Recent revolutions in the world of finance have created a need for the expertise of research mathematicians in solving problems. The articles in this volume are based on recent research in methods in mathematical finance.

A comprehensive, in-depth, and authoritative guide to China's financial system The Chinese economy is one of the most important in the world, and its success is driven in large part by its financial system. Though closely scrutinized, this system is poorly understood and vastly different than those in the West. The Handbook of China's Financial System will serve as a standard reference guide and invaluable resource to the workings of this critical institution. The handbook looks in depth at the central aspects of the system, including banking, bonds, the stock market, asset management, the pension system, and financial technology. Each chapter is written by leading experts in the field, and the contributors represent a unique mix of scholars and policymakers, many with firsthand knowledge of setting and carrying out Chinese financial policy. The first authoritative volume on China's financial system, this handbook sheds new light on how it developed, how it works, and the prospects and direction of significant reforms to come. Contributors include Franklin Allen, Marlene Amstad, Kaiji Chen, Tuo Deng, Hanming Fang, Jin Feng, Tingting Ge, Kai Guo, Zhiguo He, Yiping Huang, Zhaojun Huang, Ningxin Jiang, Wenxi Jiang, Chang Liu, Jun Ma, Yanliang Mao, Fan Qi, Jun Qian, Chenyu Shan, Guofeng Sun, Xuan Tian, Chu Wang, Cong Wang, Tao Wang, Wei Xiong, Yi Xiong, Tao Zha, Bohui Zhang, Tianyu Zhang, Zhiwei Zhang, Ye Zhao, and Julie Lei Zhu.

This is a graduate level work covering the economic principles of security markets. Interested readers include students and researchers in economics and finance, as well as financial analysts following the latest theoretical developments in capital asset pricing.

Recent revolutions in the world of finance have created a need for the expertise of research mathematicians in solving problems. The articles in this volume are based on recent research in methods in mathematical finance.

This is a thoroughly updated edition of "Dynamic Asset Pricing Theory," the standard text for doctoral students and researchers on the theory of asset pricing and portfolio selection in multiperiod settings under uncertainty. The asset pricing results are based on the three increasingly restrictive assumptions: absence of arbitrage, single-agent optimality, and equilibrium. These results are unified with two key concepts, state prices and martingales. Technicalities are given relatively little emphasis, so as to draw connections between these concepts and to make plain the similarities between discrete and continuous-time models.

Readers will be particularly intrigued by this latest edition's most significant new feature: a chapter on corporate securities that offers alternative approaches to the valuation of corporate debt. Also, while much of the continuous-time portion of the theory is based on Brownian motion, this third edition introduces jumps--for example, those associated with Poisson arrivals--in order to accommodate surprise events such as bond defaults. Applications include term-structure models, derivative valuation, and hedging methods. Numerical methods covered include Monte Carlo simulation and finite-difference solutions for partial differential equations. Each chapter provides extensive problem exercises and notes to the literature. A system of appendixes reviews the necessary mathematical concepts. And references have been updated throughout. With this new edition, "Dynamic Asset Pricing Theory" remains at the head of the field.

Post-crisis capital regulations and new failure-resolution rules increased the funding costs that are borne by bank shareholders, and thus the cost to buy-side firms for access to space on the balance sheets of large banks. A policy implication is the encouragement of market infrastructure and trading methods that reduce the amount of space on bank balance sheets that is needed to conduct a given amount of trade. Using models and evidence, this book addresses the implications for financial-market liquidity of these regulations for systemically important banks and argues that current rules do not allow for potential levels of market efficiency and financial stability. In this insightful analysis of the impact of regulation on financial market efficiency post-2008, the author argues that bank capital levels could actually be pushed higher while still improving the liquidity of markets for safe assets such as low-risk fixed-income instruments by relaxing the leverage-ratio rule and increasing risk-based capital requirements.

This book, based on the author's Clarendon Lectures in Finance, examines the empirical behaviour of corporate default risk. A new and unified statistical methodology for default prediction, based on stochastic intensity modeling, is explained and implemented with data on U.S. public corporations since 1980. Special attention is given to the measurement of correlation of default risk across firms. The underlying work was developed in a series of collaborations over roughly the past decade with Sanjiv Das, Andreas Eckner, Guillaume Horel, Nikunj Kapadia, Leandro Saita, and Ke Wang. Where possible, the content based on methodology has been separated from the substantive empirical findings, in order to provide access to the latter for those less focused on the mathematical foundations. A key finding is that corporate defaults are more clustered in time than would be suggested by their exposure to observable common or correlated risk factors. The methodology allows for hidden sources of default correlation, which are particularly important to include when estimating the likelihood that a portfolio of corporate loans will suffer large default losses. The data also reveal that a substantial amount of power for predicting the default of a corporation can be obtained from the firm's "distance to default," a volatility-adjusted measure of leverage that is the basis of the theoretical models of corporate debt pricing of Black, Scholes, and Merton. The findings are particularly relevant in the aftermath of the financial crisis, which revealed a lack of attention to the proper modelling of correlation of default risk across firms.

This book, based on the author's Clarendon Lectures in Finance, examines the empirical behavior of corporate default risk. A new and unified statistical methodology for default prediction, based on stochastic intensity modeling, is explained and implemented with data on U.S. public corporations since 1980. Special attention is given to the measurement of correlation of default risk across firms. The underlying work was developed in a series of collaborations over roughly the past decade with Sanjiv Das, Andreas Eckner, Guillaume Horel, Nikunj Kapadia, Leandro Saita, and Ke Wang. Where possible, the content based on methodology has been separated from the substantive empirical findings, in order to provide access to the latter for those less focused on the mathematical foundations.
A key finding is that corporate defaults are more clustered in time than would be suggested by their exposure to observable common or correlated risk factors. The methodology allows for hidden sources of default correlation, which are particularly important to include when estimating the likelihood that a portfolio of corporate loans will suffer large default losses. The data also reveal that a substantial amount of power for predicting the default of a corporation can be obtained from the firm's "distance to default," a volatility-adjusted measure of leverage that is the basis of the theoretical models of corporate debt pricing of Black, Scholes, and Merton. The findings are particularly relevant in the aftermath of the financial crisis, which revealed a lack of attention to the proper modelling of correlation of default risk across firms.

Over-the-counter (OTC) markets for derivatives, collateralized debt obligations, and repurchase agreements played a significant role in the global financial crisis. Rather than being traded through a centralized institution such as a stock exchange, OTC trades are negotiated privately between market participants who may be unaware of prices that are currently available elsewhere in the market. In these relatively opaque markets, investors can be in the dark about the most attractive available terms and who might be offering them. This opaqueness exacerbated the financial crisis, as regulators and market participants were unable to quickly assess the risks and pricing of these instruments.

"Dark Markets" offers a concise introduction to OTC markets by explaining key conceptual issues and modeling techniques, and by providing readers with a foundation for more advanced subjects in this field. Darrell Duffie covers the basic methods for modeling search and random matching in economies with many agents. He gives an overview of asset pricing in OTC markets with symmetric and asymmetric information, showing how information percolates through these markets as investors encounter each other over time. This book also features appendixes containing methodologies supporting the more theory-oriented of the chapters, making this the most self-contained introduction to OTC markets available.

This is a written version of the Cattedra Galileiana lectures, presented in 2002 at the Scuola Normale in Pisa. The objective is to combine an orientation to credit-risk modeling (emphasizing the valuation of corporate debt and credit derivatives) with an introduction to the analytical tractability and richness of affine state processes. This is not a general survey of either topic, but rather is designed to introduce researchers with some background in mathematics to a useful set of modeling techniques and an interesting set of applications.