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.

This book covers the growing convergence between Blockchain and Artificial Intelligence for Big Data, Multi-Agent systems, the Internet of Things and 5G technologies. Using real case studies and project outcomes, it illustrates the intricate details of blockchain in these real-life scenarios. The contributions from this volume bring a state-of-the-art assessment of these rapidly evolving trends in a creative way and provide a key resource for all those involved in the study and practice of AI and Blockchain.

The book offers an introduction to the technical foundation of decentralized insurance models, for advanced undergraduate students, graduate students and practitioners. The book is self-contained and anyone with a basic knowledge of probability and statistics should be able to follow through the entire book. It adopts a minimalist approach to describe the essential elements and first principles so that readers can get a gist of these models without being overwhelmed with too much technicality. It can be used as a reference for business model designs. The inclusion of exercises and practical examples makes the book suitable for advanced courses on decentralized insurance and risk sharing. There is a mix of industry practices and academic models presented in this book. The exposition starts with an overview of historic and current business practices and preliminaries on the mathematics and economics of risk and insurance. A bird's-eye view of traditional insurance is provided as a benchmark for various topics to be used in contrast with decentralized insurance. The book then continues with decentralized insurance practices around the world, including online mutual aid originated in China, takaful from the Islamic world, peer-to-peer insurance in the West, catastrophe risk pooling for Carribean countries, etc. Theories of aggregate risk pooling and peer-to-peer risk exchanges are provided for readers to appreciate the mathematical foundation of risk sharing. A unified framework of decentralized insurance is presented to show a structured approach to the economic design of decentralized business models. The book ends with a technical review of blockchain and decentralized finance (DeFi) insurance applications.

This book describes several techniques, first invented in physics for solving problems of heat and mass transfer, and applies them to various problems of mathematical finance defined in domains with moving boundaries. These problems include: (a) semi-closed form pricing of options in the one-factor models with time-dependent barriers (Bachelier, Hull-White, CIR, CEV); (b) analyzing an interconnected banking system in the structural credit risk model with default contagion; (c) finding first hitting time density for a reducible diffusion process; (d) describing the exercise boundary of American options; (e) calculating default boundary for the structured default problem; (f) deriving a semi-closed form solution for optimal mean-reverting trading strategies; to mention but some.The main methods used in this book are generalized integral transforms and heat potentials. To find a semi-closed form solution, we need to solve a linear or nonlinear Volterra equation of the second kind and then represent the option price as a one-dimensional integral. Our analysis shows that these methods are computationally more efficient than the corresponding finite-difference methods for the backward or forward Kolmogorov PDEs (partial differential equations) while providing better accuracy and stability.We extend a large number of known results by either providing solutions on complementary or extended domains where the solution is not known yet or modifying these techniques and applying them to new types of equations, such as the Bessel process. The book contains several novel results broadly applicable in physics, mathematics, and engineering.

This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. It contains primers on operations, finance, and their interface. Innovative technologies and new business models enabled by those technologies are changing the practice and the theory of Operations Management and Finance, as well as their interface. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The book will be an attractive choice for PhD-level courses and for self-study.

Part of The Elgar Series on Central Banking and Monetary Policy, this book explores the relationship between central banking, monetary policy and the economy at large, focusing on the specific relationship between central banking, monetary policy and the future of money.

This edited book explores the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance. While the general use of big data has been the subject of frequent discussions, this book will take a more focused look at big data applications in the financial sector. With contributions from researchers, practitioners, and entrepreneurs involved at the forefront of big data in finance, the book discusses technological and business-inspired breakthroughs in the field. The contributions offer technical insights into the different applications presented and highlight how these new developments may impact and contribute to the evolution of the financial sector. Additionally, the book presents several case studies that examine practical applications of big data in finance. In exploring the readiness of financial institutions to adapt to new developments in the big data/artificial intelligence space and assessing different implementation strategies and policy solutions, the book will be of interest to academics, practitioners, and regulators who work in this field.

This book contains contributions by the best-known and consequential researchers who, over several decades, shaped the field of financial engineering. It presents a comprehensive and unique perspective on the historical development and the current state of derivatives research. The book covers classical and modern approaches to option pricing, realized and implied volatilities, classical and rough stochastic processes, and contingent claims analysis in corporate finance. The book is invaluable for students, academic researchers, and practitioners working with financial derivatives, market regulation, trading, risk management, and corporate decision-making.