The forecasting performance of of Cartesian ARIMA search and a vector-valued state space model

The performance of Aoki's state space algorithm and the Cartesian ARIMA sea rch algorithm (CARIMA) of Ostermark and Hog lund is compared. The analysis is carried out on a set of stock prices on the Helsinki (Finland) and Stock holm (Sweden) Stock Exchanges. Demonstrates that the Finnish and Swedish st ock markets differ in predictability of stock prices. With Finnish stock da ta, Aoki's state space algorithm outperforms the subset of MAPE minimizing forecasts. In contrast, with Swedish stock data, ARIMA-models of a fairly s imple structure outperform Aoki's algorithm. The stock markets are seen to differ in complexity of time series models as well as in predictability of individual asset prices.