We compare non-nested parametric specifications of the Stochastic Discount Factor (SDF) in terms of their conditional Hansen-Jagannathan (HJ-) distance. This distance is defined as the discrepancy between a parametric SDF family identifying an asset pricing model and the set of admissible SDF's satisfying the conditional no-arbitrage restrictions for a set of traded assets. The conditional HJ-distance accounts for the models' ability to match the dynamic pricing restrictions for any set of managed portfolios, and not just a set of static restrictions for a specific choice of instruments like the often employed (unconditional) HJ-distance. We estimate the conditional HJ-distance by a kernel-based Generalized Method of Moments estimator and establish its large sample properties for model selection purposes. We demonstrate empirically the usefulness of our approach by comparing several SDF models including preference-based specifications, beta-pricing models and recently proposed SDF models that are conditionally linear in the priced risk factors.
Although it is of interest to test whether or not a particular asset pricing model is literally true, a more useful task for empirical researchers is to determine how wrong a model is and to compare the performance of competing asset pricing models. In this paper, we propose a new methodology to test whether or not two competing linear asset pricing models have the same Hansen-Jagannathan distance. We show that the asymptotic distribution of the test statistic depends on whether the competing models are correctly specified or misspecified, and on whether the competing models are nested or non-nested. In addition, given the increasing interest in misspecified models, we propose a simple methodology for computing the standard errors of the estimated stochastic discount factor parameters that are robust to model misspecification. Using monthly data on 25 size and book-to-market ranked portfolios and the one-month T-bill, we show that the commonly used returns and factors are, for the most part, too noisy for us to conclude that one model is superior to the other models in terms of Hansen-Jagannathan distance. Specifically, there is little evidence that conditional and intertemporal CAPM-type specifications outperform the simple unconditional CAPM. In addition, we show that many of the macroeconomic factors commonly used in the literature are no longer priced once potential model misspecification is taken into account.
This thesis consists of three essays on empirical asset pricing around three themes: evaluating linear factor asset pricing models by comparing their misspecified measures, understanding the long-run risk on consumption-leisure to investigate their pricing performances on cross-sectional returns, and evaluating conditional asset pricing models by using the methodology of dynamic cross-sectional regressions. The first chapter is ̀̀Comparing Asset Pricing Models: What does the Hansen-Jagannathan Distance Tell Us?''. It compares the relative performance of some important linear asset pricing models based on the Hansen-Jagannathan (HJ) distance using data over a long sample period from 1952-2011 based on U.S. market. The main results are as follows: first, among return-based linear models, the Fama-French (1993) five-factor model performs best in terms of the normalized pricing errors, compared with the other candidates. On the other hand, the macro-factor model of Chen, Roll, and Ross (1986) five-factor is not able to explain industry portfolios: its performance is even worse than that of the classical CAPM. Second, the Yogo (2006) non-durable and durable consumption model is the least misspecified, among consumption-based asset pricing models, in capturing the spread in industry and size portfolios. Third, the Lettau and Ludvigson (2002) scaled consumption-based CAPM (C-CAPM) model obtains the smallest normalized pricing errors pricing gross and excess returns on size portfolios, respectively, while Santos and Veronesi (2006) scaled C-CAPM model does better in explain the return spread on portfolios of U.S. government bonds. The second chapter (̀̀Leisure, Consumption and Long Run Risk: An Empirical Evaluation'') uses a long-run risk model with non-separable leisure and consumption, and studies its ability to price equity returns on a variety of portfolios of U.S. stocks using data from 1948-2011. It builds on early work by Eichenbaum et al. (1988) that explores the empirical properties of intertemporal asset pricing models where the representative agent has utility over consumption and leisure. Here we use the framework in Uhlig (2007) that allows for a stochastic discount factor with news about long-run growth in consumption and leisure. To evaluate our long-run model, we assess its performance relative to standard asset pricing models in explaining the cross-section of returns across size, industry and value-growth portfolios. We find that the long-run consumption-leisure model cannot be rejected by the J-statistic and it does better than the standard C-CAPM, the Yogo durable consumption and Fama-French three-factor models. We also rank the normalized pricing errors using the HJ distance: our model has a smaller HJ distance than other candidate models. Our paper is the first, as far as we are aware, to use leisure data with adjusted working hours as a measure of leisure i.e., defined as the difference between a fixed time endowment and the observable hours spent on working, home production, schooling, communication, and personal care (Yang (2010)). The third essay: ̀̀Empirical Evaluation of Conditional Asset Pricing Models: An Economic Perspective'' uses dynamic Fama-MacBeth cross-sectional regressions and tests the performance of several important conditional asset pricing models when allowing for time-varying price of risk. It compares the performance of conditional asset pricing models, in terms of their ability to explain the cross-section of returns across momentum, industry, value-growth and government bond portfolios. We use the new methodology introduced by Adrian et al. (2012). Our main results are as follows: first we find that the Lettau and Ludvigson (2001) conditional model does better than other models in explaining the cross-section of momentum and value-growth portfolios. Second we find that the Piazessi et al. (2007) consumption model does better than others in pricing the cross-section of industry portfolios. Finally, we find that in the case of the cross-section of risk premia on U.S. government bond portfolios the conditional model in Santos and Veronesi (2006) outperforms other candidate models. Overall, however, the Lettau and Ludvigson (2001) model does better than other candidate models. Our main contributions here is using a recently developed method of dynamic Fama-MacBeth regressions to evaluate the performance of leading conditional CAPM (C-CAPM) models in a common set of test assets over the time period from 1951-2012.
