Complexity reduction for parametric high dimensional models in the analysis of financial risk
Abstract
This paper presents a parametric model order reduction (pMOR) approach based on the proper orthogonal decomposition approach for financial risk analysis. pMOR requires solving the high dimensional model for some training parameters to obtain the reduced basis. We propose an adaptive greedy sampling approach based on surrogate modeling for the selection of training parameters. The developed algorithms are tested on an industrial example of a puttable steepener. The results show that the reduced model works excellent and has potential applications in historical or Monte-Carlo value at risk calculations.
Onkar Jadhav
Research Fellow
Applied Machine Learning Scientist specializing in the research, optimization, and machine learning. As a researcher, I bring deep theoretical and practical understanding of Transformer architectures and state-of-the-art deep learning methods. I am proficient in developing robust ML pipelines and translating cutting-edge research into effective product features.