Application of Machine Learning to the Simulated Method of Moments in Financial Agent-Based Models Estimation
| Author: | Bc. Eric Žíla |
|---|---|
| Year: | 2021 - summer |
| Leaders: | PhDr. Jiří Kukačka Ph.D. |
| Consultants: | |
| Work type: | Bachelors |
| Language: | English |
| Pages: | 72 |
| Awards and prizes: | Nomination Deloitte Outstanding Thesis Award. |
| Link: | https://dspace.cuni.cz/handle/20.500.11956/147841 |
| Abstract: | We build a simple machine learning extension on top of the simulated method of moments, an estimation technique commonly applied to analytically intractable complex agent-based models. It offers a new approach to the selection of the moment set. The idea beyond the extension follows the stepwise regression framework. Two algorithms are proposed, the forward stepwise moment selection and the backward stepwise moment elimination. The methodology is tested on four models with increasing complexity. We use three standard financial models to test the elementary functionality of the algorithms and one popular financial agent-based model. We employ three sets of moments from the literature as benchmarks. We find that for every model, both selection processes consistently identify multiple moment sets that outperform all benchmark sets. In line with our expectations, the estimation improvement is minor for the three simple models. However, for the complex agent-based model, for which one needs to deal with moment set selection in practice, we achieve a severe performance boost. |
