Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)

For citation:

Karatetskaia E. Y., Lakshina V. V. Multiple Hedging on Energy Market. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2019, vol. 19, iss. 1, pp. 105-113. DOI: 10.18500/1816-9791-2019-19-1-105-113, EDN: TQVEIV

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
28.02.2019
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Language: 
English
Heading: 
Computer Sciences
Article type: 
Article
UDC: 
519.25
DOI: 
10.18500/1816-9791-2019-19-1-105-113
EDN: 
TQVEIV

Multiple Hedging on Energy Market

Autors: 
Karatetskaia Efrosiniia Yu., Higher School of Economics – National Research University
Lakshina Valeriya V., Higher School of Economics – National Research University
Abstract: 

The article is devoted to the calculation of the dynamic hedge ratio based on three different types of volatility models, among which S-BEKK-GARCH model takes into account cross-sectional dependence. The hedging strategy is built for eight stock-futures pairs on energy market in Russia.

Key words: 
multivariate volatility models
spatial specifications
dynamic hedge ratio
energy market
References: 
  1. Cotter J., Hanly J. A utility based approach to energy hedging. Energy Economics, 2012, vol. 34, iss. 3, pp. 817–827.
  2. Shrestha K., Subramaniam R., Peranginangin Y. Quantile hedge ratio for energy markets. Energy Economics, 2018, vol. 71, iss. 3, pp. 253–272.
  3. Ederington L. The hedging performance of the new futures markets. The Journal of Finance, 1979, vol. 34, iss. 1, pp. 157–170.
  4. Mohamed El Hedi Arouri, Jamel Jouini, Duc Khuong Nguyen. On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 2012, vol. 34, no. 2, iss. 2, pp. 611–617. DOI: https://doi.org/10.1016/j.eneco.2011.08.009
  5. Khalfaoui R., Boutahar M., Boubaker H. Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics, 2015, vol. 49, iss. C, pp. 540–549.
  6. Billio M., Casarin R., Osuntuyi A. Markov switching GARCH models for Bayesian hedging on energy futures markets. Energy Economics, 2018, vol. 70, iss. C, pp. 545–562.
  7. Olson E., Vivian A. J., Wohar M. E. The relationship between energy and equity markets: Evidence from volatility impulse response functions. Energy Economics, 2014, vol. 43, iss. C, pp. 297–305.
  8. Wang Y., Liu L. Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging. Empirical Economics, 2016, vol. 50, iss. 4, pp. 1481–1509.
  9. Walid Mensi, Shawkat Hammoudeh, Sang Hoon Kang. Risk spillovers and portfolio management between developed and BRICS stock markets. The North American Journal of Economics and Finance, 2017, vol. 41, pp. 133–155. DOI: https://doi.org/10.1016/j.najef.2017.03.006
  10. Ghoddusi H., Emamzadehfard S. Optimal hedging in the US natural gas market: The effect of maturity and cointegration. Energy Economics, 2017, vol. 63, iss. C, pp. 92–105.
  11. Arnold M., Stahlberg S., Wied D. Modeling different kinds of spatial dependence in stock returns. Empirical Economics, 2013, vol. 44, iss. 2, pp. 761–774.
  12. Fernández-Avilés G., Montero J., Orlov A. Spatial modeling of stock market comovements. Finance Research Letters, 2012, vol. 9, iss. 4, pp. 202–212.
  13. Huaying Gu, Zhixue Liu, Yingliang Weng. Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach. Physica A: Statistical Mechanics and its Applications, 2017, vol. 471, pp. 460–472. DOI: https://doi.org/10.1016/j.physa.2016.12.056
  14. Anatolyev S., Khrapov S. Do spatial structures yield better volatility forecasts? 2016. Available at: https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=EEA... (accessed 18 May 2018).
  15. Chen X., Tian Y. Impact Effects and Spatial Volatility Spillover Effects of Sovereign Cred- it Rating Downgrades-Empirical Analysis of Multivariate Spatial-BEKK-GARCH Model Based on Symbolic Transfer Entropy. Bolet ´ in T écnico, 2017, vol. 55, no. 9, pp. 614–623.
  16. Fernández V. Multi-period hedge ratios for a multi-asset portfolio when accounting for returns comovement. The Journal of Futures Markets, 2008, vol. 28, iss. 2, pp. 182–207.
  17. Massimiliano Caporin, Paolo Paruolo. Proximity-Structured Multivariate Volatility Models. Econometric Reviews, 2015, vol. 34, iss. 5, pp. 559–593. DOI: https://doi.org/10.1080/07474938.2013.807102
  18. Luc Bauwens, Sébastien Laurent, Jeroen V. K. Rombouts. Multivariate GARCH models: a survey. Journal of Applied Econometrics, 2006, vol. 21, iss. 1, pp. 79–109. DOI: https://doi.org/10.1002/jae.842
  19. Borovkova S. A., Lopuhaa Rik. Spatial GARCH: A Spatial Approach to Multivariate Volatility Modeling. Econometric Reviews, 2015, vol. 34, iss. 5, pp. 559–593. DOI: https://doi.org/10.1080/07474938.2013.807102
  20. Robert F. Engle, Kenneth F. Kroner. Multivariate Simultaneous Generalized ARCH. Econometric Theory, 1995, vol. 11, iss. 1, pp. 122–150. DOI: https://doi.org/10.1017/s0266466600009063
  21. Engle R. Dynamic Conditional Correlation. Journal of Business & Economic Statistics, 2002, vol. 20, no. 3, iss. 3, pp. 339–350. DOI: https://doi.org/10.1198/073500102288618487
  22. van der Weide R. GO-GARCH: a multivariate generalized orthogonal GARCH model. Journal of Applied Econometrics, 2002, vol. 17, iss. 5, pp. 549–564. DOI: https://doi.org/10.1002/jae.688
  23. Cont R. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 2001, vol. 1, iss. 2, pp. 223–236.
  24. Bessembinder H., Seguin Paul J. Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets. The Journal of Financial and Quantitative Analysis, 1993, vol. 28, iss. 1, pp. 21–39.
Received: 
14.08.2018
Accepted: 
01.10.2018
Published: 
28.02.2019
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