Risk Management of Fuel Hedging Strategy Based on CVaR and Markov Switching GARCH in Airline Company

Shuang Lin, Minke Wang, Zhihong Cheng, Fan He, Jiuhao Chen, Chuanhui Liao and Shengda Zhang ()
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Shuang Lin: School of Economics and Management, Civil Aviation Flight University of China, Deyang 618307, China
Minke Wang: School of Airport Engineering, Civil Aviation Flight University of China, Deyang 618307, China
Zhihong Cheng: Department of Adminstration, Civil Aviation Flight University of China, Deyang 618307, China
Fan He: School of Economics and Management, Civil Aviation Flight University of China, Deyang 618307, China
Jiuhao Chen: Department of Adminstration, Civil Aviation Flight University of China, Deyang 618307, China
Chuanhui Liao: School of Economics and Management, Civil Aviation Flight University of China, Deyang 618307, China
Shengda Zhang: School of Economics and Management, Civil Aviation Flight University of China, Deyang 618307, China

Sustainability, 2022, vol. 14, issue 22, 1-9

Abstract: Using a hedging strategy to stabilize fuel price is very important for airline companies in order to reduce the cost of their main business. In this paper, we construct models for managing the risk of the hedging strategy. First, we use conditional value at risk (CVaR) to measure the risk of an airline company’s hedging strategy. Compared with the value at risk (VaR), CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transfer invariance. Therefore, CVaR is a consistent method of risk measurement. Second, time-varying state transition probability is introduced into our model in order to build a Markov Switching-GARCH (MS-GARCH). MS-GARCH takes dynamic changes of market state into account, a feature which has obvious advantages over the traditional constant state model. Additionally, we use a Markov chain Monte Carlo (MCMC) algorithm to estimate the parameters of MS-GARCH based on Gibbs sampling. We use fuel oil futures data from the Shanghai Futures Stock Exchange to implement and evaluate our model. In this paper, we empirically estimate the risk of airlines’ hedging strategy and draw the conclusion that our model is obviously effective in terms of the risk management of hedging, a use which has a certain guiding significance for reality.

Keywords: aviation fuel; MS-GARCH; hedge strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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