Paper # Author Title
Understanding the determinants of individuals' perceptions of their risk of becoming infected with HIV and their perceptions of acceptable strategies of prevention is an essential step towards curtailing the spread of this disease. We focus in this paper on learning and decision-making about AIDS in the context of high uncertainty about the disease and appropriate behavioral responses, and we argue that social interaction is animportant determinant of risk perceptions and the acceptability of behavioral change. Using longitudinal survey data from rural Kenya and Malawi, we test this hypothesis. We investigate whether social interactions-and especially theextent to which social network partners perceive themselves to be at risk -exert causal influenceson respondents' risk perceptions and on one approach to prevention, spousal communication about the threat of AIDS to the couple and their children. The study explicitly allows for the possibility that important characteristics, such as unobserved preferences or community characteristics, determine not only the outcomes of interest but also the size and composition of networks. The most important empirical result is that social networks have significant and substantial effects on risk perception and the adoption of new behaviors even after controlling for unobserved factors. Download Paper
</p> This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.  Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities  erform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications. Download Paper