摘要翻译：
经济情景生成器（ESGs）模拟经济和金融变量的时间向前进行风险管理和资产配置的目的。单凭历史数据来校准ESG内所有变量的动态往往是不可行的。压力测试和投资组合优化需要对未来情景和回报预期等前向信息进行校准，但没有公认的方法可用。本文介绍了条件情景模拟器，它是一个对历史数据和前瞻性信息一致校准经济和金融变量的模拟和预测的框架。该框架可以看作是Black-Litterman模型的多时期、多因素的概括，可以嵌入广泛的金融和宏观经济模型。两个实际的例子在频率和贝叶斯的设置中证明了这一点。
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英文标题：
《Consistent Calibration of Economic Scenario Generators: The Case for
  Conditional Simulation》
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作者：
Misha van Beek
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2004.09042