Traditionally automated quantitative financial modelling faced great challenges with respect to computation, implementation and maintenance, since most financial models are highly non-linear and thus require rocket science skills, substantial effort and advanced technology. Recently, however, a methodology was proposed and developed in a series of papers which overcomes many of the computational challenges earlier associated with automated financial volatility modelling. Moreover, softwares (for example PcG ets) in which the methodology can be implemented in an automated manner is already available, so that only minor modifications and additions to the methodology are necessary before it can be used in automated financial modelling.
The software PcGets implements an econometric methodology called general-to-specific (GETS) modelling, thus the name PcGets, and the modelling framework is sometimes referred to as LSE econometrics after the academic institution (London School of Economics and Political Science) in which it originated. More recently though the approach has become widely associated with David F. Hendry at the University of Oxford. In brief, the methodology consists of starting with a general model that adequately characterises the data, and then simplifying it while paying careful attention to the model properties.
The methodology provides a systematic framework for statistical economic hypothesis testing, model development and model evaluation, and the methodology is popular among large- scale econometric model developers. However, it is unused for the purpose of financial econometric modelling. The aim of the project Automated Financial Modelling is to implement and evaluate the usefulness of automated valute-at-risk modelling and automated derivative price modelling within a general-to-specific modelling framework. The main evaluation criterion of the methodology will be its forecast accuracy compared with alternative approaches.
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