Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers
Bibliographic Data
| ID | 19420176 |
|---|---|
| Authors | Jim Q Smith (University of Warwick), António A F Santos, António Alberto Santos (0000-0001-5472-8601, University of Coimbra) |
| Year | 2006 |
| Volume | 24 |
| Issue | 3 |
| Pages | 329-337 |
| Publication date | 2006-07-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1198/073500105000000199 |
| OpenAlex | W2007769761 |
| Language | EN |
| References cited | 19 |
Particle filters are regularly used to obtain the filter distributions associated with state–space financial time series. The most common use today is the auxiliary particle filter (APF) method in conjunction with a first-order Taylor expansion of the log-likelihood. We argue that for series such as stock returns, which exhibit fairly frequent and extreme outliers, filters based on this first-order approximation can easily break down. However, an APF based on the much more rarely used second-order approximation appears to perform well in these circumstances. To detach the issue of algorithm design from problems related to model misspecification and parameter estimation, we demonstrate the lack of robustness of the first-order approximation and the feasibility of a specific second-order approximation using simulated data
Econometrics · Economics · Mathematical analysis · Mathematical optimization · Outlier · Particle filter · Statistics · Taylor series · Time series · Climate variability and models · Computer Science · Financial Risk and Volatility Modeling · Hydrology and Drought Analysis · Mathematics · Applied Mathematics · Finance
| Highly cited | No |
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