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Wednesday, August 5, 2020 | History

2 edition of Dispersion and volatility in stock returns found in the catalog.

Dispersion and volatility in stock returns

John Y. Campbell

Dispersion and volatility in stock returns

an empirical investigation

by John Y. Campbell

  • 72 Want to read
  • 31 Currently reading

Published by National Bureau of Economic Research in Cambridge, MA .
Written in English

    Subjects:
  • Stock exchanges -- Econometric models.,
  • Stocks -- Prices -- Econometric models.

  • Edition Notes

    StatementJohn Y. Campbell, Martin Lettau.
    SeriesNBER working paper series -- working paper 7144, Working paper series (National Bureau of Economic Research) -- working paper no. 7144.
    ContributionsLettau, Martin, 1966-, National Bureau of Economic Research.
    The Physical Object
    Pagination29, [13] p. :
    Number of Pages29
    ID Numbers
    Open LibraryOL22398895M

    Deng, Qian, Volatility Dispersion Trading ( ). Available at SSRN: The Impact of Jumps in Volatility and Returns. Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity by: 6. Regardless of size, book-to-market ratio, momentum, and to a lesser extent, illiquidity and volatility, local firms outperform geographically dispersed firms in the United States. The authors find that this variation in average returns is particularly pronounced for smaller firms, less-liquid firms, and firms with high idiosyncratic volatility.

      Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are by: turns. We develop a model of the dispersion of opinion among investors that has implica-tions for asset pricing. We test the relationship between dispersion of investor opinion and stock returns using a two-year panel of more than 91 thousand individual accounts in a S&P index fund. We show that dispersion of opinion, proxied by the.

    between future stock return volatility and the dispersion measure. The importance of dispersion on future stock return volatility is high in January and the first few months of the year and declines thereafter. Finally, forecast disper-sions in , the crash year, seem to . Up-to-Date Research Sheds New Light on This Area. Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in developed, emerging, and frontier : Hardcover.


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Dispersion and volatility in stock returns by John Y. Campbell Download PDF EPUB FB2

“Earlier studies [1] show that return dispersion possesses incremental information regarding idiosyncratic as well as aggregate stock market volatility [2] show that return dispersion reliably predicts the time-variation in stock market returns, volatility as well as the value and momentum premia.” Empirical analysis.

Dispersion and Volatility in Stock Returns: An Empirical Investigation John Y. Campbell, Martin Lettau. NBER Working Paper No. Issued in May NBER Program(s):Asset Pricing Program 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.

Volatility represents how large an asset's prices swing around the mean price - it is a statistical measure of its dispersion of returns. There are several ways to measure volatility, including Author: Justin Kuepper.

Sustainability11, 4 of 15 equity return dispersion (RDt) for day t as the cross-sectional standard deviation of daily stock returns calculated as: RD t= v u u tw i, N å i=1 (r rm,t) 2, (1) where ri,t and rm,t are the return for stock i and the market for day t, respectively; wi,t = 1/N for the equally-weighted cross-sectional dispersion of equity returns; and N is the number of by: 2.

Dispersion: Measuring Market Opportunity December INDEX INVESTMENT STRATEGY 2 The result is sometimes called cross-sectional portfolio volatility; we prefer the more concise term dispersion.2 Computing dispersion requires us to specify both the time period over which returns are to be measured, as well as the degree of granularity at which the calculation will be made.3 For example.

The data used to construct our measure of geographic dispersion are downloaded from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) of the U.S. Securities and Exchange Commission (SEC). Stock returns, stock prices, and data on volume traded are from the Center for Research in Security Prices (CRSP).Cited by: The dispersion trading uses the known fact that the difference between implied and realized volatility is greater between index options than between individual stock options.

The investor, therefore, could sell options on index and buy individual stocks options. Dispersion trading is a sort of correlation trading as trades are usually.

Additional Physical Format: Online version: Campbell, John Y. Dispersion and volatility in stock returns. Cambridge, MA: National Bureau of Economic Research, © 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.

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.

Strictly defined, volatility is a measure of dispersion around the mean or average return of a security. Volatility can be measured using the standard deviation, which signals how tightly the Author: Hans Wagner.

This paper contributes to the literature on stock market predictability by exploring the causal relationships between equity return dispersion, stock market volatility and excess returns via.

In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market.

In particular, we investigate whether sentiment dispersion contains Cited by: 5. Consistent with the evidence on U.S. stock returns (Jiang,Demirer and Jategaonkar, ), we find that equity return dispersion cross-sectionally drives stock returns, even after controlling for market, size, book-to-market, and idiosyncratic volatility effects.

We observe that stocks with greater sensitivities to equity return Cited by: 4. Volatility refers to the standard deviation of stock returns around their time-series mean from the previous year.

Dispersion refers to the cross-sectional standard deviation of stock returns. Dispersion of opinion and stock returns.

belief dispersion leads to higher stock volatility and trading volume. for the well known determinants of stock returns like the firm-size, book-to. Cross-sectional market volatility is a measure of dispersion of individual stock returns with respect to the market return.

This measure is equivalent to the common heteroskedasticity in stock-specific returns which is a cross-sectional average of individual stock by: 4. The dispersion of returns is positively related to time-series volatility (correlation = +) and the VIX (correlation = +).

Moreover, the dispersion of alpha is. We employ bivariate and multivariate nonlinear causality tests to document causality from equity return dispersion to stock market volatility and excess returns, even after controlling for the state of the economy. Expansionary (contractionary) market states are associated with a low (high) level of equity return dispersion, indicating asymmetries in the relationship between return dispersion Cited by: 2.

Volatility Trading (Wiley Trading Book ) - Kindle edition by Sinclair, Euan. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Volatility Trading (Wiley Trading Book )/5(24).

Recent work by Diether, Malloy, and Scherbina () has established a negative relationship between stock returns and the dispersion of analysts' earnings forecasts. I offer a simple explanation for this phenomenon based on the interpretation of dispersion as a proxy for unpriced information risk arising when asset values are by: analyst forecast dispersion and return volatility, both Jiang et al.

() and Zhang () find that high information uncertainty, in the form of large analyst dispersion or return volatility, induces negative future returns.

The negative association between analyst forecast dispersion and future returns is also documented in Diether et al.returns and study the realized moments™time-series and cross-sectional properties. We investi-gate if this week™s realized moments are informative for the cross-section of next week™s stock returns.

We –nd a very strong negative relationship between realized skewness and next week™s stock by: