Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach
Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

Author: Manuel Ammann

Publisher:

Published: 2014

Total Pages: 41

ISBN-13:

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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.

Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models
Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models

Author: Xiangjin Shen

Publisher:

Published: 2011

Total Pages:

ISBN-13:

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We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non-nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1 1990 to April 18 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data. -- Deviance information criterion ; Cumulative density of the mean squared errors of forecast ; Markov chain Monte Carlo algorithms ; Block bootstrap ; Generalized methods of moments ; Conditional Kolmogorov test ; CIR and Vasicek models

Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models
Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models

Author: Xiangjin Shen

Publisher:

Published: 2013

Total Pages:

ISBN-13:

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We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1, 1990 to April 18, 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data.

Markov Chain Monte Carlo Simulations and Their Statistical Analysis
Markov Chain Monte Carlo Simulations and Their Statistical Analysis

Author: Bernd A Berg

Publisher: World Scientific Publishing Company

Published: 2004-10-01

Total Pages: 380

ISBN-13: 9813106379

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This book teaches modern Markov chain Monte Carlo (MC) simulation techniques step by step. The material should be accessible to advanced undergraduate students and is suitable for a course. It ranges from elementary statistics concepts (the theory behind MC simulations), through conventional Metropolis and heat bath algorithms, autocorrelations and the analysis of the performance of MC algorithms, to advanced topics including the multicanonical approach, cluster algorithms and parallel computing. Therefore, it is also of interest to researchers in the field. The book relates the theory directly to Web-based computer code. This allows readers to get quickly started with their own simulations and to verify many numerical examples easily. The present code is in Fortran 77, for which compilers are freely available. The principles taught are important for users of other programming languages, like C or C++.

Handbook of Markov Chain Monte Carlo
Handbook of Markov Chain Monte Carlo

Author: Steve Brooks

Publisher: CRC Press

Published: 2011-05-10

Total Pages: 620

ISBN-13: 1420079425

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Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

Monte Carlo Simulation and Finance
Monte Carlo Simulation and Finance

Author: Don L. McLeish

Publisher: John Wiley & Sons

Published: 2011-09-13

Total Pages: 308

ISBN-13: 1118160940

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Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.

Advanced Markov Chain Monte Carlo Methods
Advanced Markov Chain Monte Carlo Methods

Author: Faming Liang

Publisher: John Wiley & Sons

Published: 2011-07-05

Total Pages: 308

ISBN-13: 1119956803

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Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features: Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants. Up-to-date accounts of recent developments of the Gibbs sampler. Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals. This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.

Monte Carlo Methods and Models in Finance and Insurance
Monte Carlo Methods and Models in Finance and Insurance

Author: Ralf Korn

Publisher: CRC Press

Published: 2010-02-26

Total Pages: 485

ISBN-13: 1420076191

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Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Rom

A Dynamic Test of Conditional Asset Pricing Models
A Dynamic Test of Conditional Asset Pricing Models

Author: Daniele Bianchi

Publisher:

Published: 2019

Total Pages: 42

ISBN-13:

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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.