A Bayesian Analysis Of A Variance Decomposition For Stock Returns
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A Bayesian Analysis Of A Variance Decomposition For Stock Returns
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Author : Burton Hollifield
language : en
Publisher:
Release Date : 2009
A Bayesian Analysis Of A Variance Decomposition For Stock Returns written by Burton Hollifield and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
We apply Bayesian methods to study a common VAR-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including quot;non-informativequot; limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the quot;non-informativequot; prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.
Bayesian Analysis Of A Variance Decomposition For Stock Returns
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Author :
language : en
Publisher:
Release Date :
Bayesian Analysis Of A Variance Decomposition For Stock Returns written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
The Finance Division of the Faculty of Commerce and Business Administration at the University of British Columbia in Vancouver, British Columbia, Canada, presents the full text of a working paper entitled " A Bayesian Analysis of a Variance Decomposition for Stock Returns," by Burton Hollifield, Gary Koop, and Kai Li. The paper discusses using Bayesian methods to study the variance of excess stock returns.
A Variance Decomposition For Stock Returns
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Author : John Y. Campbell
language : es
Publisher:
Release Date : 1990
A Variance Decomposition For Stock Returns written by John Y. Campbell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with categories.
Essays On The Predictability And Volatility Of Returns In The Stock Market
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Author : Ruojun Wu
language : en
Publisher:
Release Date : 2008
Essays On The Predictability And Volatility Of Returns In The Stock Market written by Ruojun Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Bayesian statistical decision theory categories.
This dissertation studies the effect of parameter uncertainty on the return predictability and volatility of the stock market. The first two chapters focus on the decomposition of market volatility, and the third chapter studies the return predictability. When facing imperfect information, the investors tend to form a learning scheme that encompasses both historical data and prior beliefs. In the variance decomposition framework, the introducing of learning directly impacts the way that return forecasts are revised and consequently the relative component of market volatility based on these forecasts, namely the price movements from revision on future discount rates and those from future cash flows. According to the empirical study in Chapter 1, the former is not necessarily the major driving force of market volatility, which provides an alternative view on what moves stock prices. Learning is modeled and estimated by Bayesian method. Chapter 2 follows the topic in Chapter 1 and studies the role of persistent state variables in return decomposition in order to provide more robust inference on variance decomposition. In Chapter 3 we propose to utilize theoretical constraints to help predict market returns when in sample data is very noisy and creates model uncertainty for the investors. The constraints are also incorporated by Bayesian method. We show in the out-of-sample forecast experiment that models with theoretical constraints produce better forecasts.
Why Does Stock Market Volatility Change Over Time A Time Varying Variance Decomposition For Stock Returns
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Author : John T. Scruggs
language : en
Publisher:
Release Date : 2006
Why Does Stock Market Volatility Change Over Time A Time Varying Variance Decomposition For Stock Returns written by John T. Scruggs and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
We extend the variance decomposition model of Campbell (1991) to allow for time-varying stock market volatility. Specifically, we introduce a model in which the covariance matrix of the vector autoregression (VAR) follows a multivariate stochastic volatility (MSV) process. This VAR-MSV model permits the decomposition of unexpected real stock return variance into three time-varying components: variance of news about future dividends, variance of news about future returns, and a covariance term. We develop Bayesian Markov chain Monte Carlo (MCMC) econometric techniques for estimating the VAR-MSV model. These methods are well-suited for estimating models with latent stochastic volatilities, and are not subject to the small-sample biases and unit root problems that plague frequentist estimation of predictive regressions. We report strong evidence that real stock returns are predictable when the dividend-price ratio and a stochastically detrended short-term interest rate are employed as forecasting variables. The time-varying variance of news about future returns is the primary determinant of stock market volatility (both levels and changes). The variance of news about future dividends increased dramatically during the 1973-1974 recession and peaked during the 1980 recession before descending in the 1980s. However, its contribution to stock market volatility was offset by positive correlation between news about future dividends and news about future returns from 1974-1984.
What Moves The Stock And Bond Markets A Variance Decomposition For Long Term Asset Returns
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Author : John Y. Campbell
language : es
Publisher:
Release Date : 1991
What Moves The Stock And Bond Markets A Variance Decomposition For Long Term Asset Returns written by John Y. Campbell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Tactical Industry Allocation And Model Uncertainty
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Author : Manuel Ammann
language : en
Publisher:
Release Date : 2013
Tactical Industry Allocation And Model Uncertainty written by Manuel Ammann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
We use Bayesian model averaging to analyze the sample evidence on industry return predictability within the U.S. stock market in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out-of-sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out-of-sample forecasting power of a naive iid forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.
Predicting The Distribution Of Stock Returns
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Author : Daniele Massacci
language : en
Publisher:
Release Date : 2017
Predicting The Distribution Of Stock Returns written by Daniele Massacci and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
A large literature has investigated predictability of the conditional mean of low frequency stock returns by macroeconomic and financial variables; however, little is known about predictability of the conditional distribution. We look at one-step-ahead out-of-sample predictability of the conditional distribution of monthly U.S. stock returns in relation to the macroeconomic and financial environment. Our methodological approach is innovative: we consider several specifications for the conditional density and combinations schemes. Our results are as follows: the entire density is predicted under combination schemes as applied to univariate GARCH models with Gaussian innovations; the Bayesian winner in relation to GARCH - skewed - t models is informative about the 5% VaR; the average realised utility of a mean-variance investor is maximised under the Bayesian winner as applied to GARCH models with symmetric student-t innovations. Our results have two implications: the best prediction model depends on the evaluation criterion; and combination schemes outperform individual models.
Return Decomposition
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Author : Long Chen
language : en
Publisher:
Release Date : 2008
Return Decomposition written by Long Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
A crucial issue in asset pricing is to understand the relative importance of discount rate (DR) news and cash flow (CF) news in driving the time-series and cross-sectional variations of stock returns. Many studies directly estimate the DR news but back out the CF news as the residual. We argue that this approach has a serious limitation because the DR news cannot be accurately measured due to the small predictive power, and the CF news, as the residual, inherits the large misspecification error of the DR news. We apply this residual-based decomposition approach to Treasury bonds and equities, and find results that are either counter-intuitive or unrobust. Potential solutions, including modeling both DR news and CF news directly, the Bayesian model averaging approach, and the principal component analysis, are explored.
Ibss Economics 1993 Vol 42
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Author :
language : en
Publisher: Psychology Press
Release Date : 1994
Ibss Economics 1993 Vol 42 written by and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Business & Economics categories.
This bibliography lists the most important works published in economics in 1993. Renowned for its international coverage and rigorous selection procedures, the IBSS provides researchers and librarians with the most comprehensive and scholarly bibliographic service available in the social sciences. The IBSS is compiled by the British Library of Political and Economic Science at the London School of Economics, one of the world's leading social science institutions. Published annually, the IBSS is available in four subject areas: anthropology, economics, political science and sociology.