Liquidity Risk Management and Supervision

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Pre Order Now: Data Science in Economics and Finance - Coming Spring 2021

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Data Science in Economics and Finance is a key resource for any financial-market participant, policy-maker, central banker, economist or decision-maker required to understand the impact and opportunities presented by the transformation of digitalization and Fintech.

This book is a comprehensive overview of the data-science tools and techniques that already exist and that are emerging. ECB stalwart Per Nymand-Andersen has brought together over 20 global experts from both the private and public sectors, as well as authors from academia and the media in this expansive title, helping the reader to better understand the impact of digital data and the significant changes international economies and financial markets are undergoing, and the new challenges these changes pose.

£145.00
Availability: In stock
ISBN
978-1-78272-394-3
 

“Economies are complex, adaptive systems full of heterogeneous agents [and markets] whose interactions have thus far been almost impossible to discern.” With the advent of digitalization  or digital evolution (disruption), digital data and rapidly-developing technology are beginning to totally transform the worlds of finance and economics, and society at large.   The new world of Fintech has emerged, disrupting traditional banking and finance and it can no longer be ignored.    

Data Science in Economics and Finance provides an overview of how digital transformation and data science can support decision-making under uncertainty and provides multiple perspectives on managing digital data. Split into four sections this title offers essential insights both to practitioners of data-science tools and techniques as well as to policymakers, who are increasingly dependent on the use of digital data for aiding sustainable decisions for the collective benefit of society.

  • Part I: data science and quality in economics and finance: A broad overview of digitalization and digital transformation in economics and finance covering both their potential and challenges.
  • Part II: data science techniques: Describes the core concepts of data science tools and techniques, including machine learning, AI, network analysis and the expression of digital data using visual techniques.
  • Part III: data: from the experimental to generating insights: Provides an overview of the challenges of building data science infrastructures and provides several “big data” case studies in the public, private and academic sectors.
  • Part IV: digital data vision and the social benefits of big data: Sets out a vision for the free movement of digital data, and discusses the challenges for legal enablers, journalists and a human-centric approach to AI.

 

Data Science in Economics and Finance not only explores new techniques and tools which advance our understanding of systemic processes, such as AI, but also thoughtfully assesses the inadequacies and challenges put forth by the digital revolution, described as a “‘how to’ guide to the future of data and modelling” that will stimulate economists, statisticians, data scientists, central bankers, financial-market participants, regulators, Fintech firms and any decision-makers.”

More Information
ISBN 978-1-78272-394-3
Navision code MHAS
Publication date Spring 2021
Size 155mm x 235 mm
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Per Nymand-Andersen

About the Editor: Per Nymand-Andersen is an adviser to senior management at the European Central Bank. Over the past 20 years of central banking Per has developed his expertise in European banking and financial markets, fintech, data science, securities settlement systems, statistics, digital communication and management. Per is the key editor of the ECB Working Paper Series and a lecturer on central banking policies and transparency at Goethe-Universität Frankfurt. Prior to joining the ECB, he provided market research consultancy services for the European Commission, Luxembourg. Per has anMBAin economics and management science from Copenhagen Business School and a Fintech certificate from Harvard University. Per is a frequent speaker at international events and the author of several publications and articles regarding financial markets, data science, communication and statistics.

Further details can be found at www.linkedin.com/in/per-nymand-andersen-81609913.

About the Authors

David Jamieson Bolder is director of model development and economic capital at the Nordic Investment Bank. Previously, he was in charge of the World Bank Group’s model-risk function. Other jobs include quantitative analytic roles at the Bank for International Settlements, the Bank of Canada, the World Bank Treasury and the European Bank for Reconstruction and Development. He has authored numerous papers, articles and chapters in books on risk-management, financial modelling, stochastic simulation and optimization, in particular two comprehensive books on fixedincome portfolio analytics and credit-risk modelling. His career has focused on the application of mathematical techniques to informing decision-making in the areas of sovereign-debt, pension-fund, portfolio-risk and foreign-reserve management.

Giuseppe Bruno is director IT support for economics and statistics, Bank of Italy. He joined econometrics office of the economic research department the Bank in 1989, where he was responsible for developing procedures for database management and for the estimation and simulation of the Bank’s quarterly econometric model, working with experts from the Federal Reserve’s division of research and statistics and other leading research institutes. In 1993 he spent a year at the University of Pennsylvania developing algorithms for optimal control of econometric models. In 2000, after a short period at the statistical division of the OECD, he was appointed head of the economic research department’s IT unit, coordinating projects for the introduction of new information and communications technology platforms for economic and statistical applications. In 2016 he coordinated a multidisciplinary team on big data with economists, statisticians, computer scientists with a goal of building a hardware/
software infrastructure to deal with big data related to various macroeconomic and microeconomic issues. Over his career he has published over 25 papers in different economic and computational economics journals.

