Forecasting Realized Volatility Using a Nonnegative Semiparametric Time Series Model
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...
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Main Authors: | , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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Subjects: | |
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 |
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Institution: | Singapore Management University |
Language: | English |
Summary: | 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. |
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