From the late nineties, the spectacular growth of a secondary market for credit through derivatives has been matched by the emergence of mathematical modeling analysing the credit risk embedded in these contracts. This book aims to provide a broad and deep overview of this modeling, covering statistical analysis and techniques, modeling of default of both single and multiple entities, counterparty risk, Gaussian and non-Gaussian modeling, and securitization. Both reduced-form and firm-value models for the default of single entities are considered in detail, with extensive discussion of both their theoretical underpinnings and practical usage in pricing and risk. For multiple entity modeling, the now notorious Gaussian copula is discussed with analysis of its shortcomings, as well as a wide range of alternative approaches including multivariate extensions to both firm-value and reduced form models, and continuous-time Markov chains. One important case of multiple entities modeling--counterparty risk in credit derivatives--is further explored in two dedicated chapters. Alternative non-Gaussian approaches to modelling are also discussed, including extreme-value theory and saddle-point approximations to deal with tail risk. Finally, the recent growth in securitization is covered, including house price modeling and pricing models for asset-backed CDOs.The current credit crisis has brought modeling of the previously arcane credit markets into the public arena. Lipton and Rennie with their excellent team of contributors, provide a timely discussion of the mathematical modeling that underpins both credit derivatives and securitization. Though technical in nature, the pros and cons of various approaches attempt to provide a balanced view of the role that mathematical modeling plays in the modern credit markets. This Handbook will appeal to students and researchers in statistics, economics, and finance, as well as practicioners, credit traders, and quantitative analysts.

The Oxford Handbook of Credit Derivatives (Oxford Handbooks)

Business & Money

Rennie, Andrew

Lipton, Alexander

Economics

Oxford University Press

From the late 1990s, the spectacular growth of a secondary market for credit through derivatives has been matched by the emergence of mathematical modelling analysing the credit risk embedded in these contracts. This book aims to provide a broad and deep overview of this modelling, covering statistical analysis and techniques, modelling of default of both single and multiple entities, counterparty risk, Gaussian and non-Gaussian modelling, and securitisation. Both reduced-form and firm-value models for the default of single entities are considered in detail, with extensive discussion of both their theoretical underpinnings and practical usage in pricing and risk. For multiple entity modelling, the now notorious Gaussian copula is discussed with analysis of its shortcomings, as well as a wide range of alternative approaches including multivariate extensions to both firm-value and reduced form models, and continuous-time Markov chains. One important case of multiple entities modelling - counterparty risk in credit derivatives - is further explored in two dedicated chapters. Alternative non-Gaussian approaches to modelling are also discussed, including extreme-value theory and saddle-point approximations to deal with tail risk. Finally, the recent growth in securitisation is covered, including house price modelling and pricing models for asset-backed CDOs. The current credit crisis has brought modelling of the previously arcane credit markets into the public arena. Lipton and Rennie with their excellent team of contributors, provide a timely discussion of the mathematical modelling that underpins both credit derivatives and securitisation. Though technical in nature, the pros and cons of various approaches attempt to provide a balanced view of the role that mathematical modelling plays in the modern credit markets. This book will appeal to students and researchers in statistics, economics, and finance, as well as practitioners, credit traders, and quantitative analysts

The Oxford Handbook of Credit Derivatives (Oxford Handbooks in Finance)

