Stochastic volatility

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Efficient numerical methods for pricing American options under stochastic volatility - Ikonen - - Numerical Methods for Partial Differential Equations - Wiley Online Library

American options can be priced by solving linear complementary problems LCPs with parabolic partial -integro differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model FOM. Here reduced order models ROMs are derived employing proper orthogonal decomposition POD and non negative matrix factorization NNMF in order to make pricing much faster within a given model parameter variation range.

The numerical experiments demonstrate orders of magnitude faster pricing with ROMs.

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efficient numerical methods for pricing american options under stochastic volatility

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COMPONENTWISE SPLITTING METHODS FOR PRICING AMERICAN OPTIONS UNDER STOCHASTIC VOLATILITY | International Journal of Theoretical and Applied Finance , Vol 10, No 02 | World Scientific

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efficient numerical methods for pricing american options under stochastic volatility

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COMPONENTWISE SPLITTING METHODS FOR PRICING AMERICAN OPTIONS UNDER STOCHASTIC VOLATILITY | International Journal of Theoretical and Applied Finance , Vol 10, No 02 | World Scientific

Click to expose these in author workspace a University of Illinois at Urbana-Champaign, Champaign, Illinois, U. Under a Creative Commons license. Abstract American options can be priced by solving linear complementary problems LCPs with parabolic partial -integro differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models.

efficient numerical methods for pricing american options under stochastic volatility

Keywords reduced order model. Download full text in PDF.

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