Name
Abstract | ||
|---|---|---|
The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account for this, a Bayesian framework for the estimation of conditional copulas is proposed. In this framework the parameters of a copula are non-linearly related to some arbitrary conditioning variables. We evaluate the ability of our method to predict time-varying dependencies on several equities and currencies and observe consistent performance gains compared to static copula models and other time-varying copula methods. |
Year | Venue | Field |
|---|---|---|
2013 | NIPS | Econometrics,Covariate,Mathematical optimization,Copula (linguistics),Computer science,Copula (probability theory),Gaussian process,Finance,Bayesian probability |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Miguel Hernández-Lobato | 1 | 613 | 49.06 |
| James Robert Lloyd | 2 | 103 | 6.43 |
| Daniel Hernández-Lobato | 3 | 440 | 26.10 |
| Hernández-Lobato, José Miguel | 4 | 1 | 0.36 |