Purpose

It is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.

Design/methodology/approach

The key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.

Findings

The backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.

Originality/value

This paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

中文翻译：

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经济情景生成模型如何应对金融市场的突然变化？

目的

包括人寿保险公司在内的金融机构很可能会在政策成熟之前遇到 COVID-19 大流行等动荡局势。因此，构建能够为主要资产生成情景以涵盖金融市场突然变化的模型对于金融机构的风险管理至关重要。

设计/方法/方法

这种建模的关键问题包括如何管理所涉及的大量风险因素，如何对选定或衍生因素的动态进行建模，以及如何合并这些因素之间的关系。作者提出了正交 ARMA-GARCH（自回归移动平均-广义自回归条件异方差）方法来解决这些问题。构建的经济情景生成 (ESG) 模型通过了涵盖 2018 年初至 2020 年 5 月底期间的回测，其中包括由 COVID-19 引起的动荡局势。

发现

涵盖 COVID-19 动荡时期的回测，以及扇形图以及模拟和历史统计数据的比较，验证了我们的方法。

原创性/价值

本文是第一个尝试从其通过正交 ARMA-GARCH 模型估计的大维协方差矩阵为大规模投资组合生成复杂的长期经济情景的论文。