JACIII Vol.23 No.4 pp. 641-648
doi: 10.20965/jaciii.2019.p0641


Forecasting Realized Volatility in Financial Markets Based on a Time-Varying Non-Parametric Model

Wentao Gu*,†, Zhongdi Liu*, Cui Dong*, Jian He*, and Ming-Chuan Hsieh**

*School of Statistics and Mathematics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

Corresponding author

**Research Center for Testing and Assessment, National Academy for Educational Research
No.2 Sanshu Road, Sanxia District, New Taipei City 23703, Taiwan

October 24, 2018
January 11, 2019
July 20, 2019
realized volatility, time-varying probability density function, adaptive time-varying weight, combination forecast

This paper proposes a new non-parametric adaptive combination model for the prediction of realized volatility on the basis of applying and extending the time-varying probability density function theory. We initially construct an adaptive time-varying weight mechanism for a combination forecast. To compare the predictive power of the models, we take the SPA test, which uses bootstrap as the evaluation criterion and employs the rolling window strategy for out-of-sample forecasting. The empirical study shows that the non-parametric TVF model forecasts more accurately than the HAR-RV model. In addition, the average combination forecast model does not have a significant advantage over any single model while our adaptive combination model does.

The predictive performance of the arithmetic average and adaptive combination forecast models

The predictive performance of the arithmetic average and adaptive combination forecast models

Cite this article as:
W. Gu, Z. Liu, C. Dong, J. He, and M. Hsieh, “Forecasting Realized Volatility in Financial Markets Based on a Time-Varying Non-Parametric Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.4, pp. 641-648, 2019.
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Last updated on May. 28, 2024