Digital financePub Date : 2020-06-02DOI: 10.1007/s42521-021-00030-w
F. Benth, Nils Detering, Silvia Lavagnini
{"title":"Accuracy of deep learning in calibrating HJM forward curves","authors":"F. Benth, Nils Detering, Silvia Lavagnini","doi":"10.1007/s42521-021-00030-w","DOIUrl":"https://doi.org/10.1007/s42521-021-00030-w","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"209 - 248"},"PeriodicalIF":0.0,"publicationDate":"2020-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-021-00030-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45808222","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}
Digital financePub Date : 2020-03-27DOI: 10.1007/s42521-020-00019-x
Alexander Jakob Dautel, W. Härdle, S. Lessmann, H. Seow
{"title":"Forex exchange rate forecasting using deep recurrent neural networks","authors":"Alexander Jakob Dautel, W. Härdle, S. Lessmann, H. Seow","doi":"10.1007/s42521-020-00019-x","DOIUrl":"https://doi.org/10.1007/s42521-020-00019-x","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1","pages":"69 - 96"},"PeriodicalIF":0.0,"publicationDate":"2020-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-020-00019-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52726662","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}
Digital financePub Date : 2020-03-19DOI: 10.2139/ssrn.3557733
A. Meyer, Lennart Ante
{"title":"Effects of initial coin offering characteristics on cross-listing returns","authors":"A. Meyer, Lennart Ante","doi":"10.2139/ssrn.3557733","DOIUrl":"https://doi.org/10.2139/ssrn.3557733","url":null,"abstract":"The low level of regulation and publication requirements in cryptocurrency markets leads to little information on cryptocurrency projects being publicly available. Against the background of high information asymmetry, the interpretation of the available information is all the more important. This paper examines how initial coin offering (ICO) characteristics affect cross-listing returns, i.e. whether or not available information is a valuable market signal of quality. For this purpose, we analyze 250 cross-listings of 135 different tokens issued via ICOs and calculate abnormal returns for specific samples using event study methodology. We find that cross-listing returns are driven by success in terms of token performance and project funding, as well as by jurisdiction-specific characteristics like the extent of regulation and domestic market size. Other characteristics such as the choice or change of blockchain infrastructure, token distribution across investors and the project team, campaign duration and whitepaper characteristics also seem to influence perceived project quality and thus cross-listing returns. The results contribute to the literature on cross-listings, cryptocurrency markets and entrepreneurial finance in the form of ICOs. They also make it possible to interpret the information available on the market and enable investors, project teams and cryptocurrency exchanges to evaluate probable market reactions to cross-listings.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1","pages":"259 - 283"},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45442499","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}
Digital financePub Date : 2020-01-01Epub Date: 2020-05-11DOI: 10.1007/s42521-020-00021-3
Arianna Agosto, Paolo Giudici
{"title":"COVID-19 contagion and digital finance.","authors":"Arianna Agosto, Paolo Giudici","doi":"10.1007/s42521-020-00021-3","DOIUrl":"https://doi.org/10.1007/s42521-020-00021-3","url":null,"abstract":"<p><p>Digital finance is going to be heavily affected by the COVID-19 outbreak. We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, so that its impact on finance can possibly be anticipated, and digitally monitored. The model is a Poisson autoregression of the daily new observed cases, and considers both short-term and long-term dependence in the infections counts. Model results are presented for the observed time series of China, the first affected country, but can be easily reproduced for all countries.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1-2","pages":"159-167"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-020-00021-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38687916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2020-01-01Epub Date: 2020-08-13DOI: 10.1007/s42521-020-00026-y
Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer
{"title":"Neural networks and arbitrage in the VIX: A deep learning approach for the VIX.","authors":"Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer","doi":"10.1007/s42521-020-00026-y","DOIUrl":"https://doi.org/10.1007/s42521-020-00026-y","url":null,"abstract":"<p><p>The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors' sentiment, representing the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors' knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1","pages":"97-115"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-020-00026-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38608677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2019-11-18DOI: 10.1007/s42521-019-00016-9
Jeonghoe Lee, Navid Sabbaghi
{"title":"Multi-objective optimization case study for algorithmic trading strategies in foreign exchange markets","authors":"Jeonghoe Lee, Navid Sabbaghi","doi":"10.1007/s42521-019-00016-9","DOIUrl":"https://doi.org/10.1007/s42521-019-00016-9","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1","pages":"15 - 37"},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-019-00016-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46902547","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}
Digital financePub Date : 2019-11-01DOI: 10.1007/s42521-019-00015-w
Jörg Osterrieder, Andrea Barletta
{"title":"Editorial on the Special Issue on Cryptocurrencies","authors":"Jörg Osterrieder, Andrea Barletta","doi":"10.1007/s42521-019-00015-w","DOIUrl":"https://doi.org/10.1007/s42521-019-00015-w","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"1 1","pages":"1 - 4"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-019-00015-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48071555","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}
Digital financePub Date : 2019-09-18DOI: 10.1007/s42521-019-00014-x
Thomas Renault
{"title":"Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages","authors":"Thomas Renault","doi":"10.1007/s42521-019-00014-x","DOIUrl":"https://doi.org/10.1007/s42521-019-00014-x","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"2 1","pages":"1 - 13"},"PeriodicalIF":0.0,"publicationDate":"2019-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-019-00014-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42088097","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}