Search results for "Valuation of options"

showing 10 items of 28 documents

Application of Operator Splitting Methods in Finance

2016

Financial derivatives pricing aims to find the fair value of a financial contract on an underlying asset. Here we consider option pricing in the partial differential equations framework. The contemporary models lead to one-dimensional or multidimensional parabolic problems of the convection-diffusion type and generalizations thereof. An overview of various operator splitting methods is presented for the efficient numerical solution of these problems.

FinanceMathematical optimizationPartial differential equationbusiness.industry010103 numerical & computational mathematicsType (model theory)01 natural sciencesLinear complementarity problem010101 applied mathematicsOperator splittingValuation of optionsFair valueJump modelEconomicsAsset (economics)0101 mathematicsbusinessMathematical economics
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A Sequential Quadratic Programming Method for Volatility Estimation in Option Pricing

2006

Our goal is to identify the volatility function in Dupire's equation from given option prices. Following an optimal control approach in a Lagrangian framework, we propose a globalized sequential quadratic programming (SQP) algorithm with a modified Hessian - to ensure that every SQP step is a descent direction - and implement a line search strategy. In each level of the SQP method a linear-quadratic optimal control problem with box constraints is solved by a primal-dual active set strategy. This guarantees L^1 constraints for the volatility, in particular assuring its positivity. The proposed algorithm is founded on a thorough first- and second-order optimality analysis. We prove the existe…

Hessian matrixMathematical optimizationLine searchComputer scienceMathematicsofComputing_NUMERICALANALYSISOptimal controlsymbols.namesakeValuation of optionsLagrange multipliersymbolsDescent directionVolatility (finance)Dupire equation parameter identification optimal control optimality conditions SQP method primal-dual active set strategySequential quadratic programming
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Optimal control of option portfolios and applications

1999

We present an expected utility maximisation framework for optimally controlling a portfolio of options. By combining the replication approach to option pricing with ideas of the martingale approach to (stock) portfolio optimisation we arrive at an explicit solution of the option portfolio problem. Its characteristics are illustrated by some specific examples. As an application, we calculate an optimal option and consumption strategy for an investor who is obliged to hold a stock position until the time horizon.

Mathematical optimizationComputer scienceMathematics::Optimization and ControlTime horizonManagement Science and Operations ResearchOptimal controlMartingale (betting system)Computer Science::Computational Engineering Finance and ScienceValuation of optionsBusiness Management and Accounting (miscellaneous)PortfolioPosition (finance)Expected utility hypothesisStock (geology)OR Spectrum
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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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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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Impact of Stock Price Jumps on Option Values

1999

Many empirical papers document the fact that the distribution of stock returns exhibits fatter tails than would be expected from a normal distribution. This might explain some of the pricing biases of the Black/Scholes model, which is] based on a normal return distribution. Given this result, alternative option pricing models should be based on one of the following three classes of return models: (1) a stationary process, such as a paretian stable or a student’s t-distribution, (2) a mixture of stationary distributions, such as two normal distributions with different means or variances, or a mixture of a diflusion and a pure jump process, or (3) a distribution such as a normal distribution …

Normal distributionCost priceFinancial economicsValuation of optionsJump diffusionJumpEconometricsMid priceEconomicsJump processFutures contract
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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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Evaluation of Insurance Products with Guarantee in Incomplete Markets

2008

Abstract Life insurance products are usually equipped with minimum guarantee and bonus provision options. The pricing of such claims is of vital importance for the insurance industry. Risk management, strategic asset allocation, and product design depend on the correct evaluation of the written options. Also regulators are interested in such issues since they have to be aware of the possible scenarios that the overall industry will face. Pricing techniques based on the Black & Scholes paradigm are often used, however, the hypotheses underneath this model are rarely met. To overcome Black & Scholes limitations, we develop a stochastic programming model to determine the fair price of the mini…

Statistics and ProbabilityIncomplete marketsEconomics and EconometricsActuarial sciencebusiness.industryOption pricingLife insurance; Policies with minimum guarantee; Option pricing; Incomplete marketsLife insuranceStochastic programmingKey person insurancePolicies with minimum guaranteeSettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Valuation of optionsFair valueLife insuranceIncomplete marketsEconomicsAuto insurance risk selectionStatistics Probability and UncertaintybusinessRisk management
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Designing and pricing guarantee options in defined contribution pension plans

2015

Abstract The shift from defined benefit (DB) to defined contribution (DC) is pervasive among pension funds, due to demographic changes and macroeconomic pressures. In DB all risks are borne by the provider, while in plain vanilla DC all risks are borne by the beneficiary. However, for DC to provide income security some kind of guarantee is required. A minimum guarantee clause can be modeled as a put option written on some underlying reference portfolio and we develop a discrete model that selects the reference portfolio to minimize the cost of a guarantee. While the relation DB–DC is typically viewed as a binary one, the model shows how to price a wide range of guarantees creating a continu…

Statistics and ProbabilityPensions; Minimum guarantee; Defined benefit; Defined contribution; Embedded options; Risk sharing; Portfolio selection; Stochastic programmingRisk sharingEconomics and EconometricsPensionActuarial scienceComputer sciencePensionStochastic programmingAsset allocationMinimum guaranteeEmbedded optionPortfolio selectionEmbedded optionStochastic programmingDefined contributionSettore SECS-S/06 -Metodi Mat. dell'Economia e d. Scienze Attuariali e Finanz.Defined benefitValuation of optionsPortfolioAsset (economics)Statistics Probability and UncertaintyPut optionInsurance: Mathematics and Economics
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