0000000000425322

AUTHOR

Santtu Salmi

showing 9 related works from this author

Robust and Efficient IMEX Schemes for Option Pricing under Jump-Diffusion Models

2013

We propose families of IMEX time discretization schemes for the partial integro-differential equation derived for the pricing of options under a jump diffusion process. The schemes include the families of IMEX-midpoint, IMEXCNAB and IMEX-BDF2 schemes. Each family is defined by a convex parameter c ∈ [0, 1], which divides the zeroth-order term due to the jumps between the implicit and explicit part in the time discretization. These IMEX schemes lead to tridiagonal systems, which can be solved extremely efficiently. The schemes are studied through Fourier stability analysis and numerical experiments. It is found that, under suitable assumptions and time step restrictions, the IMEX-midpoint fa…

Mathematical optimizationTridiagonal matrixDiscretizationJump diffusionRegular polygonComputer Science::Numerical AnalysisStability (probability)Mathematics::Numerical Analysissymbols.namesakeFourier transformValuation of optionssymbolsMathematicsLinear multistep methodSSRN Electronic Journal
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An IMEX-Scheme for Pricing Options under Stochastic Volatility Models with Jumps

2014

Partial integro-differential equation (PIDE) formulations are often preferable for pricing options under models with stochastic volatility and jumps, especially for American-style option contracts. We consider the pricing of options under such models, namely the Bates model and the so-called stochastic volatility with contemporaneous jumps (SVCJ) model. The nonlocality of the jump terms in these models leads to matrices with full matrix blocks. Standard discretization methods are not viable directly since they would require the inversion of such a matrix. Instead, we adopt a two-step implicit-explicit (IMEX) time discretization scheme, the IMEX-CNAB scheme, where the jump term is treated ex…

Mathematical optimizationimplicit-explicit time discretizationDiscretizationStochastic volatilityApplied Mathematicsta111Linear systemLU decompositionMathematics::Numerical Analysislaw.inventionComputational MathematicsMatrix (mathematics)stochastic volatility modelMultigrid methodlawValuation of optionsjump-diffusion modelJumpoption pricingfinite difference methodMathematicsSIAM Journal on Scientific Computing
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A Comparison and Survey of Finite Difference Methods for Pricing American Options Under Finite Activity Jump-Diffusion Models

2012

Partial-integro differential formulations are often used for pricing American options under jump-diffusion models. A survey on such formulations and numerical methods for them is presented. A detailed description of six efficient methods based on a linear complementarity formulation and finite difference discretizations is given. Numerical experiments compare the performance of these methods for pricing American put options under finite activity jump models.

Iterative methodNumerical analysisComplementarity (molecular biology)Jump diffusionFinite difference methodJumpFinite differenceApplied mathematicsLinear complementarity problemMathematicsSSRN Electronic Journal
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IMEX schemes for pricing options under jump–diffusion models

2014

We propose families of IMEX time discretization schemes for the partial integro-differential equation derived for the pricing of options under a jump-diffusion process. The schemes include the families of IMEX-midpoint, IMEX-CNAB and IMEX-BDF2 schemes. Each family is defined by a convex combination parameter [email protected]?[0,1], which divides the zeroth-order term due to the jumps between the implicit and explicit parts in the time discretization. These IMEX schemes lead to tridiagonal systems, which can be solved extremely efficiently. The schemes are studied through Fourier stability analysis and numerical experiments. It is found that, under suitable assumptions and time step restric…

ta113Numerical AnalysisMathematical optimizationTridiagonal matrixDiscretizationApplied MathematicsJump diffusionStability (probability)Term (time)Computational MathematicsValuation of optionsConvex combinationLinear multistep methodMathematicsApplied Numerical Mathematics
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Iterative Methods for Pricing American Options under the Bates Model

2013

We consider the numerical pricing of American options under the Bates model which adds log-normally distributed jumps for the asset value to the Heston stochastic volatility model. A linear complementarity problem (LCP) is formulated where partial derivatives are discretized using finite differences and the integral resulting from the jumps is evaluated using simple quadrature. A rapidly converging fixed point iteration is described for the LCP, where each iterate requires the solution of an LCP. These are easily solved using a projected algebraic multigrid (PAMG) method. The numerical experiments demonstrate the efficiency of the proposed approach. Furthermore, they show that the PAMG meth…

ta113Mathematical optimizationStochastic volatilityDiscretizationIterative methodComputer scienceFinite difference methodLinear complementarity problemIterative methodQuadrature (mathematics)Multigrid methodFixed-point iterationBates modelLinear complementarity problemGeneral Earth and Planetary SciencesPartial derivativeAmerican optionGeneral Environmental ScienceProcedia Computer Science
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An iterative method for pricing American options under jump-diffusion models

2011

We propose an iterative method for pricing American options under jump-diffusion models. A finite difference discretization is performed on the partial integro-differential equation, and the American option pricing problem is formulated as a linear complementarity problem (LCP). Jump-diffusion models include an integral term, which causes the resulting system to be dense. We propose an iteration to solve the LCPs efficiently and prove its convergence. Numerical examples with Kou@?s and Merton@?s jump-diffusion models show that the resulting iteration converges rapidly.

Numerical AnalysisNumerical linear algebraPartial differential equationIterative methodApplied MathematicsNumerical analysisJump diffusionta111computer.software_genreLinear complementarity problemComputational MathematicsComplementarity theoryValuation of optionsApplied mathematicscomputerMathematicsApplied Numerical Mathematics
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An Iterative Method for Pricing American Options Under Jump-Diffusion Models

2011

We propose an iterative method for pricing American options under jump-diffusion models. A finite difference discretization is performed on the partial integro-differential equation, and the American option pricing problem is formulated as a linear complementarity problem (LCP). Jump-diffusion models include an integral term, which causes the resulting system to be dense. We propose an iteration to solve the LCPs efficiently and prove its convergence. Numerical examples with Kou's and Merton's jump-diffusion models show that the resulting iteration converges rapidly.

Mathematical optimizationIterative methodValuation of optionsJump diffusionConvergence (routing)Finite difference methodFinite difference methods for option pricingLinear complementarity problemTerm (time)MathematicsSSRN Electronic Journal
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Multidisciplinary Design Optimization in an integrated CAD/FEM environment

2008

tietokoneavusteinen suunnittelumonitieteisyysoptimointiModeFRONTIERdesignCADCAEMDOmuotooptimizationCAPRImultidisciplinary
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Numerical methods for pricing options under jump-diffusion processes

2013

numeeriset menetelmätjump-diffusionPIDEoptiotnumerical methodshinnoittelujohdannaismarkkinatmatemaattiset mallitoption pricingstokastiset prosessit
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