Journal of Korea Port Economic Association (한국항만경제학회지)

The Korea Port Economic Association (한국항만경제학회)
권호 표지
DOI QR코드

DOI QR Code

The impact of market fear, uncertainty, stock market, and maritime freight index on the risk-return relationship in the crude oil market

시장 공포, 불확실성, 주식시장, 해상운임지수가 원유시장의 위험-수익 관계에 미치는 영향

  • 최기홍 (부산대학교 경제통상연구원)
  • Received : 2022.12.09
  • Accepted : 2022.12.27
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.

본 연구에서는 원유시장을 대상으로 위험-수익 관계와 시장 공포, 불확실성, 주식시장, 해상운임지수 사이의 연관성을 검증하기 위해 2002년 1월부터 2022년 6월까지 일별자료를 이용하여 분석하였다. 본 연구를 위해 위험-수익 관계는 TVP-EGARCH-M 모형을 적용하였으며, 시장 공포, 불확실성, 주식시장, 해상운임지수와의 관계를 분석하기 위해 웨이블릿 일치성 모형을 이용하였다. 본 연구의 분석결과는 다음과 같다. 첫째, 시간 가변적 위험-수익 관계 결과에 따르면, 원유시장도 높은 수익률과 높은 위험과 관련이 있는 것으로 나타났다. 둘째, 상관관계와 그랜져 인과관계 분석결과, 위험-수익 관계와 VIX, EPU, S&P500, BDI 사이에서 약한 상관관계가 존재하는 것으로 나타났다. 그리고 EPU, S&P500과 위험-수익 관계에서 양방향 인과관계가 존재하지 않는 것으로 나타났지만 VIX와 BDI는 위험-수익 관계에 영향을 주는 것으로 나타났다. 셋째, 웨이블릿 일관성 결과를 보면, 위험-수익 관계와 VIX, EPU, S&P500, BDI 간의 관계 정도는 시간 가변적인 것으로 나타났다. 특히, 위기기간(금융위기, 코로나19) 전후에 서로 간의 관계가 높은 것으로 나타났다. 그리고 장기에 연관성이 높은 것으로 나타났다. 또한 위험-수익 관계는 VIX, EPU와는 양(+)의 관계, S&P500, BDI와는 음(-)의 관계가 있는 것으로 나타났다. 따라서 시장참여자가 의사결정을 할 때 경제적인 환경 변화를 잘 인식해야 할 것이다.

Keywords

Acknowledgement

이 논문 또는 저서는 2021년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2021S1A5B5A16078200)

