Entrepreneurship & Organization Management

Using CVaR and Markov Switching GARCH, an Airline Organization Manages the Menace Associated with Propellant Hedging

Abstract

Zhihong Chen*

In order to lower the cost of their core business, airline companies must use a hedging strategy to stabilise the price of fuel. In this study, we build models for controlling the risk associated with the hedging strategy. First, we quantify the risk associated with a company's hedging strategy using conditional value at risk (CVaR). CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transfer invariance when compared to the value at risk (VaR). CVaR is a reliable way to quantify risk as a result. Second, to create a Markov Switching-GARCH, time-varying state transition probability is added to our model (MS-GARCH). The dynamic changes in market state are taken into account by MS-GARCH, a feature that has clear advantages over the conventional constant state model. Furthermore, we apply a Markov chain Monte Carlo an approach based on Gibbs sampling to estimate the MS-parameters. GARCH's To apply and assess our approach, we use fuel oil futures data from the Shanghai Futures Stock Exchange. In this study, we empirically evaluate the risk associated with the hedging approach used by airlines and come to the conclusion that our model is clearly successful in terms of hedging risk management, a use that has some guiding relevance for reality.