Simulation of Coherent Risk Measures Based on Generalized Scenarios
提出基于模拟的固定宽度置信区间程序，用于估计多个期望的最大值（即连贯风险度量），并验证其正确性和计算效率，旨在推动连贯风险度量在风险管理中的应用。
In financial risk management, coherent risk measures have been proposed as a way to avoid undesirable properties of measures such as value at risk that discourage diversification and do not account for the magnitude of the largest, and therefore most serious, losses. A coherent risk measure equals the maximum expected loss under several different probability measures, and these measures are analogous to “populations” or “systems” in the ranking-and-selection literature. However, unlike in ranking and selection, here it is the value of the maximum expectation under any of the probability measures, and not the identity of the probability measure that attains it, that is of interest. We propose procedures to form fixed-width, simulation-based confidence intervals for the maximum of several expectations, explore their correctness and computational efficiency, and illustrate them on risk-management problems. The availability of efficient algorithms for computing coherent risk measures will encourage their use for improved risk management.