Division of Research and Statistics
Federal Reserve Board
20th Street and Constitution Avenue
Washington, DC 20551
E-Mail: no email available
Institutional Affiliation: Federal Reserve Board
NBER Working Papers and Publications
|November 2005||Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence|
with Francis X. Diebold: w11736
We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwisestandard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R-squared. Expected...
- Frank Diebold & Sean Campbell, 2005. "Stock returns and expected business conditions: half a century of direct evidence," Proceedings, Board of Governors of the Federal Reserve System (U.S.). citation courtesy of
- Campbell, Sean D. & Diebold, Francis X., 2009. "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 27(2), pages 266-278. citation courtesy of
|December 2003||Weather Forecasting for Weather Derivatives|
with Francis X. Diebold: w10141
We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. The approach can easily be used to produce not only short-horizon point forecasts, but also the long-horizon density forecasts of maximal relevance in weather derivatives contexts. We produce and evaluate both, with some success. We conclude that additional inq...
Published: Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March. citation courtesy of