Forecasting realized volatility using a nonnegative semiparametric model
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...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2019
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 |
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