BENCHOP – The BENCHmarking project in option pricing

Sydow, Lina von; Höök, Lars Josef; Larsson, Elisabeth; Lindström, Erik; Milovanović, Slobodan; Persson, Jonas; Shcherbakov, Victor; Shpolyanskiy, Yuri; Sirén, Samuel; Toivanen, Jari; Waldén, Johan; Wiktorsson, Magnus; Levesley, Jeremy; Li, Juxi; Oosterlee, Cornelis W.; Ruijter, Marjon; Alexander, Toropov; Zhao, Yangzhang

doi:10.1080/00207160.2015.1072172

Cited by 59 publications

(43 citation statements)

References 47 publications

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“…where 20) and f n B remains as in (4.13). In order to solve the arising non-linear system we employ the Newton method.…”

Section: The Penalty Methodsmentioning

confidence: 99%

“…If we compare this number with the FD OS method, it will be just three times slower, instead of 60 times slower as with the RBF-PUM IMEX method. But even this three times difference will be diminished in higher dimensions, because already for two-dimensional problems RBF-PUM performed better than any of the FD methods [20]. Figure 2 displays the error profiles for the given four methods.…”

Section: The Single-asset Casementioning

confidence: 99%

“…The RBF-PUM IMEX penalty implementation is the same one as was used for the American put option in the BENCHOP project [20], where it was compared with the FD operator splitting method within the same scope on the same machine (let us call it "the cluster"). The run time on "the cluster" for the RBF-PUM IMEX penalty method was 3.6 seconds and for the FD OS method it was on average (out of three different modifications) 0.059 seconds.…”

Section: The Single-asset Casementioning

confidence: 99%

“…The Benchmark Project in Option Pricing (BENCHOP) [20] was recently launched. In this project established methods for option pricing were tested and compared on different benchmark problems.…”

Section: Introductionmentioning

confidence: 99%

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Radial basis function partition of unity operator splitting method for pricing multi-asset American options

Shcherbakov

2016

Bit Numer Math

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PostprintThis is the accepted version of a paper published in BIT Numerical Mathematics. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. AbstractThe operator splitting method in combination with finite differences has been shown to be an efficient approach for pricing American options numerically. Here, the operator splitting formulation is extended to the radial basis function partition of unity method. An approach that has previously often been used together with radial basis function methods to deal with the free boundary arising in American option pricing is to solve a penalised version of the Black-Scholes equation. It is shown that the operator splitting technique outperforms the penalty approach when used with the radial basis function partition of unity method. Numerical experiments are performed for one, two and three underlying assets. The advantage of the operator splitting technique grows with the number of dimensions.

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“…where 20) and f n B remains as in (4.13). In order to solve the arising non-linear system we employ the Newton method.…”

Section: The Penalty Methodsmentioning

confidence: 99%

Section: The Single-asset Casementioning

confidence: 99%

Section: The Single-asset Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radial basis function partition of unity operator splitting method for pricing multi-asset American options

Shcherbakov

2016

Bit Numer Math

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“…This is similar to many option pricing problems where it is common to impose a boundary condition from asymptotic expansion of the solution, cf. von Sydow et al (2015) for an overview of numerical techniques for computing option prices. For Eq.…”

Section: Discretization Of the Kolmogorov Backward Equationmentioning

confidence: 99%

Efficient computation of the quasi likelihood function for discretely observed diffusion processes

Höök

Lindström

2016

Computational Statistics & Data Analysis

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We introduce a simple method for nearly simultaneous computation of all moments needed for quasi maximum likelihood estimation of parameters in discretely observed stochastic differential equations commonly seen in finance. The method proposed in this papers is not restricted to any particular dynamics of the differential equation and is virtually insensitive to the sampling interval. The key contribution of the paper is that computational complexity is sublinear in the number of observations as we compute all moments through a single operation. Furthermore, that operation can be done offline. The simulations show that the method is unbiased for all practical purposes for any sampling design, including random sampling, and that the computational cost is comparable (actually faster for moderate and large data sets) to the simple, often severely biased, EulerMaruyama approximation.

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Optimal Adaptive Sequential Calibration of Option Models

Lindström

Åkerlindh

2017

International Series in Operations Research &Amp; Management Science

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BENCHOP – The BENCHmarking project in option pricing

Cited by 59 publications

References 47 publications

Radial basis function partition of unity operator splitting method for pricing multi-asset American options

Radial basis function partition of unity operator splitting method for pricing multi-asset American options

Efficient computation of the quasi likelihood function for discretely observed diffusion processes

Optimal Adaptive Sequential Calibration of Option Models

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