Suchergebnisse

AbstractUsing the spectral regression method, we test for long‐term stochastic memory in three‐ and six‐month daily returns series of Eurocurrency deposits denominated in major currencies. Significant evidence of positive long‐term dependence is found in several Eurocurrency returns series. Compared with benchmark linear models, the estimated fractional models result in dramatic out‐of‐sample forecasting improvements over longer horizons for the Eurocurrency deposits denominated in German marks, Swiss francs, and Japanese yen.

AbstractWe employ a nonlinear, nonparametric method to model the stochastic behavior of changes in the 90‐day U.S. T‐bill rate. The estimation technique is locally weighted regression (LWR), a nearest‐neighbor method, and the forecasting criteria are the root mean square error (RMSE) and mean absolute deviation (MAD) measures. We compare the forecasting performance of the nonparametric fit to the performance of two benchmark linear models: an autoregressive model and a random‐walk‐with‐drift model. The nonparametric fit results in significant improvements in forecasting accuracy as compared to benchmark linear models both in‐sample and out‐of‐sample, thus establishing the presence of substantial nonlinear mean predictability in the 90‐day T‐bill rate.