Paola Cerchiello is associate professor of statistics at the department of economics and management of the University of Pavia. She has a BS with honours in economics from the University of Pavia and a PhD in statistics from the University of Milan-Bicocca. She teaches the basics of coding and big data analysis within the Master’s in international business and management programme at the University of Pavia. She is senior data scientist at Fintech Lab, where she has carried out research and consulting projects for the European Union, the Italian Ministry of Research, Cariplo Foundation, the Italian Banking Association, Intesa San Paolo, Mediolanum, Credito Valtellinese, Istituto Credito Sportivo, KPMG, Mediaset, SAS Institute and Sky. She was member of research groups on big data at the Bank of Italy and the Deutche Bundesbank. She is the leader of work packages and research units including Periscope (EUHorizon 2020 on the Covid pandemic economic impact 2020–2023) and an EU fintech project on the assessment of financial risk connected to new financial technologies. Her research activity is mainly devoted to statistical models for unstructured and complex data in economics and finance, focusing on text data analysis, systemic risk, reputational risk, initial coin offerings, cyber risk, sentiment analysis and deep learning. She has published more than 40 papers in scientific international journals.

John W. Galbraith is professor of economics at McGill University, Montreal, and president of the scientific committee at CIRANO, Montreal. He has a doctorate from the University of Oxford and has since worked in the fields of time-series econometrics, forecasting and applications in macroeconomics and financial economics. Information on his recent research may be found via www.mcgill.ca/economics/john-w-galbraith

Raji Ghawi is a post-doctoral researcher at the computational social science and big data group at the Bavarian School of Public Policy of the Technical University of Munich. Previously, he served as an affiliate researcher at the American University of Beirut, Lebanon, and as a lecturer at Al-Baath University, Syria. Raji has a PhD in informatics from the University of Burgundy, France. His research interests include ontologies and the semantic web, database systems, social network analysis, information retrieval and text mining and analytics. Raji has published more than 20 peer-reviewed articles in international journals, and he has participated in programme committees of several computer science conferences.

Riad Hamade has 30 years’ experience as an editor and news manager. He heads Bloomberg News’s global internship and entry-level staff program. He was previously executive editor for the Middle East and Africa, based in Dubai, and led the company’s expansion in some of the fastest growing economies in the world. Until 2016, Hamade was in charge of the Middle East and North Africa region, and between 2001 and 2008, he built up Bloomberg News’s economic coverage inWestern Europe. Before joining Bloomberg, Hamadewas an editor for Bridge News, Frankfurt, and the BBC’s Monitoring Service in the UK. Hamade has a BA in economics from the American University of Beirut and an MSc in economic history from the London School of Economics. 

Daniel Hinge is assistant editor at Central Banking. He has reported on the work of central banks since joining the news desk in 2012 as a reporter, and is editor of an annual survey of central banks on big data and data governance. He holds a degree in politics, philosophy and economics from the University of Oxford.

Juri Marcucci works in the Research Data Center and Innovation Lab in the Directorate General for Economics, Statistics and Research at the Bank of Italy and coordinates the Bank’s task force on big data and machine learning. He represents the Bank on the Big Data Committee at the Italian national statistical institute. His research interests are big data, text mining, machine learning, forecasting and applied econometrics. Juri holds a PhD in economics from the University of California, San Diego; His doctoral thesis on financial econometrics, under the supervision of Nobel Memoriall prizewinner Robert Engle, focused on the predictive ability of regime-switching GARCH models and common features in volatility. Juri has lectured at the University of Bologna, and Tor Vergata University of Rome and is lecturing a data-driven economics course at the Sapienza University of Rome. He was previously a visiting scholar at the Federal Reserve Bank of Boston, University of California San Diego and Universitat Pompeu Fabra. He is the organizer of the Italian Summer School of Econometrics on behalf of the Italian Econometric Association (SIDE). He has guest edited Econometrics and the International Journal of Forecasting. He is co-organizer of a series of webinars on applied machine learning, economics and data science.

Mikhail Oet is an associate teaching professor and the faculty lead in the commerce and economic development graduate programme at Northeastern University and the director of analytics at Financial Network Analytics, a deep technology analytics innovator. Mikhail began his career with the Federal Reserve System, strengthening the resilience of risky and complex financial service organizations.
He later led research, development and extension of supervisory technologies and financial stability analytics at the Federal Reserve Bank of Cleveland. In 2016, he started the Economic Forecasting Group at the Bank of New York Mellon, the world’s largest asset servicing company. He has held teaching positions in economics, finance and supervisory analytics at the Federal Reserve System, Cleveland State University and Case Western Reserve University. He is the author of numerous journal articles, including the Review of Finance, Journal of Financial Stability, Journal of Banking and Finance and European Journal of Finance.

