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

Paper:

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

Received:
October 24, 2018
Accepted:
January 11, 2019
Published:
July 20, 2019
Keywords:
realized volatility, time-varying probability density function, adaptive time-varying weight, combination forecast
Abstract
Forecasting Realized Volatility in Financial Markets Based on a Time-Varying Non-Parametric Model

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

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.

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.
Data files:
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Last updated on Nov. 18, 2019