A Dynamic Approach to Fuel Hedging with Reference to the 2020 International Maritime Organization Regulations

• Název práce v češtině: Dynamický přístup k hedgeování paliv s odkazem na nařízení Mezinárodní námořní organizace v roce 2020
• Název v anglickém jazyce: A Dynamic Approach to Fuel Hedging with Reference to the 2020 International Maritime Organization Regulations
• Akademický rok vypsání: 2018/2019
• Typ práce: bakalářská práce
• Jazyk práce: angličtina
• Ústav: Institut ekonomických studií (23-IES)
• Vedoucí / školitel: PhDr. František Čech, Ph.D.
• Řešitel: skrytý - zadáno vedoucím/školitelem
• Datum přihlášení: 10.05.2019
• Datum zadání: 10.05.2019
• Datum a čas obhajoby: 09.06.2020 09:00
• Datum odevzdání elektronické podoby: 07.05.2020
• Datum proběhlé obhajoby: 09.06.2020
• Oponenti: Mgr. Ing. Šarlota Smutná, M.Sc.

Seznam odborné literatury

Aielli, G. P. (2013). Dynamic Conditional Correlation: On Properties and Estimation. Journal of Business & Economic Statistics 31(3), 282–299.
Baruník, J. and L. Vácha (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics 34(1), 241–247.
Caporin, M. and M. McAleer (2014). Robust ranking of multivariate GARCH models by problem dimension. Computational Statistics & Data Analysis 76, 172–185.
Cappiello, L., R. F. Engle, and K. Sheppard (2006). Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns. Journal of Financial Econometrics 4(4), 537–572.
Chang, C.-L., M. McAleer, and R. Tansuchat (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics 33(5), 912–923.
Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics 20(3), 339–350.
Khalfaoui, R., M. Boutahar, and H. Boubaker (2015). Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics 49, 540–549.
Lim, S. H. and P. A. Turner (2016). Airline Fuel Hedging: Do Hedge Horizon and Contract Maturity Matter? Journal of the Transportation Research Forum 55(1), 29–49.
Pan, Z., Y. Wang, and L. Yang (2014). Hedging crude oil using refined product: A regime switching asymmetric DCC approach. Energy Economics 46(C), 472–484.
Tsay, R. S. (2010). Analysis of Financial Time Series. Wiley.

Předběžná náplň práce v anglickém jazyce

Research question and motivation
This thesis sheds light on the volatility dynamics of spot and futures fuel prices as the optimal cross-hedging instrument, following highly anticipated 2020 International Maritime Organization Regulations (IMO 2020).

IMO has decided to implement a global 0.5% sulphur cap (currently 3.5%) for shipping fuel, coming into force on January 1, 2020. It is widely regarded as one of the biggest challenges and the most impactful changes to the oil industry in the last three decades due to its global and instantaneous effect. In consequence, shipping, aviation, and energy firms are currently revisiting the hedging aspects on IMO 2020. The potential shortage of compliant fuel induces companies to find other alternatives to bridge the gap. As a matter of fact, fuel prices across the whole energy complex are set to be substantially affected, well beyond marine fuel. There will be significant run cuts at refineries. The primary replacement is generally expected to be middle distillates, either diesel or gasoil; while the gasoline market is going to be challenged. Some other refined products, such as heating oil, may also be temporarily used to blend with non-compliant bunker fuel.

As the uncertainty in the prices of energy commodities arises, their futures price dynamics is of broad and current interest to energy investors and policymakers, not to mention the intense study of the academia in recent times. The focus on portfolio formations to manage companies’ exposure to risk is important because it allows shielding investment in case of unpredictable swings in fuel prices, oil price shocks, capacity decline, or market turmoil as such. Hence, the optimality is associated with the least variability in returns. For that reason, price dynamics, evolution and the relationship between other commodities have major implications for entities that are subject to fuel price fluctuations. Multivariate generalized autoregressive conditional heteroskedasticity models (MGARCH) have long been used in such research on financial markets. Correspondingly, dynamic conditional correlations have been applied to capture commodity price volatility, and by extension, to mitigate price risk.

