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Peer-Review Record

Multi-Horizon Dependence between Crude Oil and East Asian Stock Markets and Implications in Risk Management

Energies 2020, 13(2), 294; https://doi.org/10.3390/en13020294
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Energies 2020, 13(2), 294; https://doi.org/10.3390/en13020294
Received: 5 December 2019 / Revised: 31 December 2019 / Accepted: 2 January 2020 / Published: 7 January 2020
(This article belongs to the Section Energy Economics and Policy)

Round 1

Reviewer 1 Report

 The abstract lacks a brief conclusion and significance of findings.  Followed by methodology and results, and significance of work for practical situations sholud be presented. The English language could to be improved. Figure 1. unites should be introduced for prices? 

Author Response

Thank you very much for your comments. We briefly conclude the significance of findings for practical situations in the abstract. Unites for prices are introduced.

Reviewer 2 Report

Congratulations for the efforts made, first of all and a very interesting research with clear concluding elements. My comments below:

Section 1. Introduction.

Refresh information on global energy consumption, now is from 2016, with that available from 2018 Bp’s SEO, publication already released few months ago. Provide more references of existing literature on the relationship between commodity prices and stock markets to properly introduce the topic. Without this pre-assessment the study loses quality and perspective to understand the importance of the topic. Some examples of references that might be reviewed/included are: Hayette G. 2016. Linking the gas and oil markets with the stock market: Investigating the U.S. relationship. Energy Economics 2016; 53; 5-15 Huang, S, Haizhong, A., Gao, X, Sun, X. 2017. Do oil price asymmetric effects on the stock market persist in multiple time horizons?. Applied Energy, 185: 1799 Sadorsky, P., 2003. The macroeconomic determinants of technology stock price volatility. Review of Financial Economics 12, 191–205. Sadorsky, P. 2012. Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics 2012; 1: 248–255 The claim that ‘East Asian stock markets suffered huge losses in the periods of 1997 Asian and 2008 global turmoil’ is not clearly supported. Please state supporting evidence in the literature or delete. It is stated that research is novel but one can find in the existing literature that the topic has been already examined and also analysed in a similar manner. Some examples of references worth to mention to clarify this would be: Rania Jammazi, Juan C. Reboredo, Dependence and risk management in oil and stock markets. A wavelet-copula analysis, Energy, Volume 107, 2016, Pages 866-888, https://doi.org/10.1016/j.energy.2016.02.093. Jia, Xiaoliang & An, Haizhong & Fang, Wei & Sun, Xiaoqi & Huang, Xuan, 2015. "How do correlations of crude oil prices co-move? A grey correlation-based wavelet perspective," Energy Economics, Elsevier, vol. 49(C), pages 588-598.

 

Section 2. Model Specification.

5. The decision of choosing the MODWT as wavelet transform does not seem to be properly supported. Specifically, there is no literature review related to wavelet analysis made in similar situations in the study. The reason or reasons to select the wavelet transform in the particular case of crude oil and stock market returns relationship should be justified in a better manner.

 

Section 4. Empirical results.

It is stated that ‘we can observe volatility clustering effect in the short term, but not in the mid and long-term….’ . This consideration is technically incorrect as volatility clustering is a feature inherently embedded along the full time domain of the time series and as a result of this, quantification of volatility clustering effects is characteristic of the time series itself, not of some timely fractions. Moreover, it is probably worth mentioning that using autoregressive heteroskedastic models is not generally sufficient to analyse volatility clustering since this property is not intrinsically linked to a GARCH specification and other methodologies such as the analysis of autocorrelation of absolute returns or the copula approach have been developed. See:

 

Niu, H, Wang J. Volatility clustering and long memory of financial time series and financial price model. Digital Signal Processing 2013; 23: 489 – 498. Ning, C, Xu D, Wirjanto T. Is volatility clustering of asset returns asymmetric?. Journal of Banking & Finance. 2015; 52: 62 – 76..

 

Section 6. Conclusions.

The presentation of the results is made and concluding observations are really interesting for practitioners. However, it is stated to conclude that on one side, oil-stock dependence increases as the time-scale increases, but on the other side hedging benefits decrease also as time-scale increases. It would be very useful in this sense, to give at least author’s view why the two main concluding statements are not contradictory.

Author Response

Introduction:

Thank you very much for your comments. We refresh the information on global energy consumption to 2018. The claim about East Asian stock markets suffered huge losses in the periods of 1997 Asian and 2008 global turmoil’ is deleted. The literatures mentioned in the comments are provided in the reference.

 

Model specification:

We provide additional literatures and explanation to support the choice of MODWT.

 

Empirical results:

Since volatility clustering is irrelevant to the topic of this paper, we delete this sentence.

 

Conclusions:

We provide additional explanation from both statistical and economic perspectives in the conclusions to explain why the two main concluding statements are not contradictory.

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