This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen and Shephard (2001) and Nielsen and Shephard (2003) by way of a Box-Cox transformation. It is semiparametric in the sense that the dependency structure and the distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Its out-of-sample performance 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 tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the new model perform exceptionally well under the mean absolute error and the mean absolute percentage error measures.
Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast.
Preve, D.; Eriksson, A.; and YU, Jun.
Forecasting Realized Volatility Using a Nonnegative Semiparametric Model. (2009). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1158
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