Forecasting Realized Volatility Using a Nonnegative Semiparametric Time Series Model

• Main Authors: Eriksson, A., Preve, D., YU, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Autoregression; nonlinear/non-Gaussian time series; realized volatility; semiparametric model; volatility forecast.; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1296; https://ink.library.smu.edu.sg/context/soe_research/article/2295/viewcontent/PEY.pdf
• Institution: Singapore Management University

This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the distributional form of its error component is left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Asymptotic properties are established for the new estimation method. Simulation studies validate the new estimation method. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.