Efficient Monte Carlo methods for value-at-risk

By Paul Glasserman, Philip Heidelberger, Perwez Shahabuddin. 2001

64 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/10d9bde2595b0173c4c5d64b8e302a0c523e123f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jakub Marecek
2. Claudio Gambella
3. Rudy Raymond
4. Andrea Simonetto
5. Elena Yndurain
6. Scott McFaddin
7. Martin Mevissen
8. Jakub Konecný

Citations in the corpus

• Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, methodology): “This represents a quadratic speedup compared to theO(M−1/2) convergence rate of classical MC methods [40].”
• Quantum risk analysis (2018, methodology): “This represents a quadratic speedup compared to the O(M) convergence rate of classical Monte Carlo methods.(1) To use AE to estimate quantities related to a random variable X we must first represent X as a quantum state.”