An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

"TAIL RISKS" originate from the failure of mean reversion and the idealized bell curve of asset returns, which assumes that highly probable outcomes occur near the center of the curve and that unlikely occurrences, good and bad, happen rarely, if at all, at either "tail" of the curve. Ever since the global financial crisis, protecting investments against these severe tail events has become a priority for investors and money managers, but it is something Vineer Bhansali and his team at PIMCO have been doing for over a decade. In one of the first comprehensive and rigorous books ever written on tail risk hedging, he lays out a systematic approach to protecting portfolios from, and potentially benefiting from, rare yet severe market outcomes. Tail Risk Hedging is built on the author's practical experience applying macroeconomic forecasting and quantitative modeling techniques across asset markets. Using empirical data and charts, he explains the consequences of diversification failure in tail events and how to manage portfolios when this happens. He provides an easy-to-use, yet rigorous framework for protecting investment portfolios against tail risk and using tail hedging to play offense. Tail Risk Hedging explores how to: Generate profits from volatility and illiquidity during tail-risk events in equity and credit markets Buy attractively priced tail hedges that add value to a portfolio and quantify basis risk Interpret the psychology of investors in option pricing and portfolio construction Customize explicit hedges for retirement investments Hedge risk factors such as duration risk and inflation risk Managing tail risk is today's most significant development in risk management, and this thorough guide helps you access every aspect of it. With the time-tested and mathematically rigorous strategies described here, including pieces of computer code, you get access to insights to help mitigate portfolio losses in significant downturns, create explosive liquidity while unhedged participants are forced to sell, and create more aggressive yet tail-risk-focused portfolios. The book also gives you a unique, higher level view of how tail risk is related to investing in alternatives, and of derivatives such as zerocost collars and variance swaps. Volatility and tail risks are here to stay, and so should your clients' wealth when you use Tail Risk Hedging for managing portfolios. PRAISE FOR TAIL RISK HEDGING: "Managing, mitigating, and even exploiting the risk of bad times are the most important concerns in investments. Bhansali puts tail risk hedging and tail risk management under a microscope--pricing, implementation, and showing how we can fine-tune our risk exposures, which are all crucial ways in how we can better weather our bad times." -- ANDREW ANG, Ann F. Kaplan Professor of Business at Columbia University "This book is critical and accessible reading for fiduciaries, financial consultants and investors interested in both theoretical foundations and practical considerations for how to frame hedging downside risk in portfolios. It is a tremendous resource for anyone involved in asset allocation today." -- CHRISTOPHER C. GECZY, Ph.D., Academic Director, Wharton Wealth Management Initiative and Adj. Associate Professor of Finance, The Wharton School "Bhansali's book demonstrates how tail risk hedging can work, be concretely implemented, and lead to higher returns so that it is possible to have your cake and eat it too! A must read for the savvy investor." -- DIDIER SORNETTE, Professor on the Chair of Entrepreneurial Risks, ETH Zurich

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 book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.