Ionescu, Dan,McGonigal, Denis.2009-03-252009-03-2519961996Source: Masters Abstracts International, Volume: 35-05, page: 1521.9780612164253http://hdl.handle.net/10393/10127http://dx.doi.org/10.20381/ruor-8136This thesis describes a first experimental project using a recursive parameter estimation and Kalman filter approach to on-line modelling and prediction of stock market time-series. On-line (real-time) and daily closing price stock data are identified as Box-Jenkins ARIMA models. Differencing is performed to obtain a locally wide sense stationary process which is identified through spectral estimation methods. The initial model parameters are updated on-line via the Recursive Prediction Error algorithm and predictions are performed using the Kalman filter. This approach is studied and compared to the traditional Box-Jenkins SISO approach. The daily stock processes are also modeled as autoregressive processes embedded in white noise, which make an ideal investigation for the Kalman filter.121 p.Economics, Finance.Thesis