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Robust estimation of multivariate jump-diffusion processes via dynamic programming.
| Content Provider | CiteSeerX |
|---|---|
| Author | Johansson, B. Jain, S. Montoya-Torres, J. Hugan, J. Yücesan, E. Torzhkov, Andrey Sharma, Puneet Chakraborty, Amit |
| Abstract | In this work we present a framework for estimation of a rather general class of multivariate jumpdiffusion processes. We assume that a continuous unobservable linear diffusion processes system is additively mixed together with a discrete jump processes vector and a conventional multi-variate white-noise process. This sum is observed over time as a multi-variate jump-diffusion time-series. Our objective is to identify realizations of all components of the mix in a robust and scalable way. First, we formulate this model as an Mixed-Integer-Programming (MIP) optimization problem extending traditional least-squares estimation framework to include discrete jump processes. Then we propose a Dynamic Programming (DP) approximate algorithm that is reasonably fast & accurate and scales polynomially with time horizon. Finally, we provide numerical test cases illustrating the algorithm performance and robustness. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multivariate Jumpdiffusion Process Dynamic Programming Conventional Multi-variate White-noise Process Time Horizon Continuous Unobservable Linear Diffusion Traditional Least-squares Estimation Framework Discrete Jump Numerical Test Case Optimization Problem Scalable Way Fast Accurate Algorithm Performance Multi-variate Jump-diffusion Time-series General Class Discrete Jump Process |
| Content Type | Text |