Panagiotis Papapaschalis is senior lead legal counsel at the Directorate General Legal Services of the European Central Bank, dealing with financial market infrastructures (FMIs), fintech and cybersecurity. Prior to that, he has worked as senior counsel (on secondment) at the International Monetary Fund’s legal department, dealing with technical assistance on central bank, banking and FMI legislation, and as legal team leader with the European Securities and Markets Authority. He holds two LLM degrees in financial law (Aristotle University, Greece and Fordham University, USA) and is admitted to practice in New York, Greece and England and Wales. He is an accredited data protection officer.

Jürgen Pfeffer is a professor of Computational social science and big data at the Bavarian School of Public Policy of the Technical University of Munich. He is adjunct professor, Institute for Software Research, School of Computer Science, Carnegie Mellon University, where he served as assistant research professor from 2012 to 2015. Jürgen has a PhD in business informatics from Vienna University of Technology. His research is on the intersection of social science and computer science, focusing on analysing the structure and dynamics of large-scale social, political, economic and technical systems with a special emphasis on the associated algorithmic and methodological challenges. Jürgen has published more than 60 peer-reviewed articles in highly acknowledged journals (eg, Science, Nature Scientific Reports) and participated in more than 70 programme committees of major computer science conferences, including as general chair and host of the 2019 International AAAI Conference on Web and Social Media.

Claudia Quinonez is a managing editor, head of news automation,at Bloomberg News. She has over 20 years of experience at Bloomberg, including as global head of news market specialists, where she led the deployment of news products, audience building and news and information management across the globe, and news product manager, working directly with teams of artificial intelligence engineers to develop AI-based news products. As head of news automation, she oversees the systematic data-driven content creation using high-quality financial data and modelling. Claudia holds a post-graduate diploma in strategies and innovation from Said Business School, Oxford, and is reading for a second master’s degree in philosophy and artificial intelligence at theNewCollege of Humanities, Northeastern University, focusing on the ethics of AI.

Ivana Ruffini manages a team of data scientists at Financial Network Analytics offering tailored analytic solutions to help clients maximize tangible business outcomes. Prior to joining FNA in 2019, Ivana worked for nine years at the Federal Reserve Bank of Chicago, where she used network theory to improve the understanding of the propagation of risk in the global financial system. Ivana was also active in the alternative investment space as a private equity investment professional, a derivatives trader and a credit risk specialist. Her previous employers include Baird Capital Partners, One Equity Partners and JP Morgan. Ivana graduated with honours from Denison University and did her graduate studies at Northwestern University. In addition to incorporating network theory in modeling financial markets, her research interests include the development of predictive analytics that uses machine learning and Bayesian modeling.

Valérie Saintot is a lawyer with more than 25 years of experience in both the private and the public sector. She started her career at the European Court of Justice, Luxembourg. She is the head of the legislation division, Directorate General Legal Services at the European Central Bank. She is a life-long learner and holds postgraduate degrees in law, psychology and management research. Her PhD research includes how knowledge visualization can help teams better share their tacit knowledge in meetings and she is working on developing legislative data visualization methods to help experts
and citizens to navigate online legal frameworks in a more visual way. Valérie is passionate about Europe and has been promoting data-driven and evidence-based dialogue to support insightful decision making and outreach communication. She led a team tasked with the creation of the ECB visitor centre, which opened in 2017 and has used the principles of science communication to make economic and financial knowledge accessible to the general public.

Aurel Schubert is honorary professor and lecturer at the Vienna University of Economics and Business, and at the Vienna School of International Studies. He was director-general of the European Central Bank’s statistics department between 2010 and 2018. In addition, he was chairman of the statistics committee of the European System of Central Banks and of the contact group on data of the European Systemic Risk Board, co-chair of the European Statistical Forum and vice chairman of the Irving Fisher Committee on Central Bank Statistics. Previously, Aurel was director of statistics at the Oesterreichische Nationalbank. He has a PhD in economics from the University of South Carolina, and a Master’s degree in business administration from the Vienna University of Economics and Business. He is the author of a book on the Credit-Anstalt Crisis of 1931, and of more than 50 articles on central banking, European monetary policy, statistics and monetary history.

Kimmo Soramäki is the founder and CEO of Financial Network Analytics (FNA) and the founding editor-in-chief of the Journal of Network Theory in Finance. He started his career as an economist at the Bank of Finland, where, in 1997, he developed the first simulator for interbank payment systems. In 2004, while at the research department of the Federal Reserve Bank of New York, he was among the first to apply methods from network theory to improve our understanding of financial interconnectedness. During the financial crisis of 2007–9, Kimmo advised several central banks, including the Bank of England and European Central Bank, on modeling interconnections and systemic risk. This work led him to found FNA in 2013 in order to solve important issues around financial risk and to explore the complex financial networks that play an increasing role in the world around us. Kimmo holds a Doctor of Science degree in operations research and a Master of Science degree in economics (finance) from Aalto University, Helsinki.

