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.

This study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset-pricing model, termed as the dynamic factor pricing model (DFPM). We then conduct asset-pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and conditional versions of the Fama and French (1993) three-factor model. In particular, the DFPM can explain and better forecast the momentum portfolio returns, which are mostly missed by other alternative models.

While mainstream financial theories and applications assume that asset returns are normally distributed and individual preferences are quadratic, the overwhelming empirical evidence shows otherwise. Indeed, most of the asset returns exhibit “fat-tails” distributions and investors exhibit asymmetric preferences. These empirical findings lead to the development of a new area of research dedicated to the introduction of higher order moments in portfolio theory and asset pricing models. Multi-moment asset pricing is a revolutionary new way of modeling time series in finance which allows various degrees of long-term memory to be generated. It allows risk and prices of risk to vary through time enabling the accurate valuation of long-lived assets. This book presents the state-of-the art in multi-moment asset allocation and pricing models and provides many new developments in a single volume, collecting in a unified framework theoretical results and applications previously scattered throughout the financial literature. The topics covered in this comprehensive volume include: four-moment individual risk preferences, mathematics of the multi-moment efficient frontier, coherent asymmetric risks measures, hedge funds asset allocation under higher moments, time-varying specifications of (co)moments and multi-moment asset pricing models with homogeneous and heterogeneous agents. Written by leading academics, Multi-moment Asset Allocation and Pricing Models offers a unique opportunity to explore the latest findings in this new field of research.

Asset pricing models can reinforce asset allocation decisions and promote risk management gains. We compare the out-of-sample performance of mean-variance strategies when mean and covariance are sample estimators of (1) unfiltered excess returns; and (2) filtered excess returns through an asset pricing model. We report that filtered returns contribute to improve the diversification effect by reducing the estimation error of the sample estimators. Traditional alternatives aimed to address the estimation error such as restricting weight variability are successful at reducing the perverse effect of extreme allocations but cannot enhance the diversification potential since they tend to mimic (not to outperform) the suboptimal constant rule performance.

Covers applications to risky assets traded on the markets for funds, fixed-income products and electricity derivatives. Integrates the latest research and includes a new chapter on financial modeling.

This introduction to general equilibrium modelling takes an integrated approach to the analysis of macroeconomics and finance. It provides students, practitioners, and policymakers with an easily accessible set of tools that can be used to analyze a wide range of economic phenomena. Key features: • Provides a consistent framework for understanding dynamic economic models • Introduces key concepts in finance in a discrete time setting • Develops simple recursive approach for analyzing a variety of problems in a dynamic, stochastic environment • Sequentially builds up the analysis of consumption, production, and investment models to study their implications for allocations and asset prices • Reviews business cycle analysis and the business cycle implications of monetary and international models • Covers latest research on asset pricing in overlapping generations models and on models with borrowing constraints and transaction costs • Includes end-of-chapter exercises allowing readers to monitor their understanding of each topic Online resources are available at www.cambridge.org/altug_labadie