Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review

@article{Hong2014MonteCM,
  title={Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review},
  author={L. Jeff Hong and Zhaolin Hu and Guangwu Liu},
  journal={ACM Trans. Model. Comput. Simul.},
  year={2014},
  volume={24},
  pages={22:1-22:37}
}
Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures of large losses and are employed in the financial industry for risk management purposes. In practice, loss distributions typically do not have closed-form expressions, but they can often be simulated (i.e., random observations of the loss distribution may be obtained by running a computer program). Therefore, Monte Carlo methods that design simulation experiments and utilize simulated observations are… CONTINUE READING

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