Contribution
According to oil analysts, the first stages of the sulphur cap will reverberate in the entire energy sector. The market is preparing for the transition and its overhaul is thus necessary.
The expected contribution is threefold. As far as I am concerned, even though there is a rich array of literature on petroleum spot and futures volatilities, the comovements between gasoil and RBOB (reformulated) gasoline have not been inspected till now. In order to examine the hedging effectiveness, numerous studies (e.g. Pan et al., 2014) also reported conventional gasoline rather than the reformulated one. Its role has been neglected, but it is largely used for fuel proxy hedging. Secondly, employing the seminal works of Engle (2002), Cappiello et al. (2006), and Aielli (2013), I will analyse the (a)symmetry in returns of petroleum-refined products, which is fundamental to risk management, investment, and regulatory policies. The use of Corrected Dynamic Conditional Correlation GARCH (cDCC-GARCH) and its asymmetric counterpart is yet to be explored under these conditions. This model should yield the best result in case of sudden and large changes in volatilities (Caporin and McAleer, 2014; Khalfaoui et al., 2012). The critical discussion of each method will be presented in terms of suitability, accounting for the models’ strengths and weaknesses. Eventually, considering the cross-correlation of respective spot and futures contracts, and thereby the time-dependent optimal hedge ratio, I propose a sound (dynamic) fuel hedging strategy in the times of unexpected price fluctuations in a sense of higher hedging effectiveness and larger risk reduction.

Methodology
Upon understanding the aspects of the MGARCH models and the parsimony of parametrization, the DCC-GARCH family are to be used to capture necessary correlations. Owing to the fact that the difference between commodities may also accumulate over time, it is crucial to consider observations over an adequately long period. This is to effectively evaluate the differences between individual models regarding their dynamic conditional correlation estimates. Considering the problems and remedies of the DCC-GARCH, albeit not all of them, I will use more tractable alternative in Corrected DCC of Aielli (2013). Furthermore, its asymmetric version (Glosten, Jagannathan and Runkle-GARCH; cDCC-GJR) as developed by Cappiello at al. (2006) will be employed for the purpose of the robustness check as well as the introduction of conditional asymmetries in variances. By virtue of this, I investigate the asymmetric properties of the three energy commodities returns.

Thereafter, the time-varying conditional covariance matrices estimated from the aforementioned models will be used in calculating the optimal portfolio design, i.e. weights and hedge ratios on petroleum-based product returns. The risk reduction effectiveness indicator and the hedge effectiveness index will also serve us as a criterion to identify the (best) hedging performance, depending on GARCH type models, naïve strategy and a rolling window OLS. Finally, in-sample and out-of-sample testing will be formally assessed with the latter being more significant because of people’s concerns about the future.

This approach will be critical in testing the following hypotheses.
1. As a result of a sharp rise and fall in prices, there is heterogeneity in returns of petroleum-refined products.
2. The rising correlations between gasoil, gasoline, and heating oil indicate a recessionary phase.
3. The variance-covariance structure estimated from the Asymmetric (Corrected) DCC-GARCH provides the superior hedging potential.
4. The best hedging performance can be achieved using gasoline and heating oil, implying the relevance of joint production and closer application.

Hedging evaluation techniques will be constructed based on the extent of interconnection between spot and futures prices. Fuel hedging, be it diesel fuel, bunker fuel, or jet fuel, typically consists of three futures contracts – RBOB Gasoline, ULSD (heating oil), and gasoil. The estimation of the models will be conducted using daily data covering the period from April 1, 2006, until preferably January 2020. The data will be obtained from the Intercontinental Exchange (ICE) and the U.S. Energy Information Administration (EIA).

Outline
1. Introduction
2. Theoretical background and literature overview
3. Methodology
4. Data description
5. Empirical analysis and discussion of results, comparison with previous findings
6. Conclusion and future direction for research