The Relationship between Agricultural Commodities and Stock Market in Case of Thailand: Safe-haven, Hedge, or Diversifier? – Cross Quantilogram Analysis



Volume 8, Issue 3
Karjbundit Bunditsakulporn

Published online: 03 September 2022
Article Views: 20
 
AbstractReferencesCite
This paper proposes to identify Agricultural futures’ roles (Safe haven, Hedge, and Diversifier) in the Thai stock market during 2000-2020 by applying a bivariate Cross-Quantilogram (CQ) approach. The CQ approach can examine the cross-quantile correlation between assets, while the traditional approaches (such as GARCH, DCC, and MSV) examine only mean-to-mean dependency structures. The CQ methodology can estimate the tail dependencies and directional predictability between financial assets more accurately than traditional methods since financial assets typically have a skewed and asymmetric distribution. The correlation between assets during the extreme market condition (tail dependencies) is important to classify a financial asset as a Safe-haven role. The agricultural commodities considered in this study are the most active asset categories in the markets (cereals, oilseeds, other soft commodities, and miscellaneous commodities). The results show that the agricultural assets are more explicitly correlated with the Thai stock market in crisis periods, such as a negative result in canola during COVID-19. Agricultural commodities, including wheat, oats, and canola, can play a strong safe-haven role in the Thai stock market, according to the lowest cross-quantiles (bearish market) data. According to the results of overall quantiles (normal situations), wheat, corn, canola, soybean, and sugar can all be used as hedges. The rolling windows for directional predictability, which show the time-varying CQ, confirm that these agricultural commodities can be served as Safe-havens throughout the study periods. Therefore, including these specific agricultural commodities (Safe-haven or Hedge) in a portfolio of Thai stocks will help lower risk and boost performance under normal and extreme downturn situations.

