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A New Approach to Optimize Hedging

We recently released a Whitepaper with a New Approach to Optimize Hedging Costs. While this framework can be applied to any industry, we’ve demonstrated its utility specifically for Oil Refining Companies. The results from the framework are encouraging to an extent that it seems well worth exploring further – based on our joint studies with a couple of large oil refining companies.

The Problem:

Here is how we can broadly see the Hedging Problem faced by Oil Refining Companies:

  • Hedging is a tight-rope walk between maintaining margins, reducing costs and managing risks.
  • Most approaches fail to deliver consistent results. Naive Hedging (100%) is seldom a complete hedge due to basis risks. Optimal Hedging (based on Min Portfolio Risk) is rarely cheap with margin taking a hit.
  • Multiple approaches to get better at Optimal Hedging (from simple delta approach, to more complex models involving Multi-variate stochastic models with jump diffusion) often fail to deliver or are not understood by management well-enough to be completely accepted or relied upon.

The Bigger Problem:

  • Most managers consistently under-estimate the costs of Hedging – especially when they consider only the direct costs – Brokerage, Bid-Ask spreads, etc., which are generally between 0.1% – 0.4% of total costs.
  • Indirect Costs, which include Opportunity Cost of keeping idle Margin Capital (in case hedging results in losses) could be between 3% – 7% of total costs.
  • Opportunity costs of lost upside can typically be between 1% – 3% of the total costs according to some studies.
  • Companies generally spend between 4% – 10% of costs on Indirect Costs, which, even if partially salvaged, can boost margins significantly. 

A New Approach to Optimize Hedging

A New Approach:

  • The Whitepaper discusses an ERR-Driven Hedge Framework (ERR is Excess Risk Ratio, defined as the ratio between Expected Shortfall and VaR).
  • The approach delivers a Framework, which allows teams to be flexible in Hedging without compromising on Risk Management.
  • For the period of simulation (Jan 2013 – Mar 2014), the ERR-driven Hedge Framework gave better results as compared to any other Hedging approach.
  • Not only did the framework improve GRM (Gross Refinery Margin), it did so by reducing hedging costs significantly.

Methodology:

  • Price Data was taken from 1999, although positions were created from Jan 2013 onwards.
  • The data was loaded in RiskEdge, with 10,000 Monte-Carlo Simulations, Conditional EWMA Volatility (0.94), 95% confidence level and 1 day Holding Period.
  • The entire analysis was broken into 2 sections of 2013, and Q1-2014 for easier comparison.

You can read more about this White-paper by clicking on the button below and also download it from that page. We’ll be glad to have your feedback on this whitepaper, and hope you’ll find it useful.

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