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The recent global financial crisis has forced a re-examination of risk transmission in the financial sector and how it affects financial stability. Current macroprudential policy and surveillance (MPS) efforts are aimed establishing a regulatory framework that helps mitigate the risk from systemic linkages with a view towards enhancing the resilience of the financial sector. This paper presents a forward-looking framework ("Systemic CCA") to measure systemic solvency risk based on market-implied expected losses of financial institutions with practical applications for the financial sector risk management and the system-wide capital assessment in top-down stress testing. The suggested approach uses advanced contingent claims analysis (CCA) to generate aggregate estimates of the joint default risk of multiple institutions as a conditional tail expectation using multivariate extreme value theory (EVT). In addition, the framework also helps quantify the individual contributions to systemic risk and contingent liabilities of the financial sector during times of stress.
This is a major new reference work covering all aspects of finance. Coverage includes finance (financial management, security analysis, portfolio management, financial markets and instruments, insurance, real estate, options and futures, international finance) and statistical applications in finance (applications in portfolio analysis, option pricing models and financial research). The project is designed to attract both an academic and professional market. It also has an international approach to ensure its maximum appeal. The Editors' wish is that the readers will find the encyclopedia to be an invaluable resource.
March 29, 1900, is considered by many to be the day mathematical finance was born. On that day a French doctoral student, Louis Bachelier, successfully defended his thesis Théorie de la Spéculation at the Sorbonne. The jury, while noting that the topic was "far away from those usually considered by our candidates," appreciated its high degree of originality. This book provides a new translation, with commentary and background, of Bachelier's seminal work. Bachelier's thesis is a remarkable document on two counts. In mathematical terms Bachelier's achievement was to introduce many of the concepts of what is now known as stochastic analysis. His purpose, however, was to give a theory for the valuation of financial options. He came up with a formula that is both correct on its own terms and surprisingly close to the Nobel Prize-winning solution to the option pricing problem by Fischer Black, Myron Scholes, and Robert Merton in 1973, the first decisive advance since 1900. Aside from providing an accurate and accessible translation, this book traces the twin-track intellectual history of stochastic analysis and financial economics, starting with Bachelier in 1900 and ending in the 1980s when the theory of option pricing was substantially complete. The story is a curious one. The economic side of Bachelier's work was ignored until its rediscovery by financial economists more than fifty years later. The results were spectacular: within twenty-five years the whole theory was worked out, and a multibillion-dollar global industry of option trading had emerged.
Modeling the Term Structure of Interest Rates provides a comprehensive review of the continuous-time modeling techniques of the term structure applicable to value and hedge default-free bonds and other interest rate derivatives.
The essential premise of this book is that theory and practice are equally important in describing financial modeling. In it the authors try to strike a balance in their discussions between theories that provide foundations for financial models and the institutional details that provide the context for applications of the models. The book presents the financial models of stock and bond options, exotic options, investment grade and high-yield bonds, convertible bonds, mortgage-backed securities, liabilities of financial institutions--the business model and the corporate model. It also describes the applications of the models to corporate finance. Furthermore, it relates the models to financial statements, risk management for an enterprise, and asset/liability management with illiquid instruments. The financial models are progressively presented from option pricing in the securities markets to firm valuation in corporate finance, following a format to emphasize the three aspects of a model: the set of assumptions, the model specification, and the model applications. Generally, financial modeling books segment the world of finance as "investments," "financial institutions," "corporate finance," and "securities analysis," and in so doing they rarely emphasize the relationships between the subjects. This unique book successfully ties the thought processes and applications of the financial models together and describes them as one process that provides business solutions. Created as a companion website to the book readers can visit www.thomasho.com to gain deeper understanding of the book's financial models. Interested readers can build and test the models described in the book using Excel, and they can submit their models to the site. Readers can also use the site's forum to discuss the models and can browse server based models to gain insights into the applications of the models. For those using the book in meetings or class settings the site provides Power Point descriptions of the chapters. Students can use available question banks on the chapters for studying.
The increased volatility of interest rates during recent years and the corresponding introduction of a variety of interest rate derivative securities like bond options, futures and embedded options in mortgages, underlines the need for a comprehensive financial theory to determine values of fixed income instruments and derivative securities consistently. This book provides: * a detailed overview and classification of the different approaches to value interest rate dependent securities * a comparison of the numerical approaches to value complex securities * an empirical examination for the Dutch Fixed Income Market of some well-known interest rate models which demonstrates recent improvements to describe interest rate movements in relation to contingent claim valuation.
Neoclassical Finance provides a concise and powerful account of the underlying principles of modern finance, drawing on a generation of theoretical and empirical advances in the field. Stephen Ross developed the no arbitrage principle, tying asset pricing to the simple proposition that there are no free lunches in financial markets, and jointly with John Cox he developed the related concept of risk-neutral pricing. In this book Ross makes a strong case that these concepts are the fundamental pillars of modern finance and, in particular, of market efficiency. In an efficient market prices reflect the information possessed by the market and, as a consequence, trading schemes using commonly available information to beat the market are doomed to fail. By stark contrast, the currently popular stance offered by behavioral finance, fueled by a number of apparent anomalies in the financial markets, regards market prices as subject to the psychological whims of investors. But without any appeal to psychology, Ross shows that neoclassical theory provides a simple and rich explanation that resolves many of the anomalies on which behavioral finance has been fixated. Based on the inaugural Princeton Lectures in Finance, sponsored by the Bendheim Center for Finance of Princeton University, this elegant book represents a major contribution to the ongoing debate on market efficiency, and serves as a useful primer on the fundamentals of finance for both scholars and practitioners.
This text seeks to teach the basics of fixed-income securities in a way that requires a minimum of prerequisites. Its approach - the Heath Jarrow Morton model - under which all other models are presented as special cases, aims to enhance understanding while avoiding repetition.
The underlying theme of this volume is how to invest assets over time to achieve satisfactory returns subject to uncertainties, various constraints and liability commitments. Most investors, be they individuals or institutions, do not diversify properly across markets nor across time. The papers utilize several approaches and integrate a number of techniques as well as discussing a variety of models that have either been implemented, are close to being implemented, or represent new innovative approaches that may lead to future novel applications. Other issues address the future of asset-liability management modeling. This includes models for individuals, and various financial institutions such as banks and insurance companies. This will lead to custom products, that is, financial engineering. All in all, this will be essential reading for all involved in analysing the financial markets.
This book examines the applicability of a relatively new and powerful tool, genetic adaptive neural networks, to the field of option valuation. A genetic adaptive neural network model is developed to price option contracts with futures-style margining. This model is capable of estimating complex, non-linear relationships without having prior knowledge of the specific nature of the relationships. Traditional option pricing models require that the researcher or practitioner specify the distribution of the underlying asset. In addition, the methodology is able to easily accommodate additional inputs(something that cannot be preformed with existing models. Since 1973, options on stock have been traded on organized exchanges in the United States. An option on a stock gives the option owner the right to buy or sell the stock for a pre-set price.. Since the introduction of stock options, the options market has experienced tremendous growth and has spawned even more exotic types of derivative securities. Obviously, valuing these securities is an issue of great importance to investors and hedgers in the financial marketplace. Existing pricing models produce systematic pricing errors and new models have to be developed for options with differing characteristics. The genetic adaptive neural network is found to provide more accurate valuation than a traditional option pricing model when applied to the 3-month Eurodollar futures-option contract traded on the London International Financial Futures and Options Exchange.