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題名 How can an economic scenario generation model cope with abrupt changes in financial markets?
作者 Hsieh, Ming-Hua
Lee, Yi-Hsi
Kuo, Weiyu
Tsai, Chenghsien Jason
謝明華; 蔡政憲
貢獻者 風管系
關鍵詞 Economic scenario generation;Life insurance;Risk management
日期 2021-05
上傳時間 11-四月-2022 15:11:23 (UTC+8)
摘要 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
關聯 China Finance Review International, Vol.11, No.3, pp.372-405
資料類型 article
DOI https://doi.org/10.1108/CFRI-03-2021-0056
dc.contributor 風管系-
dc.creator (作者) Hsieh, Ming-Hua-
dc.creator (作者) Lee, Yi-Hsi-
dc.creator (作者) Kuo, Weiyu-
dc.creator (作者) Tsai, Chenghsien Jason-
dc.creator (作者) 謝明華; 蔡政憲-
dc.date (日期) 2021-05-
dc.date.accessioned 11-四月-2022 15:11:23 (UTC+8)-
dc.date.available 11-四月-2022 15:11:23 (UTC+8)-
dc.date.issued (上傳時間) 11-四月-2022 15:11:23 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139821-
dc.description.abstract (摘要) 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
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dc.format.extent 3266938 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) China Finance Review International, Vol.11, No.3, pp.372-405-
dc.subject (關鍵詞) Economic scenario generation;Life insurance;Risk management-
dc.title (題名) How can an economic scenario generation model cope with abrupt changes in financial markets?-
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.1108/CFRI-03-2021-0056-
dc.doi.uri (DOI) https://doi.org/10.1108/CFRI-03-2021-0056-