This study analyses the impact of macroeconomic news announcements on the conditional volatility of bond returns. Using daily returns on the 1, 3, 5 and 10 year US Treasury bonds, we find that announcement shocks have a strong impact on the dynamics of bond market volatility. Our results provide empirical evidence that the bond market incorporates the implications of macroeconomic announcement news faster than other information. Moreover, after distinguishing between types of macroeconomic announcements, releases of the employment situation and producer price index are especially influential at the intermediate and long end of the yield curve, while monetary policy seem to affect short-term bond volatility.

In this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.

Kamstra, Kramer and Levi (KKL) in their comment seem to miss the main point of our paper. Many things are correlated with the seasons so it is difficult to distinguish between them when we try to explain the well-known summer winter pattern in stock returns. Finding an isolated seasonal affective disorder (SAD) effect without proper control variables does not disprove our point but strengthens it. To sidestep all of the issues they raise and take our point to the extreme, we show using plain vanilla regressions that the seasonal stock market pattern they attribute to SAD can also be “explained” by variables like ice cream consumption or airline travel. The new variations of SAD variables (“onset” and “incidence”) KKL propose in their recent work for North America are even more problematic than the original SAD variables. We find that these new SAD proxies are not significant in countries where according to KKL they should be: Canada and the United States.

We show that results in the recent strand of the literature, which tries to explain stock returns by weather induced mood shifts of investors, might be data-driven inference. More specifically, we consider two recent studies [Kamstra, Mark J., Kramer, Lisa A., Levi, Maurice D., 2003a. Winter blues: A SAD stock market cycle. American Economic Review 93(1), 324–343; Cao, Melanie, Wei, Jason, 2005. Stock market returns: A note on temperature anomaly. Journal of Banking and Finance 29(6), 1559–1573] that claim that a seasonal anomaly in stock returns is caused by mood changes of investors due to lack of daylight and temperature variations, respectively. While we confirm earlier results in the literature that there is indeed a strong seasonal effect in stock returns in many countries: stock market returns tend to be significantly lower during summer and fall months than during winter and spring months as documented by Bouman and Jacobsen [Bouman, Sven, Jacobsen, Ben, 2002. The Halloween indicator, Sell in May and go away: Another puzzle. American Economic Review, 92(5), 1618–1635], there is little evidence in favor of a SAD or temperature explanation. In fact, we find that a simple winter/summer dummy best describes this seasonality. Our results suggest that without any further evidence the correlation between weather-related variables and stock returns might be spurious and the conclusion that weather affects stock returns through mood changes of investors is premature.