Total return

Abstract: Measuring the total return variation explained by shocks to expected cash flows, timevarying expected returns, and shocks to expected returns is one way to judge the rationality of stock prices. Variables that proxy for expected returns and expectedreturn shocks capture 30% of the variance of annual NYSE value-weighted returns. Growth rates of production, used to proxy for shocks to expected cash flows, explain 43% of the return variance. Whether the combined explanatory power of the variablesabout 58% of the variance of annual returns-is good or bad news about market efficiency is left for the reader to judge. STANDARD VALUATION MODELS POSIT three sources of variation in stock returns: (a) shocks to expected cash flows, (b) predictable return variation due to variation through time in the discount rates that price expected cash flows, and (c) shocks to discount rates. Many studies examine these three sources of return variation. Fama (1981), Geske and Roll (1983), Kaul (1987), Barro (1990), and Shah (1989) find that large fractions (often more than 50%) of annual stock-return variances can be traced to forecasts of variables such as real GNP, industrial production, and investment that are important determinants of the cash flows to firms. There is also evidence that expected returns (and thus the discount rates that price expected cash flows) vary through time (for example, Fama and Schwert (1977), Keim and Stambaugh (1986), Campbell and Shiller (1988), and Fama and French (1988, 1989)). Finally, French, Schwert, and Stambaugh (1987) find that part of the variation in stock returns can be traced to a "discount-rate effect," that is, shocks to expected returns and discount rates that generate opposite shocks to prices. Measuring the total return variation explained by a combination of shocks to expected cash flows, time-varying expected returns, and shocks to expected returns is a logical way to judge the efficiency or rationality of stock prices. Although the three sources of return variation have been studied separately, there is little evidence on their combined explanatory power. Such evidence is a major goal of this paper. The evidence says that variables that measure time-varying expected returns and shocks to expected returns capture about 30% of the variance of annual real returns on the value-weighted portfolio of New York Stock Exchange (NYSE) stocks. Future growth rates of industrial production, used to proxy for shocks to

Abstract: A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

Abstract: A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

Abstract: THIS PAPER DEFINES the effectiveness of common stocks as an inflation hedge as the extent to which they can be used to reduce the risk of an investor's real return which stems from uncertainty about the future level of the prices of consumption goods.' Since there is one type of security whose real return is certain but for inflation risk, namely single-period2, riskless-in-terms-of-default nominal bonds, it seems appropriate to identify inflation risk with the variance of the real return on such a bond. Accordingly, we measure the effectiveness of common stocks as an inflation hedge as the proportional reduction in that variance attainable by combining a "representative" well-diversified portfolio of common stocks and the nominal bond in their variance minimizing proportions. It is worthwhile to indicate the relationship between this view of hedging against inflation and the investor's ultimate objective of optimal portfolio selection. This can best be done in the framework of the Markowitz-Tobin-mean-variance model of portfolio choice.3 In that model the process of portfolio selection is divided into two separate stages: (1) identification of the efficient portfolio frontier and (2) choosing the optimal portfolio on that frontier. This paper focuses on one particular point on the efficient frontier-the minimum variance portfolio. From this perspective hedging against inflation is essentially the process of taking a risk-free-in-terms-of-default nominal bond as the starting point and using other securities to eliminate as much of the variance of its real return as possible. We define the difference between the mean real return on a nominal bond and the mean real return on the minimum variance portfolio as the "cost" of hedging