航空燃油成本套期保值的动态最优性

Xiaolu Hu, M. Sy, Liuren Wu
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摘要

只有当政策接近最优时,套期保值才会创造价值,否则就可能有害。本文以美国航空业为例,推导出最优燃油成本对冲比率作为企业特定收入和成本敏感性的函数,以及油价变动中需求冲击和供应冲击的相对构成。本文基于原油期货收益对股票指数收益的滚动窗口回归,构建了市场对冲需求指数,并利用该指数捕捉了典型航空公司燃油成本对冲需求的时间变化。通过对不同市场条件下托宾Q对套期保值比率的对数回归,我们发现只有当市场套期保值需求较高时,燃料成本套期保值才会增加企业价值。更重要的是,我们使用航空公司套期保值比率与市场套期保值需求之间的时间序列相关性来衡量航空公司燃油套期保值实践的动态最优性。在我们25年的样本中,33家美国航空公司中有三分之一根本没有对冲,而超过三分之一的对冲比率与市场对冲需求的方向相反。只有不到三分之一的航空公司在对冲实践中表现出积极的动态最优。最优性估计的横截面多样性突出了实现最优策略的固有困难。然而,我们发现在航空公司的燃油成本套期保值实践中保持动态最优的强大价值:每个航空公司套期保值实践的动态最优性通过其托宾q的对数来衡量其估值,其价值增长来自其资产收益率变化的减少和平均收益的增加。具有负对冲动态最优性的航空公司不仅在降低资产变动收益方面是无效的,而且由于建立和维持昂贵的对冲计划以及进入和退出对冲头寸而产生额外的成本。这些额外成本降低了航空公司的平均回报率,损害了它们的估值。因此,他们的平均托宾Q甚至低于那些根本不对冲的航空公司的平均水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Optimality of Airline Fuel Cost Hedging
Hedging creates value only when the policy is near optimal but can be harmful otherwise. This paper takes the US airline industry as an example and derives the optimal fuel cost hedging ratio as a function of firm-specific revenue and cost sensitivities, as well as the relative composition of demand and supply shocks in the oil price movement. We construct a market hedging demand index based on rolling-window regression of crude futures returns on equity index returns and use the index to capture the time-variation in the fuel cost hedging demand for a typical airline. By regressing the logarithm of Tobin's Q against the hedging ratio under different market conditions, we show that fuel cost hedging increases firm value only when the market hedging demand is high. More important, we use the time-series correlation between an airline's hedging ratio and the market hedging demand to measure the dynamic optimality of the airline's fuel hedging practice. Out of the 33 US airlines in our sample over a 25-year period, one third do not hedge at all, while the hedging ratios for more than another one third move in the opposite direction of the market hedging demand. Only less than one third of airlines show positive dynamic optimality for their hedging practice. The cross-sectional diversity of the optimality estimates highlights the inherent difficulty of implementing an optimal policy. Still, we find strong value in staying dynamically optimal in an airline's fuel cost hedging practice: The dynamic optimality of each airline's hedging practice strongly and positively predicts its valuation as measured by the logarithm of its Tobin's Q. The value increase comes from both reduced variation in its return on asset and increased average return. Airlines with negative hedging dynamic optimality not only are ineffective in reducing their return on asset variation, but also incur extra costs from setting up and maintaining a costly hedging program and from entering and exiting hedging positions. Such extra costs lower the airlines' average return and hurt their valuation. As a result, their average Tobin's Q is even lower than the average for airlines that do not hedge at all.
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