Time-Varying Conditional Covariances in Tests of Asset Pricing Models PDF Download

Author: Campbell R. Harvey
Publisher:
ISBN:
Category :
Languages : en
Pages : 36

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Book Description
This paper proposes tests of asset pricing models that allow for time variation in conditional covariances. The evidence indicates that the conditional covariances do change through time. Estimates of the expected excess return on the market divided by the variance of the market (reward-to-risk ratio) are presented for the Sharpe-Lintner CAPM, as well as a number of tests of the model specification. The patterns of the pricing errors through time suggest the model's inability to capture the dynamic behavior of asset returns. This is the working paper version of my 1989 Journal of Financial Economics article.

Author: Charles M. Engel
Publisher:
ISBN:
Category :
Languages : en
Pages : 55

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Book Description
We perform maximum likelihood estimation of a model of international asset pricing based on CAPM. We test the restrictions imposed by CAPM against a more general asset pricing model. The quot;betasquot; in our CAPM vary over time from two sources -- the supplies of the assets (government obligations of France, Germany, Italy, Japan, the U.K. and the U.S.) change over time, and so do the conditional covariances of returns on these assets. We let the covariances change over time as a function of macroeconomic data. We also estimate the model when the covariances follow a multivariate ARCH process. When the covariance of forecast errors are time-varying, we can identify a modified CAFM model with measurement error -- which we also estimate. We find that the model in which the CAPM restrictions are imposed (which involve cross-equation constraints between coefficients and the variances of the residuals) perform much better when variances are not constant over time. Nonetheless, the CAPM model is rejected in favor of the less restricted model of asset pricing.

Author: Charles Engel
Publisher:
ISBN:
Category : Capital assets pricing model
Languages : en
Pages : 43

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Book Description
We perform maximum likelihood estimation of a model of international asset pricing based on CAPM. We test the restrictions imposed by CAPM against a more general asset pricing model. The "betas" in our CAPM vary over time from two sources -- the supplies of the assets (government obligations of France, Germany, Italy, Japan, the U.K. and the U.S.) change over time, and so do the conditional covariances of returns on these assets. We let the covariances change over time as a function of macroeconomic data. We also estimate the model when the covariances follow a multivariate ARCH process. When the covariance of forecast errors are time-varying, we can identify a modified CAFM model with measurement error -- which we also estimate. We find that the model in which the CAPM restrictions are imposed (which involve cross-equation constraints between coefficients and the variances of the residuals) perform much better when variances are not constant over time. Nonetheless, the CAPM model is rejected in favor of the less restricted model of asset pricing

Author: Charles Engel
Publisher:
ISBN:
Category : Capital assets pricing model
Languages : de
Pages :

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Book Description
We perform maximum likelihood estimation of a model of international asset pricing based on CAPM. We test the restrictions imposed by CAPM against a more general asset pricing model. The "betas" in our CAPM vary over time from two sources -- the supplies of the assets (government obligations of France, Germany, Italy, Japan, the U.K. and the U.S.) change over time, and so do the conditional covariances of returns on these assets. We let the covariances change over time as a function of macroeconomic data. We also estimate the model when the covariances follow a multivariate ARCH process. When the covariance of forecast errors are time-varying, we can identify a modified CAFM model with measurement error -- which we also estimate. We find that the model in which the CAPM restrictions are imposed (which involve cross-equation constraints between coefficients and the variances of the residuals) perform much better when variances are not constant over time. Nonetheless, the CAPM model is rejected in favor of the less restricted model of asset pricing.

Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9781886529267
Category : Mathematics
Languages : en
Pages : 543

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Book Description
"The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning. The first volume is oriented towards modeling, conceptualization, and finite-horizon problems, but also includes a substantive introduction to infinite horizon problems that is suitable for classroom use. The second volume is oriented towards mathematical analysis and computation, treats infinite horizon problems extensively, and provides an up-to-date account of approximate large-scale dynamic programming and reinforcement learning. The text contains many illustrations, worked-out examples, and exercises."--Publisher's website.

Author: Manuel Ammann
Publisher:
ISBN:
Category :
Languages : en
Pages : 41

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Book Description
We propose a new approach for the estimation of conditional asset pricing models based on a Markov Chain Monte Carlo (MCMC) approach. In contrast to existing approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables in a one-step estimation procedure. Using Samp;P 500 panel data, we analyze the empirical performance of the CAPM and the Fama and French (1993) three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance by a similar amount. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model.

Author: Daniele Bianchi
Publisher:
ISBN:
Category :
Languages : en
Pages : 42

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Book Description
I use Bayesian tools to develop a dynamic testing methodology for conditional factor pricing models, in which time-varying betas, idiosyncratic risks, and factors risk premia are jointly estimated in a single step. Based on this framework, I test over fifty years of post-war monthly data some of the most common factor pricing models on size, book-to-market, and momentum deciles portfolios, both in the time series and in the cross section. The empirical results show that, a conditional specification of the recent five-factor model of Fama and French (2015) outperforms a set of theory-based competing linear pricing models along several dimensions.

Author: Devraj Basu
Publisher:
ISBN:
Category :
Languages : en
Pages : 43

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Book Description
In this paper, we develop a new measure of specification error, and thus derive new statistical tests, for conditional factor models, i.e. models in which the factor loadings (and hence risk premia) are allowed to be time-varying. Our test exploits the close links between the stochastic discount factor framework and mean-variance efficiency. We show that a given set of factors is a true conditional asset pricing model if and only if the efficient frontiers spanned by the traded assets and the factor-mimicking portfolios, respectively, intersect. In fact, we show that our test is proportional to the difference in squared Sharpe ratios of these two frontiers.We draw three main conclusions from our empirical findings. First, optimal scaling clearly improves the performance of asset pricing models, to the point where several of the scaled models are capable of explaining asset pricing anomalies. However, even the optimally scaled models fall short of being true conditional asset pricing models in that they fail to price actively managed portfolios correctly. Second, there is significant time-variation in factor loadings and hence risk premia, which plays a significant role in asset pricing. Moreover, the optimal factor loadings display a high degree of non-linearity in the conditioning variables, suggesting that the linear scaling prevalent in the literature is sub-optimal and does not capture the inter-temporal pattern of risk premia. Third, skewness and kurtosis do matter in the conditional setting, while adding little to unconditional performance.

Author: Esben Hedegaard
Publisher:
ISBN:
Category : Analysis of covariance
Languages : en
Pages : 57

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Book Description
We examine the prediction of Merton's intertemporal CAPM that time varying risk premiums arise from the conditional covariances of returns on assets with the return on the market and other state variables. We find a positive and significant price of risk for the covariance with the market return that is driven by the time series variation in the conditional covariances, and the risk-premium on the market remains positive and significant after controlling for additional state variables. Our method estimates the risk-return tradeoff in the ICAPM using multiple portfolios as test assets.

Author: Wayne Ferson
Publisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497

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Book Description
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.