{"title":"算法交易与买卖价差,波动性和利润和损失的分配:模拟","authors":"Arne Breuer, Hans-Peter Burghof","doi":"10.2139/ssrn.2200075","DOIUrl":null,"url":null,"abstract":"Algorithmic trading leads to contradictory conclusions of market participants and observers: While the former blame it to break the market, the latter find it makes it better. To explain these contradictory views, we create a few-type market simulation in a discrete-time, one-asset world. We analyse both the observable factors average bid-offer spread and volatility as well as the unobservable distribution of profits and losses of the market participants. We conclude that regarding HFT, market observers and market participants talk at cross-purposes.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"13 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Algorithmic Trading vs. Bid-Offer Spreads, Volatility, and the Distribution of Profits and Losses: A Simulation\",\"authors\":\"Arne Breuer, Hans-Peter Burghof\",\"doi\":\"10.2139/ssrn.2200075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithmic trading leads to contradictory conclusions of market participants and observers: While the former blame it to break the market, the latter find it makes it better. To explain these contradictory views, we create a few-type market simulation in a discrete-time, one-asset world. We analyse both the observable factors average bid-offer spread and volatility as well as the unobservable distribution of profits and losses of the market participants. We conclude that regarding HFT, market observers and market participants talk at cross-purposes.\",\"PeriodicalId\":11800,\"journal\":{\"name\":\"ERN: Stock Market Risk (Topic)\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Stock Market Risk (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2200075\",\"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: Stock Market Risk (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2200075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Algorithmic Trading vs. Bid-Offer Spreads, Volatility, and the Distribution of Profits and Losses: A Simulation
Algorithmic trading leads to contradictory conclusions of market participants and observers: While the former blame it to break the market, the latter find it makes it better. To explain these contradictory views, we create a few-type market simulation in a discrete-time, one-asset world. We analyse both the observable factors average bid-offer spread and volatility as well as the unobservable distribution of profits and losses of the market participants. We conclude that regarding HFT, market observers and market participants talk at cross-purposes.