tailieunhanh - Handbook of Economic Forecasting part 87

Handbook of Economic Forecasting part 87. Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing | 834 TG. Andersen et al. assumptions based primarily upon arbitrage-free financial markets. As such it allows us to harness the information inherent in high-frequency returns for assessment of lower frequency return volatility. It is thus the natural approach to measuring actual expost realized return variation over a given horizon. This perspective has now gained widespread acceptance in the literature where alternative volatility forecast models are routinely assessed in terms of their ability to explain the distribution of subsequent realized volatility as defined above. . Realized volatility modeling The realized volatility is by construction an observed proxy for the underlying quadratic variation and the associated measurement errors are uncorrelated. This suggests a straightforward approach where the temporal features of the series are modeled through standard time series techniques letting the data guide the choice of the appropriate distributional assumptions and the dynamic representation. This is akin to the standard procedure for modeling macroeconomic data where the underlying quantities are measured most likely with a substantial degree of error and then treated as directly observed variables. The strategy of estimating time series models directly for realized volatility is advocated in a sequence of papers by Andersen et al. 2001a 2001b 2003 . A striking finding is that the realized volatility series share fundamental statistical properties across different asset classes time periods and countries. The evidence points strongly toward a long-memory type of dependency in volatility. Moreover the logarithmic realized volatility series is typically much closer to being homoskedastic and approximately unconditionally Gaussian. These features are readily captured through an ARFIMA p d 0 representation of the logarithmic realized volatility L 1 - L d logRV t A - Mo ut t 1 2 . T where 1 - L d denotes the fractional differencing operator L is a .