Numerical solution of stochastic differential equations with jumps in finance /
"We present and analyse two implicit methods for Ito stochastic differential equations (SDEs) with Poisson-driven jumps. The first method, SSBE, is a split-step extension of the backward Euler method. The second method, CSSBE, arises from the introduction of a compensated, martingale, form of t...
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg :
Springer-Verlag,
©2010.
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| Series: | Stochastic modelling and applied probability
64. |
| Subjects: |
MARC
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| 100 | 1 | |a Platen, Eckhard | |
| 245 | 1 | 0 | |a Numerical solution of stochastic differential equations with jumps in finance / |c Eckhard Platen, Nicola Bruti-Liberati. |
| 260 | |a Berlin ; |a Heidelberg : |b Springer-Verlag, |c ©2010. | ||
| 300 | |a xxviii, 856 pages : |b illustrations ; |c 25 cm. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 490 | 1 | |a Stochastic modelling and applied probability ; |v 64 | |
| 504 | |a Includes bibliographical references (pages 783-834) and indexes. | ||
| 505 | 0 | |a 1. SDEs with Jumps -- 2. Exact Simulation of Solutions of SDEs -- 3. Benchmark Approach to Finance -- 4. Stochastic Expansions -- 5. Introduction to Scenario Simulation -- 6. Regular Strong Taylor Approximations -- 7. Regular Strong Ito Approximations -- 8. Jump-Adapted Strong Approximations -- 9. Estimating Discretely Observed Diffusions -- 10. Filtering -- 11. Monte Carlo Simulation of SDEs -- 12. Regular Weak Taylor Approximations -- 3. Jump-Adapted Weak Approximations -- 14. Numerical Stability -- 15. Martingale Representations and Hedge Ratios -- 16. Variance Reduction Techniques -- 17. Trees and Markov Chain Approximations -- 18. Solutions for Exercises. | |
| 520 | |a "We present and analyse two implicit methods for Ito stochastic differential equations (SDEs) with Poisson-driven jumps. The first method, SSBE, is a split-step extension of the backward Euler method. The second method, CSSBE, arises from the introduction of a compensated, martingale, form of the Poisson process. We show that both methods are amenable to rigorous analysis when a one-sided Lipschitz condition, rather than a more restrictive global Lipschitz condition, holds for the drift. Our analysis covers strong convergence and nonlinear stability. We prove that both methods give strong convergence when the drift coefficient is one-sided Lipschitz and the diffusion and jump coefficients are globally Lipschitz. On the way to proving these results, we show that a compensated form of the Euler-Maruyama method converges strongly when the SDE coefficients satisfy a local Lipschitz condition and the pth moment of the exact and numerical solution are bounded for some p>2. Under our assumptions, both SSBE and CSSBE give well-defined, unique solutions for sufficiently small stepsizes, and SSBE has the advantage that the restriction is independent of the jump intensity. We also study the ability of the methods to reproduce exponential mean-square stability in the case where the drift has a negative one-sided Lipschitz constant. This work extends the deterministic nonlinear stability theory in numerical analysis. We find that SSBE preserves stability under a stepsize constraint that is independent of the initial data. CSSBE satisfies an even stronger condition, and gives a generalization of B-stability. Finally, we specialize to a linear test problem and show that CSSBE has a natural extension of deterministic A-stability. The difference in stability properties of the SSBE and CSSBE methods emphasizes that the addition of a jump term has a significant effect that cannot be deduced directly from the non-jump literature"--Publisher description. | ||
| 650 | 0 | |a Stochastic differential equations | |
| 650 | 0 | |a Jump processes | |
| 700 | 1 | |a Bruti-Liberati, Nicola | |
| 830 | 0 | |a Stochastic modelling and applied probability |v 64. | |
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