Particle Filters and Bayesian Inference in Financial Econometrics
In this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space mode...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | Brasil |
| Institución: | Instituição de Ensino Superior e de Pesquisa (INSPER) |
| Repositorio: | Repositório Institucional da INSPER |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.insper.edu.br:11224/4129 |
| Acceso en línea: | https://repositorio.insper.edu.br/handle/11224/4129 |
| Access Level: | acceso abierto |
| Palabra clave: | particle learning sequential Monte Carlo Markov chain Monte Carlo stochastic volatility realized volatility Nelson-Siegel model |
| Sumario: | In this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space model, for which sequential state learning is available in closed form via standard Kalman fi lter and Kalman smoother recursions. Particle fi lters are then introduced as a set of Monte Carlo schemes that enable Kalman-type recursions when normality or linearity or both are abandoned. The seminal bootstrap fi lter (BF) of Gordon, Salmond and Smith (1993) is used to introduce the SMC jargon, potentials and limitations. We also review the literature on parameter learning, an area that started to attract much attention from the particle fi lter community in recent years. We give particular attention to the Liu–West fi lter (2001), Storvik fi lter (2002) and particle learning (PL) of Carvalho, Johannes, Lopes and Polson (2010). We argue that the BF and the auxiliary particle fi lter (APF) of Pitt and Shephard (1999) defi ne two fundamentally distinct directions within the particle fi lter lit erature. We also show that the distinction is more pronounced with parameter learning and argue that PL, which follows the APF direction, is an attractive extension. One of our contributions is to sort out the research from BF to APF (during the 1990s), from APF to now (the 2000s) and from Liu–West fi lter to Storvik fi lter to PL. To this end, we provide code in R for all the examples of the paper. Readers are invited to fi nd their own way into this dynamic and active research arena. |
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