Wienke Strathern is a doctoral candidate and research associate at the professorship of computational social science and big data at the Bavarian School of Public Policy of the Technical University of Munich with Jürgen Pfeffer. Wienke has a MA in linguistics, literature and legal studies from the University of Munich. She works at the intersection of linguistics, social sciences and computer science and her research focuses on dynamics in social media networks with a particular interest in improving automated methods for the quantitative and qualitative analysis of large text corpora. Her projects deal with the analysis of communication networks to better understand and describe the proliferation of opinions and tackling the problem of negative dynamics in social media networks. TuomasTakkois a doctoral candidate in the department of computer science at Aalto University, Finland. His research involves agent based modelling ofhumanbehavior in complex systems represented as networks in the context of game experiments, social media and in the field of cyber security. As a data scientist at Financial Network Analytics, he worked on developing analytic tools and solutions for banking and supervision.

Bruno Tissot is the head of statistics and research support at the Bank for International Settlements and head of the secretariat of the Irving Fisher Committee on Central Bank Statistics. He has worked at the BIS since 2001, first as senior economist and secretary to the markets committee of central banks in the monetary and economic department and then as the adviser to the general manager and secretary to the BIS executive committee. Between 1994 and 2001 he worked for the French Ministry of Finance. He is a graduate of the École Polytechnique (Paris) and the National Institute of Statistics and Economic Studies (INSEE).

Adrian Waddy is a technician with many years’ experience in the business intelligence industry. He was the technical lead on the design phase of the Bank of England’s most recent implementation of a big data platform, which involved working with many expert colleagues from across technology as well as coordinating the inputof external vendors partnering on the project. During 2020 Adrian was seconded to the data innovation team within the Prudential Regulation Authority with responsibility for the technical oversight of cognitive search and text analytics platforms.

Roberto V. Zicari is an affiliated professor at the Yrkeshögskolan Arcada, Helsinki, and an adjunct professor at Seoul National University. He is an internationally recognized expert in the field of databases and big data. His interests also expand to ethics and AI, innovation and entrepreneurship and he leads the team of international experts who defined an assessment process for trustworthy artificial intelligence, called Z-Inspection®. Roberto is the editor of the Operational Database Management Systems web portal and the ODBMS Industry Watch blog. Previously, he was professor of database and information systems at Goethe University, Frankfurt, where he founded the Frankfurt Big Data Lab. He has also served as associate professor at Politecnico di Milano, Italy, and visiting scientist at IBM Almaden Research Center in Silicon Valley, and the University of California at Berkeley, and visiting professor at EPFL in Lausanne, the National University of Mexico City and the Copenhagen Business School.

Introduction to big data in economic and finance:

  1. Foreword - Tom Davenport
  2. Intro - Per Nymand-Andersen

P1: Data Science and Quality in Economics and Finance

  1. Digitalisation and transformation in economics and finance - Per Nymand-Andersen
  2. Big data for policy making in economics and finance: the potential and challenges - Aurel Schubert
  3. Quality matters: for insightful quality advice, get to know your big data - Per Nymand-Andersen

P2: Data Science Techniques 

  1. Statistics and machine learning: variations on a theme - David Bolder
  2. Advanced statistical analysis of large-scale Web-based data - Juergen Pfeffer, Wienke Strathern and Raji Ghawi
  3. Text analysis - Paola Cerchiello
  4. Prudential stress testing in financial networks - Kimmo Soramäki & Adam Csabay & Ivana Ruffini & Mikhail Oet & Tuomas Takko
  5. Data visualisation: developing capabilities to make decisions and communicate - Valerie Saintot

P3: From Experimental to Generating Insights 

  1. Data science in economics and finance: tools, infrastructure and challenges - Bruno Tissot
  2. Data science and machine learning for a data-driven central bank - Juri Marcucci & Giuseppe Bruno
  3. Large-scale commercial data for economic analysis - John Galbraith
  4. Artificial intelligence and data are transforming the modern newsroom: a Bloomberg casestudy - Riad Hamade & Claudia Quinonez
  5. Implementing big data solutions - Adrian Waddy

P4: Digital Data Vision and Social Benefits of Big Data 

  1. A borderless market for digital data - Per Nymand-Andersen
  2. Legal/ethical aspects and privacy: enabling free data flows - Panagiotis Papaschalis
  3. Assessing trustworthy artificial intelligence - Roberto Zicari
  4. Big tech, journalism and the future of knowledge - Daniel Hinge