This book undertakes a detailed construction of Dynamic Markov Bridges using a combination of theory and real-world applications to drive home important concepts and methodologies. In Part I, theory is developed using tools from stochastic filtering, partial differential equations, Markov processes, and their interplay. Part II is devoted to the applications of the theory developed in Part I to asymmetric information models among financial agents, which include a strategic risk-neutral insider who possesses a private signal concerning the future value of the traded asset, non-strategic noise traders, and competitive risk-neutral market makers. A thorough analysis of optimality conditions for risk-neutral insiders is provided and the implications on equilibrium of non-Gaussian extensions are discussed. A Markov bridge, first considered by Paul Lévy in the context of Brownian motion, is a mathematical system that undergoes changes in value from one state to another when the initial and final states are fixed. Markov bridges have many applications as stochastic models of real-world processes, especially within the areas of Economics and Finance. The construction of a Dynamic Markov Bridge, a useful extension of Markov bridge theory, addresses several important questions concerning how financial markets function, among them: how the presence of an insider trader impacts market efficiency; how insider trading on financial markets can be detected; how information assimilates in market prices; and the optimal pricing policy of a particular market maker. Principles in this book will appeal to probabilists, statisticians, economists, researchers, and graduate students interested in Markov bridges and market microstructure theory.

Financial Markets in Practice: From Post-Crisis Intermediation to FinTechs delivers an overview of the development of risk-transformation undertaken by the financial services industry from the perspective of quantitative finance. It provides an instructional and comprehensive explanation of the structure of the financial system as a network of risk suppliers and risk consumers, where different categories of market participants buy, transform, net, and re-sell different kinds of risks. This risk-transformation oriented view is supported by the changes that followed the last global financial crisis: consumers of financial products asked for less complex risk transformations, regulators demanded limiting risks inside financial institutions to the maximum extent possible, and market participants turned to run mass market-like businesses and away from bespoke 'haute couture'-like businesses.This book portrays the network of intermediaries that compose the financial system, describes their most common business models, explains the exact role of each kind of market participant, and underlines the interaction between them. It seeks to reveal the potential disintermediation that could occur inside the financial sector, led by FinTechs and Artificial Intelligence-based innovations.Readers are invited to reconsider the role of market participants in the post-crisis world and are prepared for the next wave of changes driven by data science, AI, and blockchain. Amid these innovations, quantitative finance will be increasingly involved in all aspects of the financial system. This handy resource helps practitioners from both the buy-side and sell-side gain insights to, and provides an overview of, business models in the financial system from an intermediation perspective, and guides students to comprehensively understand the complex ecosystem in which they will evolve.

Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.

The ways financial analysts, traders, and other specialists use information and learn from each other are of fundamental importance to understanding how markets work and prices are set. This graduate-level textbook analyzes how markets aggregate information and examines the impacts of specific market arrangements--or microstructure--on the aggregation process and overall performance of financial markets. Xavier Vives bridges the gap between the two primary views of markets--informational efficiency and herding--and uses a coherent game-theoretic framework to bring together the latest results from the rational expectations and herding literatures. Vives emphasizes the consequences of market interaction and social learning for informational and economic efficiency. He looks closely at information aggregation mechanisms, progressing from simple to complex environments: from static to dynamic models; from competitive to strategic agents; and from simple market strategies such as noncontingent orders or quantities to complex ones like price contingent orders or demand schedules. Vives finds that contending theories like informational efficiency and herding build on the same principles of Bayesian decision making and that "irrational" agents are not needed to explain herding behavior, booms, and crashes. As this book shows, the microstructure of a market is the crucial factor in the informational efficiency of prices. Provides the most complete analysis of the ways markets aggregate information Bridges the gap between the rational expectations and herding literatures Includes exercises with solutions Serves both as a graduate textbook and a resource for researchers, including financial analysts

The Brody-Hughston-Macrina approach to information-based asset pricing introduces a new way of looking at the mechanisms determining price movements in financial markets. The resulting theory of financial informatics is applicable across a wide range of asset classes and is distinguished by its emphasis on the explicit modelling of market information flows. In the BHM theory, each asset is defined by a collection of cash flows and each such cash flow is associated with a family of one or more so-called information processes that provide partial information about the cash flow. The theory is highly appealing on an intuitive basis: it is directly applicable to trading, investment and risk management - and yet at the same time leads to interesting mathematics. The present volume brings together a collection of 18 foundational papers of the subject by Brody, Hughston, and Macrina, many written in collaboration with various co-authors. There is a preface summarizing the current status of the theory, together with a brief history and bibliography of the subject. This book will be of great interest both to newcomers to financial mathematics as well as to established researchers in the subject.

The Derivatives Sourcebook is a citation study and classification system that organizes the many strands of the derivatives literature and assigns each citation to a category. Over 1800 research articles are collected and organized into a simple web-based searchable database. We have also included the 1997 Nobel lectures of Robert Merton and Myron Scholes as a backdrop to this literature.

Financial engineering and electrical engineering are seemingly different areas that share strong underlying connections. Both areas rely on statistical analysis and modeling of systems; either modeling the financial markets or modeling wireless communication channels. Having a model of reality allows us to make predictions and to optimize the strategies. It is as important to optimize our investment strategies in a financial market as it is to optimize the signal transmitted by an antenna in a wireless link. This monograph provides a survey of financial engineering from a signal processing perspective, that is, it reviews financial modeling, the design of quantitative investment strategies, and order execution with comparison to seemingly different problems in signal processing and communication systems, such as signal modeling, filter/beamforming design, network scheduling, and power allocation.