Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared, or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.

This paper reports the first effort to use data to evaluate how new information, acquired through exogenous health shocks, affects people's longevity expectations. We find that smokers react differently to health shocks than do those who quit smoking or never smoked. These differences, together with insights from qualitative research conducted along with the statistical analysis, suggest specific changes in the health warnings used to reduce smoking. Our specific focus is on how current smokers responded to health information in comparison to former smokers and nonsmokers. The three groups use significantly different updating rules to revise their assessments about longevity. The most significant finding of our study documents that smokers differ from persons who do not smoke in how information influences their personal longevity expectations. When smokers experience smoking-related health shocks, they interpret this information as reducing their chances of living to age 75 or more. Our estimated models imply smokers update their longevity expectations more dramatically than either former smokers or those who never smoked. Smokers are thus assigning a larger risk equivalent to these shocks. They do not react comparably to general health shocks, implying that specific information about smoking-related health events is most likely to cause them to update beliefs. It remains to be evaluated whether messages can be designed that focus on the link between smoking and health outcomes in ways that will have comparable effects on smokers' risk perceptions.

Meyer (1999) has suggested that episodes of heightened uncertainty about the NAIRU may warrant a nonlinear policy response to changes in the unemployment rate. This paper offers a theoretical justification for such a nonlinear policy rule, and provides some empirical evidence on the relative performance of linear and nonlinear rules when there is heightened uncertainty about the NAIRU.