Generalized Method of Moments Estimation of Realized Stochastic Volatility Model

Round 1

Reviewer 1 Report

The paper discusses an important research issue. The methods were correctly selected and the research procedure was clearly described and correctly carried out. After minor improvements adjusting the article to the formal requirements of the Journal, the paper may be published.

The goal of this paper is to find the new explanatory variable of realized volatility constructed from daily return data and add it to the stochastic volatility model to improve the prediction of volatility. Three methods are used to estimate the parameters of the improved model to compare the accuracy of the three approaches in estimating stochastic volatility models.

The section on motivation for the research is very concise. Application of stochastic realized volatility is only in: “Therefore, the study of stock market volatility and more accurate estimation and prediction of stock market fluctuations play an important role and significance in reducing stock market risks, maintaining the safe and stable development of the stock market, and ensuring the healthy and stable operation of the macroeconomy.” An extensive literature review could be valuable.

The literature is not up-to-date (the newest is one paper from 2018).

I recommend that the authors consider a discussion of the limitations of their study.

Authors mentioned: “In future research, we consider adding the leverage effect in the RSV model to improve the volatility prediction accuracy”.

The paper by Xinyu Wu, and Xiaona Wang, Forecasting volatility using realized stochastic volatility model with time-varying leverage effect, Finance Research Letters, Volume 34, 2020, https://doi.org/10.1016/j.frl.2019.08.019 could be helpful.

 

Author Response

Thank you very much for your constructive comments and suggestions, these suggestions help us improve our paper significantly. All of the comments have been carefully accounted for, in what follows, your comments are shown in italic and followed by our responses.

1. The section on motivation for the research is very concise. Application of stochastic realized volatility is only in: “Therefore, the study of stock market volatility and more accurate estimation and prediction of stock market fluctuations play an important role and significance in reducing stock market risks, maintaining the safe and stable development of the stock market, and ensuring the healthy and stable operation of the macroeconomy.” An extensive literature review could be valuable.

Reply: Thank you very much for your comment! I have enhanced the literature review in this section. We revised it as follows:
“Therefore, the study of stock market volatility and more accurate estimation and prediction of stock market fluctuations play an important role and significance in reducing stock market risks, maintaining the safe and stable development of the stock market, and ensuring the healthy and stable operation of the macro economy, refer to Brooks and Persand (2003) [10], Giot and Laurent (2004) [14]. With the rapid advancement of computer technology, accessing high-frequency financial data has become easier. Using high frequency data, we can estimate realized volatility, refer to Andersen et al. (2003) [2], Barndorff-Nielsen and Shephard (2002) [7], Jacod et al. (2009) [22], etc. By incorporating high-frequency financial data, it provides a more accurate measure of market volatility compared to traditional methods.”

2. The literature is not up-to-date (the newest is one paper from 2018).
Reply: Thank you very much for your kind comment! We have included some

recent literature in this version.

Wu, X., and Wang, X. Forecasting volatility using realized stochastic volatility model with time-varying leverage effect. Finance Research Letters, 2020, 34, 101271.

Liu, J. A Bayesian semiparametric realized stochastic volatility model. Journal of Risk and Financial Management, 2021,14(12), 617.

Takahashi, M., Watanabe, T., and Omori, Y. Forecasting daily volatility of stock price index using daily returns and realized volatility. Econometrics and Statistics, 2021.

Bormetti, G., Casarin, R., Corsi, F., and Livieri, G. A stochastic volatility model with realized measures for option pricing. Journal of Business & Economic Statistics, 2020, 38(4), 856-871.

3. I recommend that the authors consider a discussion of the limitations of their study.

Authors mentioned: “In future research, we consider adding the leverage effect in the RSV model to improve the volatility prediction accuracy”.
The paper by Xinyu Wu, and Xiaona Wang, Forecasting volatility using realized stochastic volatility model with time-varying leverage effect, Finance Research Letters, Volume 34, 2020, https://doi.org/10.1016/j.frl.2019.08.019 could be helpful.

Reply: Great comment, thanks! I have enhanced the limitations of our study. We revised it as follows:

“Although the realized volatility is introduced on the basis of random volatility model, this paper still assumes that the disturbance term obeys normal distribution. According to the research in recent years, it is shown that the model disturbance term obeys the generalized hyperbolic distribution, which may improve the prediction effect of the model. For the improved model, we can consider using the efficient generalized moment estimation parameter estimation method to estimate the unknown parameters.”

This paper is very helpful, we refer to this paper and we can do the research about leverage effect in the RSV model in the future.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors

I would like to appreciate you for submitting the manuscript to this journal. However, I have some issue in your manuscript that the recent literature related to your topic is not included and reviewed. If you are possible to include the literature, you add. Otherwise, your manuscript is well. Thus, I recommend this manuscript to publish with minor correction. 

English is good but some spelling errors has been found. The authors are advised to correct the spelling before publishing this manuscript. 

Author Response

Please see the attachment. Thank you!

Author Response File: Author Response.pdf

Reviewer 3 Report

1) What is the difference between the differents loss functions investigated in the empirical research? are there sufficient to validate your model?

2) Why have you choosed WINBUGS software?

3) What is the impact of this work in the socio-economic world?

Should be improved

Author Response

Please see the attachment, thank you!

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Accept as it.