IDEAS home Printed from https://ideas.repec.org/p/ags/aaea16/235444.html
   My bibliography  Save this paper

Corporate Hedging In Incomplete Markets: A Solution Under Price Transmission

Author

Listed:
  • Luo, Rui
  • Fortenbery, T. Randall

Abstract

In this paper we provide a dynamic minimum-variance hedging strategy for firms in incomplete markets. Firms are looking for improved methods to more efficiently hedge input and output price risk exposure, but it is often the case that all price risk cannot be eliminated through exchange traded futures contracts. Since futures contracts exist for a limited number of assets some sources of price risk cannot be directly hedged and thus hedging markets are incomplete for many firms. If related futures contracts do not exist for both input and output price risk the traditional approach is to employ a one-sided hedge, that is, to hedge only for the risk source with related futures contracts and remain unhedged in the other. By identifying the price transmission (PT) mechanism between input and output prices in a classical complete-market model, we present a two-sided hedge that enables firms to minimize both input and output price fluctuations through a single tradable futures contract even in incomplete markets. Specifically, since in different industries PT is expected to vary in direction and magnitude, we consider four subcases in the model according to the direction of PT and the availability of futures contracts: (CO) cost-driving PT in which supply forces lead to equilibrium between input and output prices with output futures contracts; (CI) cost-driving PT with input futures contracts; (DO) demand-driving PT with output futures contracts; and (DI) demand-driving PT with input futures contracts. In all cases the firm can only directly hedge cash positions with futures contracts (a one-sided hedge), or jointly hedge input and output price risk with a single futures contract (a two-sided hedge) but accounting for PT. A two-factor diffusion model with a stochastic, mean-reverting convenience yield is assumed for the underlying asset. The optimal dynamic hedges are the weighted averages of the classic minimizing direct hedge and cross hedging ratios. We apply our results to the problem of a hypothetical firm that uses light sweet crude oil to produce jet fuel. The firm intends to reduce price exposure with a futures contract on light sweet crude oil. We compare hedging policies and hedging effectiveness between the one-sided and two-sided hedges. Weekly data of futures prices for light sweet crude oil for delivery to Cushing, OK, and spot prices for New York Harbor jet fuel, and spot prices for light sweet crude oil from April 4, 1990 to August, 16, 2015 are used to perform the analysis. We find that the two-sided model results in a more effective hedge. These findings suggest that jet fuel producers will most efficiently reduce profit fluctuations using a hedging model that directly accounts for vertical price links between the input and output prices. The contribution of this paper consists of devising a dynamic two-sided hedge ratio for firms to jointly hedge input and output payoffs in incomplete markets by incorporating the PT mechanism into the traditional complete-market minimizing hedging model. As PT is an important characteristic describing the overall operation of the market, this strategy may be practical for firms in multiple industries.

