[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-560477-105":53,"doc-detail-560477-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","bitcoin-risk-modeling-with-blockchain-graphs","Bitcoin Risk Modeling with Blockchain Graphs","","Bitcoin risk modeling using blockchain graphs investigates how full visibility of agents’ actions in a cryptocurrency market supports price discovery and can serve as an early-warning signal for market volatility or crashes. The work focuses on FX risk for holders and the danger of uninformed conversion of Bitcoin to fiat. It builds a predictive model using Bitcoin microstructure and high-fidelity transaction-history graphs to study information-flow evolution and identify chainlets linked to extreme losses.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/bitcoin-risk-modeling-with-blockchain-graphs/560477/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/bitcoin-risk-modeling-with-blockchain-graphs/560477.png","ImageObject",442,249,{"name":88,"@type":89},"Pentious","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-23","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What problem does the document target in Bitcoin markets?","Question",{"text":108,"@type":109},"It targets predicting how information flow and user interactions in Bitcoin’s transaction network relate to price discovery and investment risk, including early warnings for volatility or crashes.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the approach differ from classical time-series econometric models?",{"text":113,"@type":109},"Instead of relying on macro variables and conventional time-series cross-sectional models, it uses Bitcoin microstructure by processing the full public transaction history as a high-fidelity graph.",{"name":115,"@type":106,"acceptedAnswer":116},"What are chainlets and how are they used in the model?",{"text":117,"@type":109},"A k-chainlet is a Bitcoin sub-graph consisting of k transactions plus their input and output addresses. The work studies 1-chainlets (chainlets) and identifies extreme chainlets (left/right) whose activity provides predictive influence on Bitcoin price and volatility.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},560477,1792092559,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":47,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":73},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","# Bitcoin Risk Modeling with Blockchain Graphs\n\nCuneyt Gurcan Akcora*Matthew F.Dixon,Yulia R.Geland Murat Kantarcioglu  \n## Abstract\n\n“whales”.The econometrics of Bitcoin seeks new interdis-ciplinary research to demonstrate how full disclosure of anagent's actions in a crypto-currency market inform price dis-covery and ultimately serve as an early-warning indicator forexcess market volatility or even a crash.  \nA key challenge for Bitcoin cryptocurrency holders,such asstartups using ICOs to raise funding,is managing their FXrisk.Specifically,a mis-informed decision to convert Bitcointo fiat currency could,by itself,cost USD millions.  \nWith the goal of building a predictive model,we thereforedepart from classical times-series cross sectional models thatleverage standard macro economic variables such as GDPand inflation.Instead,we use Bitcoin's microstructure.  \nIn contrast to financial exchanges,Blockchain basedcrypto-currencies expose the entire transaction history tothe public.By processing all transactions,we model the net-work with a high fidelity graph so that it is possible to char-acterize how the flow of information in the network evolvesover time.We demonstrate how this data representationpermits a new form of microstructure modeling—with theemphasis on the local topological network structure to studythe role of users,entities and their interactions in formationand dynamics of crypto-currency investment risk.In partic-ular,we identify certain sub-graphs (chainlets)that exhibitpredictive influence on Bitcoin price and volatility and char-acterize the types of chainlets that signify extreme losses.  \nBy processing all financial interactions,our objective isto model the network with a high fidelity graph so that itis possible to characterize how the flow of information inthe network evolves over time.This novel data representa-tion permits an entirely new form of financial econometrics—with the emphasis on the topological network structuresrather than covariance of historical time series of prices.Therole of users,entities and their interactions in formation anddynamics of crypto-currency risk investment,financial pre-dictive analytics and,more generally,in re-shaping the mod-ern financial world is a novel area of research11678  \nkeywords:Cryptocurrencies,Graph analysis,forecast-ing,financial risk,ICOs.  \n12.  \nJEL codes:C58.C63.G18  \n## 2 Method:Chainlets and DataProcessing\n\n1 Introduction  \nAs shown in Figure1 a Bitcoin graph consists of three maincomponents:addresses,transactions and blocks(see1 for aprimer on Blockchain graphs).One approach to understandhow transactions relate to market price is to introduce thenovel concept of k-chainlets 2.  \nNascent empirical research suggests that short-run Bitcoinprice behavior is prone to bubbles and busts and somewhatdetached from asset pricing theory 910,5.Since Bitcoinderives its economic value from a“network effect”-the moreindividuals who use Bitcoin,the more valuable the entireBitcoin ecosystem becomes-it is expected that transactionactivity is strongly linked with Bitcoin price changes 910.In contrast to existing financial networks,Blockchain basedcrypto-currencies expose the entire transaction graph to thepublic.Bitcoin transactions are listed for all participantsand the most significant agents can be immediately locatedon the network.  \nA k-chainlet is a Bitcoin sub-graph of k≥1 transactionsand their corresponding input and output addresses corre-sponding to different accounts,not necessarily unique to auser.In the simplest case,a single transaction creates a 1-chainlet with one or more inputs and a single output.Forexample,in Figure1 transaction t₂results in the transferof Bitcoin from addresses a3,a4,a₅to address ag.Such atransaction creates a 1-chainlet that has three inputs andone output.We denote this subgraph as a chainlet of typeC₃→1,where 3 and 1 are the number of input and outputaddresses,respectively.  \nIn contrast to closed financial systems,the largest ac-c","cbCaieM7PrbWNhjv","https://ap.wps.com/l/cbCaieM7PrbWNhjv","pdf",373100,"English","# Abstract\n# 2 Method: Chainlets and Data Processing\n## 1 Introduction\n## Figure 1: Transaction-address graph representation\n## Figure 3: Extreme chainlets ratios","[{\"question\":\"What problem does the document target in Bitcoin markets?\",\"answer\":\"It targets predicting how information flow and user interactions in Bitcoin’s transaction network relate to price discovery and investment risk, including early warnings for volatility or crashes.\"},{\"question\":\"How does the approach differ from classical time-series econometric models?\",\"answer\":\"Instead of relying on macro variables and conventional time-series cross-sectional models, it uses Bitcoin microstructure by processing the full public transaction history as a high-fidelity graph.\"},{\"question\":\"What are chainlets and how are they used in the model?\",\"answer\":\"A k-chainlet is a Bitcoin sub-graph consisting of k transactions plus their input and output addresses. The work studies 1-chainlets (chainlets) and identifies extreme chainlets (left/right) whose activity provides predictive influence on Bitcoin price and volatility.\"}]","Bitcoin Risk Modeling with Blockchain Graphs | PDF",1791803200]