  1. Aftab, M., Shah, S. Z. A., & Ismail, I. (2019). Does gold act as a hedge or a safe haven against equity and currency in Asia? Global Business Review, 20(1), 105–118. doi:https://doi.org/10.1177/0972150918803993
  2. Ahmad-Ur-Rehman, M., Haq, I. U., Jam, F. A., Ali, A., & Hijazi, S. T. (2010). Psychological contract breach and burnout, mediating role of job stress and feeling of violation. European Journal of Social Sciences, 17(2), 232–237.
  3. Banton, C. (2020). Commodities: The portfolio hedge. Retrieved from https://bit.ly/3e3EiuN
  4. Basu, P., & Gavin, W. T. (2010). What explains the growth in commodity derivatives? Federal Bank of St. Louis Review, 93(1), 37–48.
  5. Baumöhl, E., & Lyócsa, Š. (2017). Directional predictability from stock market sector indices to gold: A crossquantilogram analysis. Finance Research Letters, 23, 152–164. doi:https://doi.org/10.1016/j.frl.2017.02.013
  6. Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217–229. doi:https://doi.org/10.1111/j.1540-6288.2010.00244.x
  7. Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking & Finance, 34(8), 1886–1898. doi:https://doi.org/10.1016/j.jbankfin.2009.12.008
  8. Borg, E., & Kits, I. (2020). Dependence structures between commodity futures and corresponding producer indices across varying market conditions: A cross-quantilogram approach (Master’s thesis). Linköping University, Linköping, Sweden.
  9. Candila, V., & Farace, S. (2018). On the volatility spillover between agricultural commodities and Latin American stock markets. Risks, 6(4), 1-16. doi:https://doi.org/10.3390/risks6040116
  10. Cenesizoglu, T., & Timmermann, A. (2008). Is the distribution of stock returns predictable? Retrieved from https://bit.ly/3e8SS4k
  11. Cho, D., & Han, H. (2021). The tail behavior of safe haven currencies: A cross-quantilogram analysis. Journal of International Financial Markets, Institutions and Money, 70, 1-17. doi:https://doi.org/10.1016/j.intfin.2020.101257
  12. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. doi:https://doi.org/10.1080/01621459.1979.10482531
  13. Domanski, D., & Heath, A. (2007). Financial investors and commodity markets. Retrieved from https://bit.ly/3KvjGYg Fabozzi, F. J., Gupta, F., & Markowitz, H. M. (2002). The legacy of modern portfolio theory. The Journal of Investing, 11(3), 7–22. doi:https://doi.org/10.3905/joi.2002.319510
  14. Fabozzi, F. J., Markowitz, H. M., Kolm, P. N., & Gupta, F. (2012). Mean-variance model for portfolio selection. In Encyclopedia of financial models. New York, NY: John Wiley & Sons. doi:https://doi.org/10.1002/9781118182635.efm0003
  15. Han, H., Linton, O., Oka, T., & Whang, Y.-J. (2016). The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251–270. doi:https://doi.org/10.1016/j.jeconom.2016.03.001
  16. Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2000). Efficient use of commodity futures in diversified portfolios. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 20(5), 489–506. doi:Ji, Q., Zhang, D., & Zhao, Y. (2020). Searching for safe-haven assets during the COVID-19 pandemic. International Review of Financial Analysis, 71, 1-25. doi:https://doi.org/10.1016/j.irfa.2020.101526
  17. Kaul, A., & Sapp, S. (2006). Y2k fears and safe haven trading of the US dollar. Journal of International Money and Finance, 25(5), 760–779. doi:https://doi.org/10.1016/j.jimonfin.2006.04.003
  18. Kaur, G., & Dhiman, B. (2021). Agricultural commodities and FMCG stock prices in India: Evidence from the ARDL bound test and the Toda and Yamamoto causality analysis. Global Business Review, 22(5), 1190–1201. doi:https://doi.org/10.1177/0972150919830803
  19. Khan, S., Jam, F. A., Shahbaz, M., & Mamun, M. A. (2018). Electricity consumption, economic growth and trade openness in Kazakhstan: Evidence from cointegration and causality. OPEC Energy Review, 42(3), 224–243. doi:https://doi.org/10.1111/opec.12130
  20. Kinateder, H., Campbell, R., & Choudhury, T. (2021). Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets. Finance Research Letters, 43, 1-9. doi:https://doi.org/10.1016/j.frl.2021.101951
  21. Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159–178. doi:https://doi.org/10.1016/0304-4076(92)90104-Y
  22. Linton, O., & Whang, Y.-J. (2007). The quantilogram: With an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250–282. doi:https://doi.org/10.1016/j.jeconom.2007.01.004
  23. Mangram, M. E. (2013). A simplified perspective of the Markowitz portfolio theory. Global Journal of Business Research, 7(1), 59–70.
  24. Markowits, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 71–91. Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303–1313. doi:https://doi.org/10.1080/01621459.1994.10476870
  25. Ranaldo, A., & Söderlind, P. (2010). Safe haven currencies. Review of Finance, 14(3), 385–407. doi:https://doi.org/10.1093/rof/rfq007
  26. Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B. (2019). Is bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322–330. doi:https://doi.org/10.1016/j.irfa.2019.01.002
  27. Sieczka, P., & Hołyst, J. A. (2009). Correlations in commodity markets. Physica A: Statistical Mechanics and its Applications, 388(8), 1621–1630. doi:https://doi.org/10.1016/j.physa.2009.01.004
  28. Uddin, G. S., Rahman, M. L., Hedström, A., & Ahmed, A. (2019). Cross-quantilogram-based correlation and dependence between renewable energy stock and other asset classes. Energy Economics, 80, 743–759. doi: https://doi.org/10.1016/j.eneco.2019.02.014
  29. Waheed, M., Klobas, J. E., & Ain, N. (2020). Unveiling knowledge quality, researcher satisfaction, learning, and loyalty: A model of academic social media success. Information Technology & People, 34(1), 204-227. doi:https://doi.org/10.1108/ITP-07-2018-0345
  30. Waheed, M., Klobas, J. E., & Kaur, K. (2017). The importance of actual use in defining and measuring innovative behaviour: Comparison of e-book reader users and non-users. Journal of Librarianship and Information Science, 49(4), 368–379. doi:https://doi.org/10.1177/0961000616640030 
Bunditsakulporn, K. (2022). The relationship between agricultural commodities and stock market in case of Thailand: Safe-haven, hedge, or diversifier? – Cross quantilogram analysis. International Journal of Business and Administrative Studies, 8(3), 113-126 doi: https://dx.doi.org/10.20469/ijbas.8.10002-3