Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2019

Abstract

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends a related linear nonnegative autoregressive model previously used in the volatility literature by way of a power transformation. It is semiparametric in the 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 finite samples. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities. Some commonly used loss functions are employed to evaluate the predictive accuracy of the alternative models. It is found that the new model generally generates highly competitive forecasts.

Keywords

volatility forecasting, realized volatility, linear programming estimator, Tukey’s power transformation, nonlinear nonnegative autoregression, forecast comparisons

Discipline

Econometrics | Finance | Finance and Financial Management

Research Areas

Econometrics

Publication

Journal of Risk and Financial Management

Volume

12

Issue

3

First Page

1

Last Page

23

ISSN

1911-8066

Identifier

10.3390/jrfm12030139

Publisher

MDPI

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.3390/jrfm12030139

Download

Share

COinS