Kwansei Gakuin University Researcher Provides New Study Findings on Risk Management (A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression): Insurance – Risk Management

2024 FEB 06 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on risk management. According to news originating from Hyogo, Japan, by NewsRx editors, the research stated, “In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV).”

Financial supporters for this research include Institute of Statistical Mathematics; Jsps Kakenhi.

The news correspondents obtained a quote from the research from Kwansei Gakuin University: “The first model is a residual-type model, where the RV is first predicted using the HAR model, and the residuals are used to train the SVR model. The residual component is then predicted using the SVR model, and the results from both the HAR and SVR models are combined to obtain the final prediction. The second model is a weight-based model, which is a combination of the HAR and SVR models and uses the same independent variables and dependent variables as the HAR model; we adjust the contribution of the two models to the predicted values by giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models.”

According to the news reporters, the research concluded: “For empirical analysis, the RV of returns of the Tokyo stock price index and five individual stocks of TOPIX 30 is used as the dataset. The empirical results reveal that according to the model confidence set test, the weight-type model outperforms the HAR model and the residual-type HAR-SVR model.”

For more information on this research see: A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression. Risks, 2024,12(1). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks12010012.

Our news editors report that more information may be obtained by contacting Yue Zhuo, Graduate School of Science and Technology, Kwansei Gakuin University, Gakuen Uegahara, Sanda 669-1330, Hyogo, Japan. Additional authors for this research include Takayuki Morimoto.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Yue Zhuo (http://orcid.org/0009-0007-5291-6933), Takayuki Morimoto (http://orcid.org/0000-0001-8035-3276).

(Our reports deliver fact-based news of research and discoveries from around the world.)