MCCO combines multistage stochastic programming and conditional stochastic optimization, solved via new multilevel Monte Carlo techniques with polynomial scenario complexity.
Efficient risk estimation for the credit valuation adjustment
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
An adaptive multilevel stochastic approximation scheme for Value-at-Risk computation achieves complexity O(ε^{-2} |ln ε|^{5/2}) by selecting inner samples adaptively at each level.
A multilevel stochastic approximation scheme achieves near-optimal complexity of order epsilon^{-2-delta} for VaR and epsilon^{-2}|ln epsilon|^2 for ES in nested risk estimation.
Establishes central limit theorems for the renormalized estimation errors of nested and multilevel stochastic approximation algorithms for VaR and ES, including averaged versions, with numerical illustration.
citing papers explorer
-
Multistage Conditional Compositional Optimization
MCCO combines multistage stochastic programming and conditional stochastic optimization, solved via new multilevel Monte Carlo techniques with polynomial scenario complexity.