Nonparametric Bayesian volatility learning under microstructure noise
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2018
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910369250803712 |
|---|---|
| author | Gugushvili, Shota van der Meulen, Frank Schauer, Moritz Spreij, Peter |
| author_facet | Gugushvili, Shota van der Meulen, Frank Schauer, Moritz Spreij, Peter |
| contents | In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we \emph{a priori} model the volatility function as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs sampler. Good performance of the method is demonstrated on two representative synthetic data examples. We also apply the method on a EUR/USD exchange rate dataset. Finally we present a limit result on the prior distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1805_05606 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Nonparametric Bayesian volatility learning under microstructure noise Gugushvili, Shota van der Meulen, Frank Schauer, Moritz Spreij, Peter Methodology Statistical Finance Machine Learning Primary: 62G20, Secondary: 62M05 In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we \emph{a priori} model the volatility function as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs sampler. Good performance of the method is demonstrated on two representative synthetic data examples. We also apply the method on a EUR/USD exchange rate dataset. Finally we present a limit result on the prior distribution. |
| title | Nonparametric Bayesian volatility learning under microstructure noise |
| topic | Methodology Statistical Finance Machine Learning Primary: 62G20, Secondary: 62M05 |
| url | https://arxiv.org/abs/1805.05606 |