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Is value premium a proxy for time-varying investment opportunities: some time series evidence

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  • Hui Guo
  • Robert Savickas
  • Zijun Wang
  • Jian Yang

Abstract

We uncover a positive, empirical risk-return tradeoff in the stock market after controlling for the covariance of stock market returns with the value premium. The underlying premise is that, as conjectured by Fama and French (1996), the value premium is a proxy for time-varying investment opportunities. By ignoring the value premium, early specifications suffer from an omitted variable problem that leads to a downward bias in the estimate of the risk-return tradeoff. The paper also documents a new finding on a significantly positive relation between the value premium and its conditional variance.

Suggested Citation

  • Hui Guo & Robert Savickas & Zijun Wang & Jian Yang, 2006. "Is value premium a proxy for time-varying investment opportunities: some time series evidence," Working Papers 2005-026, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2005-026
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    References listed on IDEAS

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    Cited by:

    1. Hui Guo & Robert Savickas, 2006. "Understanding stock return predictability," Working Papers 2006-019, Federal Reserve Bank of St. Louis.
    2. Hui Guo & Robert Savickas, 2006. "The relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns in G7 countries," Working Papers 2006-036, Federal Reserve Bank of St. Louis.
    3. Hui Guo & Robert Savickas, 2006. "Aggregate idiosyncratic volatility in G7 countries," Working Papers 2004-027, Federal Reserve Bank of St. Louis.
    4. Guo, Hui & Neely, Christopher J., 2008. "Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model," Economics Letters, Elsevier, vol. 99(2), pages 371-374, May.
    5. Hui Guo & Zijun Wang & Jian Yang, 2006. "Does aggregate relative risk aversion change countercyclically over time? evidence from the stock market," Working Papers 2006-047, Federal Reserve Bank of St. Louis.

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    Keywords

    Time-series analysis ; Stocks;

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