Suggested Citation

  • Luo, Rui & Fortenbery, T. Randall, 2016. "Corporate Hedging In Incomplete Markets: A Solution Under Price Transmission," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235444, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea16:235444
    DOI: 10.22004/ag.econ.235444
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/235444/files/RuiLuo_poster_cover.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.235444?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Choudhry, Taufiq, 2003. "Short-run deviations and optimal hedge ratio: evidence from stock futures," Journal of Multinational Financial Management, Elsevier, vol. 13(2), pages 171-192, April.
    2. Barry K. Goodwin & Matthew T. Holt, 1999. "Price Transmission and Asymmetric Adjustment in the U.S. Beef Sector," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 630-637.
    3. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    4. Suleyman Basak & Georgy Chabakauri, 2012. "Dynamic Hedging in Incomplete Markets: A Simple Solution," The Review of Financial Studies, Society for Financial Studies, vol. 25(6), pages 1845-1896.
    5. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    6. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    7. Jonathan M. Godbey & Jimmy E. Hilliard, 2007. "Adjusting stacked-hedge ratios for stochastic convenience yield: a minimum variance approach," Quantitative Finance, Taylor & Francis Journals, vol. 7(3), pages 289-300.
    8. Fok, Robert C. W. & Carroll, Carolyn & Chiou, Ming C., 1997. "Determinants of corporate hedging and derivatives: A revisit," Journal of Economics and Business, Elsevier, vol. 49(6), pages 569-585.
    9. von Cramon-Taubadel, Stephan, 1998. "Estimating Asymmetric Price Transmission with the Error Correction Representation: An application to the German Pork Market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 25(1), pages 1-18.
    10. Michael W. Brandt, 2003. "Hedging Demands in Hedging Contingent Claims," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 119-140, February.
    11. William W. Wilson, 1989. "Price discovery and hedging in the sunflower market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 9(5), pages 377-391, October.
    12. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    13. C. He & J. Kennedy & T. Coleman & P. Forsyth & Y. Li & K. Vetzal, 2006. "Calibration and hedging under jump diffusion," Review of Derivatives Research, Springer, vol. 9(1), pages 1-35, January.
    14. Gibson, Rajna & Schwartz, Eduardo S, 1990. "Stochastic Convenience Yield and the Pricing of Oil Contingent Claims," Journal of Finance, American Finance Association, vol. 45(3), pages 959-976, July.
    15. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
    16. Jaksa Cvitanic & Fernando Zapatero, 2004. "Introduction to the Economics and Mathematics of Financial Markets," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262532654, December.
    17. Mark Bertus & Jonathan Godbey & Jimmy E. Hilliard, 2009. "Minimum variance cross hedging under mean‐reverting spreads, stochastic convenience yields, and jumps: Application to the airline industry," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(8), pages 736-756, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chris Brooks & Marcel Prokopczuk, 2013. "The dynamics of commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 527-542, March.
    2. Suleyman Basak & Georgy Chabakauri, 2012. "Dynamic Hedging in Incomplete Markets: A Simple Solution," The Review of Financial Studies, Society for Financial Studies, vol. 25(6), pages 1845-1896.
    3. Back, Janis & Prokopczuk, Marcel & Rudolf, Markus, 2013. "Seasonality and the valuation of commodity options," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 273-290.
    4. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, October.
    5. Max F. Schöne & Stefan Spinler, 2017. "A four-factor stochastic volatility model of commodity prices," Review of Derivatives Research, Springer, vol. 20(2), pages 135-165, July.
    6. Almansour, Abdullah, 2016. "Convenience yield in commodity price modeling: A regime switching approach," Energy Economics, Elsevier, vol. 53(C), pages 238-247.
    7. Arismendi, Juan C. & Back, Janis & Prokopczuk, Marcel & Paschke, Raphael & Rudolf, Markus, 2016. "Seasonal Stochastic Volatility: Implications for the pricing of commodity options," Journal of Banking & Finance, Elsevier, vol. 66(C), pages 53-65.
    8. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.
    9. Cheng, Benjamin & Nikitopoulos, Christina Sklibosios & Schlögl, Erik, 2018. "Pricing of long-dated commodity derivatives: Do stochastic interest rates matter?," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 148-166.
    10. Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian & Sebastien Mcmahon, 2008. "Forecasting commodity prices: GARCH, jumps, and mean reversion," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 279-291.
    11. Gonzalo Cortazar & Simon Gutierrez & Hector Ortega, 2016. "Empirical Performance of Commodity Pricing Models: When is it Worthwhile to Use a Stochastic Volatility Specification?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 457-487, May.
    12. Ni, Jian & Chu, Lap Keung & Wu, Feng & Sculli, Domenic & Shi, Yuan, 2012. "A multi-stage financial hedging approach for the procurement of manufacturing materials," European Journal of Operational Research, Elsevier, vol. 221(2), pages 424-431.
    13. Askari, Hossein & Krichene, Noureddine, 2008. "Oil price dynamics (2002-2006)," Energy Economics, Elsevier, vol. 30(5), pages 2134-2153, September.
    14. Hilliard, Jimmy E. & Hilliard, Jitka, 2019. "A jump-diffusion model for pricing and hedging with margined options: An application to Brent crude oil contracts," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 137-155.
    15. Guedes, José & Santos, Pedro, 2016. "Valuing an offshore oil exploration and production project through real options analysis," Energy Economics, Elsevier, vol. 60(C), pages 377-386.
    16. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    17. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    18. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    19. Ames, Matthew & Bagnarosa, Guillaume & Matsui, Tomoko & Peters, Gareth W. & Shevchenko, Pavel V., 2020. "Which risk factors drive oil futures price curves?," Energy Economics, Elsevier, vol. 87(C).
    20. Aur'elien Alfonsi & Nerea Vadillo, 2023. "Risk valuation of quanto derivatives on temperature and electricity," Papers 2310.07692, arXiv.org, revised Apr 2024.

    More about this item

    Keywords

    Financial Economics; Resource /Energy Economics and Policy; Risk and Uncertainty;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea16:235444. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.