论文标题
经济场景生成器的一致校准：有条件模拟的情况
Consistent Calibration of Economic Scenario Generators: The Case for Conditional Simulation
论文作者
论文摘要
经济方案生成器（ESG）模拟了经济和财务变量，以进行风险管理和资产分配目的。仅将ESG中所有变量的动力学校准到仅历史数据通常是不可行的。为了进行压力测试和投资组合优化，需要校准向前信息，例如将来的方案和回报期望，但是没有公认的方法可用。本文介绍了有条件的情景模拟器，这是一个框架，用于将经济和财务变量的模拟和投影均逐渐校准，以便为历史数据和前瞻性信息。该框架可以看作是黑人列者模型的多周期，多因素概括，并且可以嵌入各种财务和宏观经济模型。两个实践的例子在一个常见主义者和贝叶斯环境中证明了这一点。
Economic Scenario Generators (ESGs) simulate economic and financial variables forward in time for risk management and asset allocation purposes. It is often not feasible to calibrate the dynamics of all variables within the ESG to historical data alone. Calibration to forward-information such as future scenarios and return expectations is needed for stress testing and portfolio optimization, but no generally accepted methodology is available. This paper introduces the Conditional Scenario Simulator, which is a framework for consistently calibrating simulations and projections of economic and financial variables both to historical data and forward-looking information. The framework can be viewed as a multi-period, multi-factor generalization of the Black-Litterman model, and can embed a wide array of financial and macroeconomic models. Two practical examples demonstrate this in a frequentist and Bayesian setting.
