The Centersfor Disease Control currently estimates that approximately 67%of the US adult population is overweight, with body mass index (BMI) between 25 and 29.9and34%is obese (BMI>30). BMI levels above 25 have been linked to diseases and health related consequences such as coronary heart disease, type 2 diabetes, cancers (endometrial, breast, and colon), hypertension, dyslipidemia, stroke, liver and gallbladder disease, sleep apnea and respiratory problems,osteoarthritis, and gynecological problems[66].
Volatility forecasting is central to financial decision making. To assess the tradeoffs between risk and return, investors need forecasts of average return and volatility of different assets. Similarly, for the pricing of options and other derivative securities with non-linear payoffs, investors not only need to estimate current volatility, but also need to forecast it up to the maturity of the options. Forecasts of covariances across assets are as invaluable for basic needs in finance, such as the efficient diversification of risks, or the valuation of convertible securities that can either become into stocks or bonds in the future. Motivated by recent economic models of time varying volatilities, in this paper we show that empirical measures of economic uncertainty predict future volatility above and beyond traditional lagged-based forecasts.
Most financial economists today believe that asset price volatilities and cross covariances change over time, and that the movements are persistent. Starting with the seminal work on auto regressive conditional heteroskedasticity in asset prices (Engle 1982), a family of models emerged in the time series literature that generalized this important finding, tested alternative specifications, and added information in economic variables (see, e.g., Bollerslev, Chou and Kroner 1992 for a survey). Despite its success in fitting volatility processes, the ARCH family of models has had only limited success in forecasting (Figlewski 1997). While Anderson and Bollerslev (1998) show that GARCH models do provide accurate one-week-ahead forecasts, Diebold and Christoffersen (2000) show that the forecasting power of these models declines quite rapidly with the time horizon. In this paper we augment these lag-based forecasts of variances and covariances with empirical measures of uncertainty that investors have over future inflation and earnings growth, and find quite remarkably that for forecast horizons of one and two years, the proportion of explained future variation increases from about a fourth to a half.
Value-at-Risk (VaR) is increasingly used in portfolio risk measurement, risk capital allocation and performance attribution. Financial risk managers are therefore rightfully concerned with the precision of typical VaR techniques.
The purpose of this paper is to assess the precision of common dynamic models and to quantify the magnitude of the estimation error by constructing confidence intervals around the point VaR and expected shortfall (ES) forecasts. A key challenge in constructing proper confidence intervals arises from the conditional variance dynamics typically found in speculative returns. Our paper suggests a resampling technique which accounts for parameter estimation error in dynamic models of portfolio variance.
