A Variance Decomposition For Stock Returns
DOWNLOAD
Download A Variance Decomposition For Stock Returns PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get A Variance Decomposition For Stock Returns book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
A Variance Decomposition For Stock Returns
DOWNLOAD
Author : John Y. Campbell
language : en
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 Autoregression (Statistics) categories.
This paper shows that unexpected stock returns must be associated with changes in expected future dividends or expected future returns A vector autoregressive method is used to break unexpected stock returns into these two components. In U.S. monthly data in 1927-88, one-third of the variance of unexpected returns is attributed to the variance of changing expected dividends, one-third to the variance of changing expected returns, and one-third to the covariance of the two components. Changing expected returns have a large effect on stock prices because they are persistent: a 1% innovation in the expected return is associated with a 4 or 5% capital loss. Changes in expected returns are negatively correlated with changes in expected dividends, increasing the stock market reaction to dividend news. In the period 1952-88, hanging expected. returns account for a larger fraction of stock return variation than they do in the period 1927-51.
A Bayesian Analysis Of A Variance Decomposition For Stock Returns
DOWNLOAD
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
DOWNLOAD
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.
Why Does Stock Market Volatility Change Over Time A Time Varying Variance Decomposition For Stock Returns
DOWNLOAD
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
DOWNLOAD
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.
Expected And Unexpected Long Term Asset Excess Returns A Variance Decomposition
DOWNLOAD
Author : Dimitrios Roumeliotis
language : en
Publisher:
Release Date : 1997
Expected And Unexpected Long Term Asset Excess Returns A Variance Decomposition written by Dimitrios Roumeliotis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Return Decomposition Over The Business Cycle
DOWNLOAD
Author : Tolga Cenesizoglu
language : en
Publisher:
Release Date : 2014
Return Decomposition Over The Business Cycle written by Tolga Cenesizoglu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
To analyze the determinants of the observed variation in stock prices, Campbell and Shiller (1988) have suggested decomposing unexpected stock returns into unexpected changes in investors' beliefs about future cash flows (cash flow news) and discount rates (discount rate news). Based on a generalization of this approach to a framework with regime-switching parameters and variances, we analyze the decomposition of the conditional variance of returns on the S&P 500 index over the business cycle. The cash flow news is relatively more important than discount rate news in determining the conditional variance of returns in expansions. The conditional variances of returns and its components increase in recessions. However, the conditional variance of discount rate news increases more than that of cash flow news and, thus, the discount rate news becomes relatively more important than cash flow news in determining the conditional variance of returns in recessions. In contrast to the standard Campbell and Shiller approach with constant parameters and variances, cash flow news becomes more important than discount rate news in determining the unconditional variance of returns when we allow parameters and variances to vary over the business cycle. We show that these results are broadly consistent with the implications of a stylized asset pricing model in which the growth rates of dividends and consumption take on different values depending on the underlying state of the economy.
Dispersion And Volatility In Stock Returns
DOWNLOAD
Author : John Y. Campbell
language : en
Publisher:
Release Date : 1998
Dispersion And Volatility In 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 1998 with Rate of return categories.
This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional volatility or 'dispersion' of daily returns on industry portfolios, relative to the market, within the month; and the dispersion of daily returns on individual firms, relative to their industries, within the month. Over the period 1962-97 there has been a noticeable increase in firm-level volatility relative to market volatility. All the volatility measures move together in a countercyclical fashion. While market volatility tends to lead the other volatility series, industry-level volatility is a particularly important leading indicator for the business cycle.
Do Accruals Drive Stock Returns A Variance Decomposition Analysis
DOWNLOAD
Author : Jeffrey L. Callen
language : en
Publisher:
Release Date : 2013
Do Accruals Drive Stock Returns A Variance Decomposition Analysis written by Jeffrey L. Callen 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.
This paper extends the variance decomposition framework of Campbell (1991), Campbell and Ammer (1993) and Vuolteenhao (2002) to address the relative value relevance of accruals news, cash flow news and expected return news in driving firm-level equity returns. The extension is based on the Feltham-Ohlson (1995, 1996) clean surplus relations. Accruals news is found to significantly dominate expected-return news in driving firm-level stock returns. Operating income news is also found to significantly dominate both expected-return news and free cash flow news in driving firm-level stock returns. Furthermore, after splitting net income into cash flow and accrual earnings components in the Vuolteenhao (2000) model, accrual earnings news is found to significantly dominate both expected-return news and cash flow earnings news in driving firm-level stock returns. Overall, these three results indicate that changes in expected future accruals are the primary driver of current stock returns rather than changes in expected future cash flows or future discount rates.
Macro Variables And The Components Of Stock Returns
DOWNLOAD
Author : Paulo F. Maio
language : en
Publisher:
Release Date : 2015
Macro Variables And The Components Of Stock Returns written by Paulo F. Maio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
We conduct a decomposition for the stock market return by incorporating the information from 124 macro variables. Using factor analysis, we estimate six common factors and run a VAR containing these factors and financial variables such as the market dividend yield and the T-bill rate. Including the macro factors does not have a significant impact in the estimation of the components of aggregate (excess) stock returns -- cash-flow, discount-rate, and interest-rate news. Using the macro factors in the computation of cash-flow and discount-rate news does not significantly improve the fit of a two-factor ICAPM for the cross-section of stock returns.