Skip to main content
SpringerLink
Log in
Book cover

International Conference of the Thailand Econometrics Society

TES 2019: Structural Changes and their Econometric Modeling pp 463–473Cite as

Markov Switching Constant Conditional Correlation GARCH Models for Hedging on Gold and Crude Oil

  • Conference paper
  • First Online:

  • 836 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 808))

Abstract

Commodities, especially gold and crude oil, are obvious to have a high fluctuation in recent years and lead a severe risk to investors. In this study, we consider a hedging strategy as a tool for offsetting the potential losses of investors. We develop various classes of Markov Switching constant conditional correlation GARCH model (MS-CCC-GARCH) to compute the optimal hedge ratios and portfolio weights in commodity markets (gold and crude oil) for the period of 2000–2018. We estimate three multivariate distributions MS-CCC-GARCH models (namely normal, student-t, and skewed student-t). The results find that MS-CCC-GARCH with student-t and normal distribution, for oil and gold, respectively, are the best model for hedge ratios and portfolio weights calculation in terms of the lowest AIC and BIC. Finally, the results of volatility and correlation of the best fit model are used to compute the value of hedge ratios and portfolio weights on gold and oil returns.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ahmad, W., Sadorsky, P., Sharma, A.: Optimal hedge ratios for clean energy equities. Econ. Model. 72, 278–295 (2018)

    Article  Google Scholar 

  2. Billio, M., Caporin, M.: Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis. Stat. Methods Appl. 14(2), 145–161 (2005)

    Article  MathSciNet  Google Scholar 

  3. Billio, M., Casarin, R., Osuntuyi, A.: Efficient Gibbs sampling for Markov switching GARCH models. Comput. Stat. Data Anal. 100, 37–57 (2016)

    Article  MathSciNet  Google Scholar 

  4. Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)

    Article  MathSciNet  Google Scholar 

  5. Chang, C.L., González-Serrano, L., Jimenez-Martin, J.A.: Currency hedging strategies using dynamic multivariate GARCH. Math. Comput. Simul. 94, 164–182 (2013)

    Article  MathSciNet  Google Scholar 

  6. Chen, S., Lee, C., Shrestha, K.: Futures hedge ratios: a review. Q. Rev. Econ. Financ. 43, 433–465 (2003)

    Article  Google Scholar 

  7. Chodchuangnirun, B., Yamaka, W., Khiewngamdee, C.: A regime switching for dynamic conditional correlation and GARCH: application to agricultural commodity prices and market risks. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 289–301. Springer, Cham, March 2018

    Google Scholar 

  8. Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384 (1989)

    Article  MathSciNet  Google Scholar 

  9. Hamilton, J.D., Susmel, R.: Autoregressive conditional heteroskedasticity and changes in regime. J. Econ. 64(1–2), 307–333 (1994)

    Article  Google Scholar 

  10. Hsu, C.C., Tseng, C.P., Wang, Y.H.: Dynamic hedging with futures: a copula-based GARCH model. J. Futur. Mark. 28(11), 1095–1116 (2008)

    Article  Google Scholar 

  11. Johnson, L.L.: The theory of hedging and speculation in commodity futures. Rev. Econ. Stud. 27(3), 139–151 (1960)

    Article  Google Scholar 

  12. Klaassen, F.: Improving GARCH volatility forecasts with regime-switching GARCH. In: Advances in Markov-Switching Models, pp. 223-254. Physica, Heidelberg (2002)

    Chapter  Google Scholar 

  13. Lamoureux, C.G., Lastrapes, W.D.: Heteroskedasticity in stock return data: volume versus GARCH effects. J. Financ. 45(1), 221–229 (1990)

    Article  Google Scholar 

  14. Pastpipatkul, P., Yamaka, W., Sriboonchitta, S.: Analyzing financial risk and co-movement of gold market, and indonesian, philippine, and thailand stock markets: dynamic copula with markov-switching. In: Causal Inference in Econometrics, pp. 565-586. Springer, Cham (2016)

    Google Scholar 

  15. Shrestha, K., Subramaniam, R., Peranginangin, Y., Philip, S.S.S.: Quantile hedge ratio for energy markets. Energy Econ. 71, 253–272 (2018)

    Article  Google Scholar 

  16. Vovk, V.G.: A logic of probability, with application to the foundations of statistics. J. R. Stat. Soc. Ser. B (Methodological), 317–351 (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International College of Digital Innovation, Chiang Mai University, Chiang Mai, Thailand

    Noppasit Chakpitak & Pichayakone Rakpho

  2. Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Pichayakone Rakpho & Woraphon Yamaka

Authors
  1. Noppasit Chakpitak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pichayakone Rakpho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Woraphon Yamaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pichayakone Rakpho .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chakpitak, N., Rakpho, P., Yamaka, W. (2019). Markov Switching Constant Conditional Correlation GARCH Models for Hedging on Gold and Crude Oil. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_36

Download citation

Publish with us

Policies and ethics

Search

Navigation