Forecasting realized volatility using a nonnegative semiparametric model - ERIKSSON, Anders (Institutional Knowledge at Singapore Management University, 2019)
This paper introduces a parsimonious and yet flexible semiparametric model to forecastfinancial volatility. The new model extends a related linear nonnegative autoregressive modelpreviously used in the volatility literature by way of a power transformation. It is semiparametric inthe sense that the distributional and functional form of its error component is partially unspecified.The statistical properties of the model are discussed and a novel estimation method is proposed.Simulation studies validate the new method and suggest that it works reasonably well in finitesamples. The out-of-sample forecasting performance of the proposed model is evaluated against anumber of standard models, using data on S&P 500 monthly realized volatilities. Some commonlyused loss functions are employed to evaluate the predictive accuracy of the alternative models. It isfound that the new model generally generates highly competitive forecasts