Decision Sciences
8 years ago

Revolutionizing Risk Measures: Adaptive Optimization Reduces Computational Burden

1 view
arXiv (Cornell University)
Simulation Optimization of Risk Measures with Adaptive Risk Levels
Helin Zhu, Joshua Q. Hale, Enlu Zhou

Paper Summary

The article explores a new method to optimize risk measures like Value-at-Risk and Conditional Value-at-Risk. By using a smart updating strategy, the researchers were able to reduce the number of samples needed for each calculation, making the process more efficient. This approach helps in improving the accuracy of risk assessment while saving computational resources.

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