{"title":"Micro-Foundation of ARCH Model","authors":"T. Mizuta","doi":"10.2139/ssrn.2710457","DOIUrl":null,"url":null,"abstract":"The Japanese version of this paper can be found at http://ssrn.com/abstract=2710516.Many macroeconomic study argued macroeconomic models should be aggregated by micro processes models (\"micro-foundation\") and many micro-founded macroeconomic models were built. On the other hand, there are many models for price variation of a risk asset, which is macro phenomena, however, there are few studies for micro-foundation of such models. In this study we tried micro-foundation of an ARCH model using intelligence of artificial market simulation studies. That is we tried to clarify which micro processes determine each coefficient of an ARCH model. Then, we showed that the dispersion of investors' estimated prices is larger or the orders by the buy-sell imbalance taking liquidity are more, the volatility is larger. And we showed that the ration of the normal investors taking liquidity to the noise traders providing liquidity is higher or the measure of risk aversion of the normal investors is lower, the magnitude of volatility clustering is larger.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2710457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The Japanese version of this paper can be found at http://ssrn.com/abstract=2710516.Many macroeconomic study argued macroeconomic models should be aggregated by micro processes models ("micro-foundation") and many micro-founded macroeconomic models were built. On the other hand, there are many models for price variation of a risk asset, which is macro phenomena, however, there are few studies for micro-foundation of such models. In this study we tried micro-foundation of an ARCH model using intelligence of artificial market simulation studies. That is we tried to clarify which micro processes determine each coefficient of an ARCH model. Then, we showed that the dispersion of investors' estimated prices is larger or the orders by the buy-sell imbalance taking liquidity are more, the volatility is larger. And we showed that the ration of the normal investors taking liquidity to the noise traders providing liquidity is higher or the measure of risk aversion of the normal investors is lower, the magnitude of volatility clustering is larger.