This outstanding collection of articles includes papers presented at the Fields Institute, Toronto, as part of the Thematic Program in Quantitative Finance that took place in the first six months of the year 2010. The scope of the volume in very broad, including papers on foundational issues in mathematical finance, papers on computational finance, and papers on derivatives and risk management. Many of the articles contain path-breaking insights that are relevant to the developing new order of post-crisis financial risk management.
Finally, a comprehensive book on land conservation financing for community and regional conservation leaders. A Field Guide to Conservation Finance provides essential advice on how to tackle the universal obstacle to protecting private land in America: lack of money. Story Clark dispels the myths that conservationists can access only private funds controlled by individuals or that only large conservation organizations have clout with big capital markets. She shows how small land conservation organizations can achieve conservation goals using both traditional and cutting-edge financial strategies. Clark outlines essential tools for raising money, borrowing money, and reducing the cost of transactions. She covers a range of subjects including transfer fees, voluntary surcharges, seller financing, revolving funds, and Project Related Investment programs (PRIs). A clear, well-written overview of the basics of conservation finance with useful insights and real stories combine to create a book that is an invaluable and accessible guide for land trusts seeking to protect more land.
Fields of Gold critically examines the history, ideas, and political struggles surrounding the financialization of farmland. In particular, Madeleine Fairbairn focuses on developments in two of the most popular investment locations, the US and Brazil, looking at the implications of financiers' acquisition of land and control over resources for rural livelihoods and economic justice. At the heart of Fields of Gold is a tension between efforts to transform farmland into a new financial asset class, and land's physical and social properties, which frequently obstruct that transformation. But what makes the book unique among the growing body of work on the global land grab is Fairbairn's interest in those acquiring land, rather than those affected by land acquisitions. Fairbairn's work sheds ethnographic light on the actors and relationships—from Iowa to Manhattan to São Paulo—that have helped to turn land into an attractive financial asset class. Thanks to generous funding from UC Santa Cruz, the ebook editions of this book are available as Open Access volumes from Cornell Open (cornellpress.cornell.edu/cornell-open) and other repositories.
"Filled with crystal-clear examples, the book helps you understand: balance sheets and income/cash flow statements; annual reports; fixed-cost and variable-cost issues; financial analysis, budgeting, and forecasting; and much more"--Back cover.
This book provides an introduction to how the mathematical tools from quantum field theory can be applied to economics and finance. Providing a range of quantum mathematical techniques for designing financial instruments, it demonstrates how a range of topics have quantum mechanical formulations, from asset pricing to interest rates.
Tools for Computational Finance offers a clear explanation of computational issues arising in financial mathematics. The new third edition is thoroughly revised and significantly extended, including an extensive new section on analytic methods, focused mainly on interpolation approach and quadratic approximation. Other new material is devoted to risk-neutrality, early-exercise curves, multidimensional Black-Scholes models, the integral representation of options and the derivation of the Black-Scholes equation. New figures, more exercises, and expanded background material make this guide a real must-to-have for everyone working in the world of financial engineering.
This self-contained volume brings together a collection of chapters by some of the most distinguished researchers and practitioners in the field of mathematical finance and financial engineering. Presenting state-of-the-art developments in theory and practice, the book has real-world applications to fixed income models, credit risk models, CDO pricing, tax rebates, tax arbitrage, and tax equilibrium. It is a valuable resource for graduate students, researchers, and practitioners in mathematical finance and financial engineering.
Decades before Occupy Wall Street challenged the American financial system, activists began organizing alternatives to provide capital to “unbankable” communities and the poor. With roots in the civil rights, anti-poverty, and other progressive movements, they brought little training in finance. They formed nonprofit loan funds, credit unions, and even a new bank—organizations that by 1992 became known as “community development financial institutions,” or CDFIs. By melding their vision with that of President Clinton, CDFIs grew from church basements and kitchen tables to number more than 1,000 institutions with billions of dollars of capital. They have helped transform community development by providing credit and financial services across the United States, from inner cities to Native American reservations. Democratizing Finance traces the roots of community development finance over two centuries, a history that runs from Benjamin Franklin, through an ill-starred bank for African American veterans of the Civil War, the birth of the credit union movement, and the War on Poverty. Drawn from hundreds of interviews with CDFI leaders, presidential archives, and congressional testimony, Democratizing Finance provides an insider view of an extraordinary public policy success. Democratizing Finance is a unique resource for practitioners, policymakers, researchers, and social investors.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
The book offers an interdisciplinary perspective on finance, with a special focus on stock markets. It presents new methodologies for analyzing stock markets’ behavior and discusses theories and methods of finance from different angles, such as the mathematical, physical and philosophical ones. The book, which aims at philosophers and economists alike, represents a rare yet important attempt to unify the externalist with the internalist conceptions of finance.
