Forecasting the Nikkei Spot Index with Fractional Cointegration - TSE, Yiu Kuen (Institutional Knowledge at Singapore Management University, 1999)
The forecast performance of the fractionally integrated error correction model is investigated against several competing models for the prediction of the Nikkei stock average index. The competing models include the martingale model, the vector autoregressive model and the conventional error correction model. Models are considered with and without conditional heteroscedasticity. For forecast horizons of over twenty days, the best forecasting performance is obtained for the model when fractional cointegration is combined with conditional heteroscedasticity. The results reinforce the notion that cointegration and fractional cointegration are important for long-horizon prediction.