{"title":"The Synchronized and Long-Lasting Structural Change on Commodity Markets: Evidence from High Frequency Data","authors":"David Bicchetti, Nicolas Maystre","doi":"10.2139/ssrn.2046584","DOIUrl":"https://doi.org/10.2139/ssrn.2046584","url":null,"abstract":"This paper analyses the intraday co-movements between returns on several commodity markets and on the stock market in the United States over the 1997-2011 period. By exploiting a new high frequency database, we compute various rolling correlations at (i) 1-hour, (ii) 5-minute, (iii) 10-second, and (iv) 1-second frequencies. Using this database, we document a synchronized structural break, characterized by a departure from zero, which starts in the course of 2008 and continues thereafter. This is consistent with the idea that recent financial innovations on commodity futures exchanges, in particular the high frequency trading activities and algorithm strategies have an impact on these correlations.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2012-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67879267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cluster formation and evolution in networks of financial market indices","authors":"Leonidas Sandoval Junior","doi":"10.3233/AF-13015","DOIUrl":"https://doi.org/10.3233/AF-13015","url":null,"abstract":"Using data from world stock exchange indices prior to and during periods of global financial crises, clusters and networks of indices are built for different thresholds and diverse periods of time, so that it is then possible to analyze how clusters are formed according to correlations among indices and how they evolve in time, particularly during times of financial crises. Further analysis is made on the eigenvectors corresponding to the second highest eigenvalues of the correlation matrices, revealing a structure peculiar to markets that operate in different time zones.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"2 1","pages":"3-43"},"PeriodicalIF":0.5,"publicationDate":"2011-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-13015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Minute with David Leinweber","authors":"D. Leinweber","doi":"10.3233/AF-2011-014","DOIUrl":"https://doi.org/10.3233/AF-2011-014","url":null,"abstract":"In each issue, Algorithmic Finance features a brief interview with one member of our advisory or editorial boards or another leading academic or practitioner. These brief conversations are intended to provide a glimpse of their current thinking. In this issue, we talk with David Leinweber. – DAVID LEINWEBER, author of “Nerds on Wall Street: Math, Machines and Wired Markets”, http:// tinyurl.com/nerdsonwallst was recently named one of the Top Ten Innovators of the Decade by Advanced Trading magazine. As founder of two financial technology firms, and as manager of multi-billon dollar quantitative equity portfolios, he brings a practical approach to innovation. He is now principal of Leinweber & Co., and in a public service role, co-founder of the Center for Innovative Financial Technology at Lawrence Berkeley Lab. http://www.lbl.gov/CS/CIFT .html LBL is one of the premier data intensive science research facilities in the world. Two of LBL’s most recent Nobel laureates used methods of “data intensive science” for physical problems on some of the largest computers in the world. CIFT is supported by financial firms and foundations. CIFT’s key idea is that systemic risk includes the risk of systems. Jason Zweig of the WSJ put it this way: “Could Computers Protect the Market from Computers?” Leinweber combines the expertise of a computer scientist with the financial experience of a pioneering practitioner in electronic markets and computer driven investing. He has undergraduate degrees in Physics and Computer Science from MIT and a PhD in applied mathematics from Harvard University and is a frequent keynote speaker and writer. He blogs at http://blogs.forbes.com/people/David%20Leinweber/ and http://nerdsonwallstreet.typepad.com. What are your research interests right now? At LBL, we focus on how modern information technologies can make markets more stabile, safe and secure. A collection of well-tested stable systems, in aggregate, and operating on time scales so much faster than their users, can be unstable and unpredictable. Cyber security of markets in aggregate is so complex that it has taken its place at the back of the queue. The stock market is only the most visible because of its high transparency. Resolution of issues in much larger markets, such as swaps (including what were once known as “toxic assets”) in the highly politicized SEF and FPML efforts, as we saw in 2008, can be more damaging than events in the stock market. An amazingly well written and detailed account of these issues in modern markets is found in Scott Paterson’s book “Dark Pools” which is about much more than dark pools in the narrow industry sense. At Leinweber & Co, our commercial work has been looking to extend quant methods into the “quanttextual” world, where information comes as “big data” in the form of words as well as numbers. Understanding complex evolving events are an area where humans still have game against computers. What do you see as academically exciting? I ","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"1 1","pages":"191-192"},"PeriodicalIF":0.5,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-2011-014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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