reserve management motivation of emerging market central banks in foreign exchange market interventions. Emerging market central banks use currency intervention as a policy tool against exchange rate movements and accumulate international reserves as an insurance against sudden-stops in capital flows. To account for both of these motivations, a model of infrequent interventions only with exchange rates is extended to include international reserves-to-gross domestic product (GDP) ratio at the daily frequency. Daily values of the ratio are forecast using the Mixed Data Sampling (MIDAS) model and exchange rate returns. The model is estimated by using the floating exchange rate regime period data of Turkey. Compared with the benchmark model, it is shown that the MIDAS model does a better job in the forecasting of the reserve-to-GDP ratio. In addition to that, there are breaks in the interventions policy in Turkey, and the extended intervention model performs better than the model only with exchange rates especially in predicting purchases of US Dollar.

No abstract

No abstract