2 edition of **variance decomposition for stock returns.** found in the catalog.

variance decomposition for stock returns.

John Y. Campbell

- 327 Want to read
- 32 Currently reading

Published
**1990**
by LSE Financial Markets Group in London
.

Written in English

**Edition Notes**

Series | LSE FinancialMarkets Group Discussion Paper Series -- No.78 |

ID Numbers | |
---|---|

Open Library | OL13902949M |

Factor Models. Factor Models. MIT S Dr. Kempthorne. Fall MIT S Lecture Factor Models. rent stock returns. In what follows, section 2 brieﬂy describes the Campbell-Shiller and Vuolteenaho variance decomposition models. This section also extends the variance decomposition model to a Feltham-Ohlson accruals framework. Section 3 describes the data, and section 4 describes the major empirical results. Section 5 concludes.

The variance decomposition shows that most of the variance of stock returns is due to the variance of news about future dividends and to the covariances of news about future dividends, real interest rates and excess by: Variance decomposition for stock returns. Cambridge, MA ( Massachusetts Avenue, Cambridge, MA ): National Bureau of Economic Research, [?] (OCoLC)

First, firm-level stock returns are mainly driven by cash-flow news. For a typical stock, the variance of cash-flow news is more than twice that of expected-return news. Second, shocks to expected returns and cash flows are pos- and Campbell’s ~! return-decomposition framework enable me . The forecast variance decomposition determines the proportion of the variation Yjt due to the shock Ujt versus shocks of other variables uit for i = j.. VAR in EViews. As an example of VAR estimation in EViews, consider two time series of returns of monthly IBM stocks and the market portfolio returns from Fama-French database (data is contained in 1).

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Changes in expected returns are negatively correlated with changes in expected dividends, increasing the stock market reaction to dividend news. In the periodhanging expected. returns account for a larger fraction of stock return variation than they do in the period Cited by: A Variance Decomposition for Stock Returns John Y.

Campbell. Princeton University. Search for other works by this author on: Oxford Academic. Google Scholar. John Y. Campbell" This paper was delivered to the Royal Economic Society at Nottingham on Ma as the H.

Johnson Lecture. An earlier version is available as NBER Working Paper Cited by: A VARIANCE DECOMPOSITION FOR STOCK EflURNS ABSTRACT This paper shows that unexpected stock returns must be associated with changes in variance decomposition for stock returns.

book 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. In U.S. monthly data inone-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.

Variance decomposition is a classical statistical method in multivariate analysis for uncovering simplifying structures in a large set of variables (for example, Anderson ). For example, factor analysis or principal components are tools that are in widespread use. Variance decomposition is a classical statistical method in multivariate analysis for uncovering simplifying structures in a large set of variables (for example, Anderson, ).

For example, factor. Hence, stock price volatility can come from either volatility of future dividends or volatility of expected future returns. Which of these terms contribute more to volatility empirically. Fit a VAR to dividend growth and returns and use the VAR to compute the implied decomposition: 0 @ d t+1 r t+1 x t+1 1 A= A(L) 0 @ d t r t x t 1 A+ 0 @ "d t+1 File Size: KB.

In a range of variance decomposition studies, the industry effects are estimated to explain 4%% and firm effects explain 40% or more of the variance in firm performance (Fitza, ; McGahan. This article needs additional citations for verification. Please help improve this article by adding citations to reliable ced material may be challenged and removed.

March ) (Learn how and when to remove this template message). A Bayesian Analysis of a Variance Decomposition for Stock Returns 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.

Hollifield, Burton and Li, Kai and Koop Cited by: short-run dynamics of dividends. Understanding the dynamics of expected returns is enough.” We call this the return decomposition approach in the rest of the paper.

Using the return decomposition approach, some important conclusions have been drawn: • The variance of the DR news is much larger than the variance of the CF news for the market. The return variance decomposition (Equation (5)) cannot be computed without esti- mates (of the dynamics) of expected returns and expected earnings.

We initially estimate a parsimonious VAR where the state variables consist of log stock returns, log book return on equity, and the log book-to-market ratio. 9 The VAR model can then be.

In U.S. monthly data inone-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 by: Downloadable.

Author(s): John Y. Campbell. Abstract: 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 inone-third of the variance of unexpected returns is attributed to the. of an aggregate stock index, namely the major German stock index, DAX.

We use returns data for DAX, as well as returns and weights data for its constituent stocks for a period of trading days between September and July Through a decomposition of the index variance. variation in expected stock returns, and cannot be accounted for by other high-frequency-based realized variation measures recently analyzed in the literature.

The relation between equity return and volatility has been extensively studied in the literature. Even though a number of studies have argued that variance risk is priced atFile Size: 3MB. we can produce the usual variance decomposition (in percentage) for the unexpected stock market return, (5) 1 = V a r N C F, t + 1 V a r r m, t + 1 − E t r m, t + 1 + V a r N D R, t + 1 V a r r m, t + 1 − E t r m, t + 1 − 2 C o v N C F, t + 1 N D R, t + 1 V a r r m, t + 1 − E t r m, t + 1, where the first term on the right-hand side represents the share of cash-flow news in driving the variation in the current market return, Cited by: A Variance Decomposition for Stock Returns.

John Campbell () Economic Journal,vol. issueAbstract: 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 Cited by: where DE t ¼ E t E t 1 denotes the revision from period t 1 to period t, r is a discount rate and roe t is the (log) return on book value equity at time t. 4 Note that earnings news can be decomposed into the conventional earnings surprise DE troe t plus the revision to future earnings E t P1 j¼1 jroe earnings news generalizes the notion of an earnings surprise to the entire gamut.

A Variance Decomposition for Long‐Term Asset Returns. JOHN Y. CAMPBELL. stock and bond returns are driven largely by news about future excess stock returns and inflation, respectively.

Real interest rates have little impact on returns, although they do affect the short‐term nominal interest rate and the slope of the term structure Cited by:. 3 presents the benchmark variance decomposition for the earnings yield, based on long-horizon regressions.

In Section 4, I present an alternative variance decomposition based on a rst-order VAR. Section 5 presents the results from a Monte-Carlo simulation. In Section 6, I analyze the predictability of the earnings yield for excess stock Size: 1MB.Using the Vuolteenaho’s () return variance decomposition methodology, this study assesses the value implication of corporate governance in the US stock market.Get this from a library!

A variance decomposition for stock returns. [John Y Campbell; National Bureau of Economic Research.].