References

  1. Booth, G. G., Fung, H. G., and Leung, W. K. (2016). A risk-return explanation of the momentum-reversal "anomaly". Journal of Empirical Finance, 35, 68-77. https://doi.org/10.1016/j.jempfin.2015.10.007
  2. Brandt, M., and Wang, L. (2010). Measuring the time-varying risk-return relation from the cross-section of equity returns. Manuscript, Duke University.
  3. Campbell, J. Y., and Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of financial Economics, 31(3), 281-318.
  4. Chan, K. C., Karolyi, G. A., and Stulz, R. (1992). Global financial markets and the risk premium on US equity. Journal of Financial Economics, 32(2), 137-167. https://doi.org/10.1016/0304-405X(92)90016-Q
  5. Chatrath, A., Miao, H., Ramchander, S., and Wang, T. (2016). An examination of the flow characteristics of crude oil: Evidence from risk-neutral moments. Energy Economics, 54, 213-223. https://doi.org/10.1016/j.eneco.2015.12.005
  6. Chou, R., Engle, R. F., and Kane, A. (1992). Measuring risk aversion from excess returns on a stock index. Journal of Econometrics, 52(1-2), 201-224. https://doi.org/10.1016/0304-4076(92)90070-8
  7. Cotter, J., and Hanly, J. (2010). Time-varying risk aversion: an application to energy hedging. Energy Economics, 32(2), 432-441. https://doi.org/10.1016/j.eneco.2009.08.009
  8. Diaz, E. M., Molero, J. C., and de Gracia, F. P. (2016). Oil price volatility and stock returns in the G7 economies. Energy Economics, 54, 417-430. https://doi.org/10.1016/j.eneco.2016.01.002
  9. Gong, X., Wen, F., Xia, X. H., Huang, J., and Pan, B. (2017). Investigating the risk-return trade-off for crude oil futures using high-frequency data. Applied Energy, 196, 152-161. https://doi.org/10.1016/j.apenergy.2016.11.112
  10. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of financial economics, 76(3), 509-548. https://doi.org/10.1016/j.jfineco.2004.03.008
  11. He, Z., He, L., and Wen, F. (2019). Risk compensation and market returns: The role of investor sentiment in the stock market. Emerging Markets Finance and Trade, 55(3), 704-718. https://doi.org/10.1080/1540496X.2018.1460724
  12. Hongsakulvasu, N., and Liammukda, A. (2020). The risk-return relationship in crude oil markets during COVID-19 pandemic: Evidence from time-varying coefficient GARCH-in-mean model. The Journal of Asian Finance, Economics and Business, 7(10), 63-71. https://doi.org/10.13106/jafeb.2020.vol7.no10.063
  13. Ji, Q., and Fan, Y. (2015). Dynamic integration of world oil prices: A reinvestigation of globalisation vs. regionalisation. Applied Energy, 155, 171-180. https://doi.org/10.1016/j.apenergy.2015.05.117
  14. Kolos, S. P., and Ronn, E. I. (2008). Estimating the commodity market price of risk for energy prices. Energy Economics, 30(2), 621-641. https://doi.org/10.1016/j.eneco.2007.09.005
  15. Kristoufek, L. (2014). Leverage effect in energy futures. Energy Economics, 45, 1-9. https://doi.org/10.1016/j.eneco.2014.06.009
  16. Lettau, M., and Ludvigson, S. C. (2010). Measuring and modeling variation in the risk-return trade-off. Handbook of financial econometrics: Tools and techniques, 617-690.
  17. Li, Z., Sun, J., and Wang, S. (2013). An information diffusion-based model of oil futures price. Energy Economics, 36, 518-525. https://doi.org/10.1016/j.eneco.2012.10.009
  18. Lundblad, C. (2007). The risk return tradeoff in the long run: 1836-2003. Journal of Financial Economics, 85(1), 123-150.
  19. Lyu, Y., Tuo, S., Wei, Y., and Yang, M. (2021). Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility: New evidence. Resources Policy, 70, 101943.
  20. Mohanty, S. K., Nandha, M., Turkistani, A. Q., and Alaitani, M. Y. (2011). Oil price movements and stock market returns: Evidence from Gulf Cooperation Council (GCC) countries. Global Finance Journal, 22(1), 42-55.
  21. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370.
  22. Qadan, M., and Nama, H. (2018). Investor sentiment and the price of oil. Energy Economics, 69, 42-58. https://doi.org/10.1016/j.eneco.2017.10.035
  23. Rua, A., and Nunes, L. C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16(4), 632-639. https://doi.org/10.1016/j.jempfin.2009.02.002
  24. Torrence, C., and Webster, P. J. (1999). Interdecadal changes in the ENSO-monsoon system. Journal of climate, 12(8), 2679-2690. https://doi.org/10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2
  25. Van Robays, I. (2016). Macroeconomic uncertainty and oil price volatility. Oxford Bulletin of Economics and Statistics, 78(5), 671-693. https://doi.org/10.1111/obes.12124
  26. Wen, F., He, Z., Dai, Z., and Yang, X. (2014). Characteristics of investors' risk preference for stock markets. Economic Computation & Economic Cybernetics Studies & Research, 48(3), 235-254.
  27. Wen, F., & Yang, X. (2009). Skewness of return distribution and coefficient of risk premium. Journal of Systems Science and Complexity, 22(3), 360-371. https://doi.org/10.1007/s11424-009-9170-x