Forecasting realized volatility using a nonnegative semiparametric model

• Main Authors: ERIKSSON, Anders, PREVE, Daniel P. A., Jun YU
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: volatility forecasting; realized volatility; linear programming estimator; Tukey’s power transformation; nonlinear nonnegative autoregression; forecast comparisons; Econometrics; Finance; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/soe_research/2312; https://ink.library.smu.edu.sg/context/soe_research/article/3311/viewcontent/jrfm_12_00139_pv_oa.pdf
• Institution: Singapore Management University

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