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This is a Experimental Study study.

September 26, 2025Applied and Computational EngineeringOpen Access

An Adaptive Importance Sampling Approach for Variance Reduction in Jump-Diffusion CVA Calculations

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YHYutong HuangNanjing University of Chinese Medicine

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Implication

This research develops an adaptive importance sampling method that improves variance reduction and computational efficiency in CVA calculations under jump-diffusion processes.

Key Points

The introduction of an adaptive importance sampling framework leads to variance reduction ratios exceeding 85%.
Computational efficiency improvements of approximately 400% are reported for portfolios using the new method.
The approach dynamically adjusts sampling densities to efficiently account for jump risk in high-dimensional processes.
Critical paths contributing to CVA variance are identified and oversampled, enhancing estimation accuracy.

Cite This Study

Yutong Huang (2025) studied this question.

synapsesocial.com/papers/68d6c68eb1249cec298b306a https://doi.org/10.54254/2755-2721/2025.gl27136

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Implication

This research develops an adaptive importance sampling method that improves variance reduction and computational efficiency in CVA calculations under jump-diffusion processes.

An Adaptive Importance Sampling Approach for Variance Reduction in Jump-Diffusion CVA Calculations

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Implication

Key Points

Cite This Study

An Adaptive Importance Sampling Approach for Variance Reduction in Jump-Diffusion CVA Calculations

Discussion

Member takes

Implication

Key Points

Cite This Study

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