{"title":"宏观经济情景分析的过滤历史模拟方法","authors":"Kaname Hirano","doi":"10.2139/ssrn.3127277","DOIUrl":null,"url":null,"abstract":"This paper applies a filtered historical simulation (FHS) approach to macroeconomic scenario generation. The aim of the approach is to generate more plausible macroeconomic scenarios than other macroeconomic scenario models such as the global vector autoregression (GVAR) model. This paper shows how to handle the relation between macroeconomic statistics and financial market factors in a filtered historical simulation approach. The application fields of the approach are integrated risk management, stress testing, business planning and so on.","PeriodicalId":364869,"journal":{"name":"ERN: Simulation Methods (Topic)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Filtered Historical Simulation Approach to Macroeconomic Scenario Analysis\",\"authors\":\"Kaname Hirano\",\"doi\":\"10.2139/ssrn.3127277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applies a filtered historical simulation (FHS) approach to macroeconomic scenario generation. The aim of the approach is to generate more plausible macroeconomic scenarios than other macroeconomic scenario models such as the global vector autoregression (GVAR) model. This paper shows how to handle the relation between macroeconomic statistics and financial market factors in a filtered historical simulation approach. The application fields of the approach are integrated risk management, stress testing, business planning and so on.\",\"PeriodicalId\":364869,\"journal\":{\"name\":\"ERN: Simulation Methods (Topic)\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Simulation Methods (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3127277\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Simulation Methods (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3127277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Filtered Historical Simulation Approach to Macroeconomic Scenario Analysis
This paper applies a filtered historical simulation (FHS) approach to macroeconomic scenario generation. The aim of the approach is to generate more plausible macroeconomic scenarios than other macroeconomic scenario models such as the global vector autoregression (GVAR) model. This paper shows how to handle the relation between macroeconomic statistics and financial market factors in a filtered historical simulation approach. The application fields of the approach are integrated risk management, stress testing, business planning and so on.