Product Description 
How to empower people and communities with user-centric data ownership, transparent and accountable algorithms, and secure digital transaction systems. \nData is now central to the economy, government, and health systems—so why are data and the AI systems that interpret the data in the hands of so few people? 
Building the New Economy calls for us to reinvent the ways that data and artificial intelligence are used in civic and government systems. Arguing that we need to think about data as a new type of capital, the authors show that the use of data trusts and distributed ledgers can empower people and communities with user-centric data ownership, transparent and accountable algorithms, machine learning fairness principles and methodologies, and secure digital transaction systems. \nIt’s well known that social media generate disinformation and that mobile phone tracking apps threaten privacy. But these same technologies may also enable the creation of more agile systems in which power and decision-making are distributed among stakeholders rather than concentrated in a few hands. Offering both big ideas and detailed blueprints, the authors describe such key building blocks as data cooperatives, tokenized funding mechanisms, and tradecoin architecture. They also discuss technical issues, including how to build an ecosystem of trusted data, the implementation of digital currencies, and interoperability, and consider the evolution of computational law systems.\nReview 
“Beating the pandemic is a tough challenge, but also an opportunity to retool for the bigger disruptions of digitization and AI. 
Building the New Economy brings together the best thinking on exactly how to do that, with perfect combination of rigor and relevance.” 
— 
Erik Brynjolfsson, Stanford University\n“These essays together offer depth and a path forward. Required reading for a sane future.” 
— 
Lawrence Lessig, Harvard University\n“Each chapter of 
Building the New Economy tackles an urgent and important societal question, from how to build effective health surveillance without a national healthcare system to how to protect people’s right to control their own data while exploiting its economic value, to how to enjoy the economic benefits of asset-backed securitization while reducing fraud. This boundary-pushing preview of the future we could have is a must-read for policymakers, thought leaders, and all of us wishing to reimagine and build a better society on the other side of the pandemic.” 
— 
Beth Noveck, former Deputy CTO, United States\n“An assembly of some of the brightest minds in engineering and data science, 
Building the New Economy bravely tackles not only what the economy needs of technology in our crisis-riddled age, but also what technology demands of financial ecosystems in order for it to make our lives more efficient, fairer, and safer. An ingenious collection of essays, the book provides a fresh new perspective on some of the most cutting-edge technologies of our time.” 
— 
Chris Brummer, Georgetown University 
 About the Author 
Alex “Sandy” Pentland directs the MIT-wide initiative MITConnection Science. Called one of the “seven most powerful data scientists in the world” by 
Forbes, he has cofounded more than a dozen companies and is the author of 
Honest Signals (MIT Press) and 
Social Physics. Alexander Lipton is Professor and Dean’s Fellow at the Jerusalem Business School at the Hebrew University of Jerusalem, Cofounder and Chief Information Officer at Sila, and Connection Fellow at MIT. Thomas Hardjono is Technology Director of the MIT Connection Science and the MIT Trust::Data Consortium.

Building the New Economy: Data as Capital

Pentland, Alex

Hardjono, Thomas

Reference

MIT Connection Science & Engineering

Hydrodynamics of Markets: Hidden Links between Physics and Finance (Elements in Quantitative Finance)

Finance

Cambridge University Press

This comprehensive book presents a systematic and practically oriented approach to mathematical modeling in finance, particularly in the foreign exchange context. It describes all the relevant aspects of financial engineering, including derivative pricing, in detail. The book is self-contained, with the necessary mathematical, economic, and trading background carefully explained. In addition to the lucid treatment of the standard material, it describes many original results.The book can be used both as a text for students of financial engineering, and as a basic reference for risk managers, traders, and academics.

Mathematical Methods for Foreign Exchange: A Financial Engineer's Approach

World Scientific Publishing Company

MATHEMATICAL METHODS FOR FOREIGN EXCHANGE: A FINANCIAL ENGINEER'S APPROACH

Blockchain And Distributed Ledgers: Mathematics, Technology, And Economics

This textbook focuses on distributed ledger technology (DLT) and its potential impact on society at large. It aims to offer a detailed and self-contained introduction to the founding principles behind DLT accessible to a well-educated but not necessarily mathematically oriented audience. DLT allows solving many complicated problems arising in economics, banking, and finance, industry, trade, and other fields. However, to reap the ultimate benefits, one has to overcome some of its inherent limitations and use it judiciously. Not surprisingly, amid increasing applications of DLT, misconceptions are formed over its use. The book thoroughly dispels these misconceptions via an impartial assessment of the arguments rooted in scientific reasoning. Blockchain and Distributed Ledgers: Mathematics, Technology, and Economics offers a detailed and self-contained introduction to DLT, blockchains, and cryptocurrencies and seeks to equip the reader with an ability to participate in the crypto economy meaningfully.

Engineering & Transportation

Treccani, Adrien

Engineering

WSPC

"Explains clearly the ideas underpinning DLT and dispels numerous misconceptions surrounding this technology in general and cryptocurrencies in particular Provides all the necessary theoretical background allowing one to understand how DLT operates in both theory and practice"--

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