This 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.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/10127 |
| Date | January 1996 |
| Creators | McGonigal, Denis. |
| Contributors | Ionescu, Dan, |
| Publisher | University of Ottawa (Canada) |
| Source Sets | Université d’Ottawa |
| Detected Language | English |
| Type | Thesis |
| Format | 121 p. |
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