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An object-oriented, maximum-likelihood parameter estimation program for GARCH(p,q)

This thesis studies the approach of object modeling when applied to the compute-intensive problem of maximum-likelihood estimation, a problem that has generally been tackled using functional approaches. Specifically, this research takes the problem of finding maximumlikelihood parameter estimates for an econometric model called Generalized Autoregressive Conditional Heteroskedasticity, and develops an object model for it. What results is an object model that clearly lays out the nature of the information in the problem, showing that there is complexity in its structure. This complexity consequently expresses a deficiency in a functional approach that assumes that the capture and expression of information can be easily solved, and that effort is therefore better spent focusing on the process. This thesis argues the opposing view and states that most of the complexity in a program is in its data, and that the best way to manage this complexity is through the design and implementation of an object model. Thesis Supervisor: Daniel Jackson Title: Associate Professor, Electrical Engineering and Computer Science