Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916594630787072 |
|---|---|
| author | Lesniewski, Andrew Trigila, Giulio |
| author_facet | Lesniewski, Andrew Trigila, Giulio |
| contents | We propose a highly efficient and accurate methodology for generating synthetic financial market data using a diffusion model approach. The synthetic data produced by our methodology align closely with observed market data in several key aspects: (i) they pass the two-sample Cramer - von Mises test for portfolios of assets, and (ii) Q - Q plots demonstrate consistency across quantiles, including in the tails, between observed and generated market data. Moreover, the covariance matrices derived from a large set of synthetic market data exhibit significantly lower condition numbers compared to the estimated covariance matrices of the observed data. This property makes them suitable for use as regularized versions of the latter. For model training, we develop an efficient and fast algorithm based on numerical integration rather than Monte Carlo simulations. The methodology is tested on a large set of equity data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics Lesniewski, Andrew Trigila, Giulio Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Portfolio Management We propose a highly efficient and accurate methodology for generating synthetic financial market data using a diffusion model approach. The synthetic data produced by our methodology align closely with observed market data in several key aspects: (i) they pass the two-sample Cramer - von Mises test for portfolios of assets, and (ii) Q - Q plots demonstrate consistency across quantiles, including in the tails, between observed and generated market data. Moreover, the covariance matrices derived from a large set of synthetic market data exhibit significantly lower condition numbers compared to the estimated covariance matrices of the observed data. This property makes them suitable for use as regularized versions of the latter. For model training, we develop an efficient and fast algorithm based on numerical integration rather than Monte Carlo simulations. The methodology is tested on a large set of equity data. |
| title | Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics |
| topic | Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Portfolio Management |
| url | https://arxiv.org/abs/2412.00036 |