Paper on collocation method for Musiela parameterization of HJM model updated to v5
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Yuki Kinoshita and Yumiharu Nakano released the 5th version of their research paper on the collocation method for Musiela parameterization of the HJM model on arXiv, proposing a kernel-based collocation numerical solution method. This method is an Euler-Maruyama approximation for finite-dimensional stochastic differential equations and can be combined with standard Monte Carlo procedures for derivative pricing. The paper provides conditional mean-square error estimates under explicit stability assumptions and validates the effectiveness of the method through two finite-resolution examples: the Vasicek model and caplet pricing.
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- arXiv q-finKernel-Based Collocation Methods for the Heath-Jarrow-Morton Model with Musiela Parameterization
This paper studies a kernel-based collocation numerical solution method for the Heath-Jarrow-Morton model under Musiela parameterization. The method is an Euler-Maruyama approximation of a finite-dimensional stochastic differential equation, which can be integrated with standard Monte Carlo workflows for derivative pricing. Conditional mean squared error estimates are derived under explicit stability assumptions, and the method's effectiveness is validated through two finite-resolution examples: the Vasicek model and caplet pricing.
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