We develop a systematic approach for evaluating asset pricing models based on the second Hansen-Jagannathan distance (HJD), which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our approach includes a specification test and a sequence of model selection procedures for non-nested, overlapping, and nested models. While the former can test whether a given model is correctly specified, the latter can compare relative performances of potentially misspecified models. Our methods are more powerful than existing ones in (i) detecting misspecified models that have small pricing errors but are not arbitrage free; and (ii) differentiating models that have similar pricing errors of a given set of test assets. Simulation studies show that our tests have reasonably good finite sample performances for typical sample sizes considered in the literature. Using the Fama-French size and book-to-market portfolios or hedge fund portfolios that exhibit option-like returns, we reach dramatically different conclusions on model performances based on our approach and existing methods.
All asset pricing models are necessarily error-ridden. While most of them have f ound supporting evidence, all have inevitably been proven inadequate at some emp irical front. It is from this perspective that all asset pricing models must be considered. The present project attempts an exhaustive comparison of a number of linear asse t pricing models. These will be compared based on their ability to price the ass ets available in the US financial market. In particular, the Hansen-Jagannathan (1997) distance measure test will be the criterion by which models will be compa red and contrasted. It will be used repeatedly to draw conclusions as far as the performance of these models across variations involving the frequency of the da ta, and the conditional information. These sensitivity tests will allow for a ra ther comprehensive evaluation of some of the most popular models, also known as the variants of CAPM. The project is organized as follows. Chapter 1 introduces the topic. The next ch apter provides a discussion of the theoretical aspects of the paper including th e stochastic discount factor concept and the derivation of HJ-distance. Chapter 3 describes the asset pricing models to be evaluated and the parameterization of the different models. Chapter 4 discusses the data and documents the empirical results. The last chapter provides the interpretation of the results as well as concluding remarks.
The University of Chicago has been and continues to be one of the most important global centres for economics. With six chapters on themes in Chicago economics and 33 chapters on the lives and work of Chicago economists, this volume shows how economics became established at the University, how it produced some of the world’s best-known economists, including Frank Knight, Milton Friedman and Robert Lucas, and how it remains a global force for the very best in teaching and research in economics. With original contributions from a stellar cast, this volume provides economists – especially those interested in macroeconomics and the history of economic thought – with an in-depth analysis of Chicago economics.
Winner of the prestigious Paul A. Samuelson Award for scholarly writing on lifelong financial security, John Cochrane's Asset Pricing now appears in a revised edition that unifies and brings the science of asset pricing up to date for advanced students and professionals. Cochrane traces the pricing of all assets back to a single idea--price equals expected discounted payoff--that captures the macro-economic risks underlying each security's value. By using a single, stochastic discount factor rather than a separate set of tricks for each asset class, Cochrane builds a unified account of modern asset pricing. He presents applications to stocks, bonds, and options. Each model--consumption based, CAPM, multifactor, term structure, and option pricing--is derived as a different specification of the discounted factor. The discount factor framework also leads to a state-space geometry for mean-variance frontiers and asset pricing models. It puts payoffs in different states of nature on the axes rather than mean and variance of return, leading to a new and conveniently linear geometrical representation of asset pricing ideas. Cochrane approaches empirical work with the Generalized Method of Moments, which studies sample average prices and discounted payoffs to determine whether price does equal expected discounted payoff. He translates between the discount factor, GMM, and state-space language and the beta, mean-variance, and regression language common in empirical work and earlier theory. The book also includes a review of recent empirical work on return predictability, value and other puzzles in the cross section, and equity premium puzzles and their resolution. Written to be a summary for academics and professionals as well as a textbook, this book condenses and advances recent scholarship in financial economics.
This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated.
This two-volume set of 23 articles authoritatively describes recent scholarship in corporate finance and asset pricing. Volume 1 concentrates on corporate finance, encompassing topics such as financial innovation and securitization, dynamic security design, and family firms. Volume 2 focuses on asset pricing with articles on market liquidity, credit derivatives, and asset pricing theory, among others. Both volumes present scholarship about the 2008 financial crisis in contexts that highlight both continuity and divergence in research. For those who seek insightful perspectives and important details, they demonstrate how corporate finance studies have interpreted recent events and incorporated their lessons. Covers core and newly-developing fields Explains how the 2008 financial crises affected theoretical and empirical research Exposes readers to a wide range of subjects described and analyzed by the best scholars
