July 5, 2017
Understanding The “Mexico Deal”
So, you’ve heard about this large oil transaction that has the potential to move markets and wanted to know more? In the article below, I use this transaction as a means to delve in to the structure of the type of trading group that might participate in this deal. From there, I then take a deeper look at the transaction itself and the complex hedging decisions those that participate might face. With this information, you will learn where to look for any related trading opportunities.
We know that several years ago the Mexican government implemented an annual crude oil hedging program as part of their budget process. Their intent is to protect the oil revenue defined in their yearly budget through the purchase of put options.
Recently, knowledge of this deal has received more press and it is now widely anticipated as a transaction large enough to move markets. To understand the process that occurs from deal negotiation through deal closing, let’s first take a look at the structure of the trading desk.
Trading desk structure
To understand how large deals like this unfold, it’s helpful to understand the different roles on a large integrated trading desk, specifically the trader and the marketer roles.
You may have noticed that many of the large banks and integrated energy companies out there have what they call their “Trading and Marketing” group. That’s because they are actually two distinct functions that depend on each other to make money.
Trading and Marketing roles defined
The trading desk manages a portfolio of risk exposure, executing strategies to capture anticipated market moves. They are usually governed by Value at Risk (VAR), Volume, and Credit limits. VAR is a common measure of how much a portfolio might lose (gain) given normal market conditions for a given confidence interval (probability) and liquidation period. It is a measure of market risk.
When working inside a large institution where capital constraints like margin are monitored at the corporate level, it’s more relevant to assign VAR limits at the individual trader or desk-level rather than assign margin limits.
In addition to VAR limits, traders or trading groups are often assigned volume limits. These volume limits are often assigned the total portfolio level, the individual market level, or both. The rationale for volume limits is to prevent position size from impacting the liquidity-portion of the VAR calculation, among other things. It’s not an exact science, and in my career I have certainly run up against assigned volume limits before my VAR limits.
The introduction of central clearing has simplified portfolio credit risk in listed products, however, it is still an issue for non-listed products. There is still a vast world of over-the-counter (OTC) transactions with non-standard terms, non-standard durations, non-standard grades and variable volume requirements. These transactions are enabled by direct credit agreements negotiated between counter-parties in the deal. As a result, corporate or portfolio-level credit exposure limits by counterparty are established to ensure exposure to a particular counterparty doesn’t exceed collateral agreements.
The marketing desk in a trading business usually focuses on originating non-standard transactions directly with another counterparty. Typically a marketer within an energy trading group might be assigned a region of the US and work to build relationships with both producers and consumers within that region. For example, they may have an industrial client that is looking to buy 800 Mmbtu’s of natural gas delivered to their plant location via pipeline in southern Alabama. This is not the standard 10,000 Mmbtu exchange-traded natural gas future. Since the marketing role is to originate business directly with customers, versus take risk, the marketer will bring the terms of the deal to their internal trading group for them to quote a price.
There can be a lot of discourse between the trading and marketing groups regarding deal valuation. Marketers generally don’t manage market risk, they manage relationships. They are primarily measured on the amount and quality of the deals they close. Conversely, a trader is measured on the returns they generate from the portfolio they manage. Once a deal is closed, it is transferred to the trading desk to manage. Some groups establish a ‘transfer price’ at which the deal is moved into the trading ‘book’ from the marketing ‘book’ in an attempt to capture any value (a marketer’s negotiation skills) added to the sales/purchase price over and above the trader’s quoted price. Ultimately, the more deals the group as a whole gets a look at the more they are aware of what is going on in the market, which is valuable information.
It would be reasonable to assume that the opportunity to participate in the Mexican hedge deal originated through the marketing group and the relationships they have created over the years. It’s also reasonable to assume that these marketers work closely with their internal trading desks regarding the terms of the deal so that their traders can establish a market price at which they are willing to transact.
Why do I assume this? Deals like the Mexican oil hedge require a lot of contracts with specific negotiated terms (credit, margin, legal, etc). This requires a lot of time, which is something that traders on a desk don’t usually have. This is clearly a deal done directly between two counter-parties, contains non-standard terms (if it was a simple put option on WTI futures, it could be executed via the exchange) and its contract language would have to be carefully monitored and updated each year.
The benefit of being a Market-Maker
Market-making on a trading and marketing desk, as described above, refers to the internal process of the traders providing market prices to the group’s marketers on deal opportunities they originate from their customers. Once the marketer has obtained a price from their trading desk and established how long that price is good for (1 hour, 1 day, etc.) they submit them to the customer. Often times it is unknown how many other companies the customer is soliciting markets from. Because markets are submitted directly to the customer, they are not public which makes for a distorted feedback loop. You generally have no idea what prices your competition gave to the customer, but can only assume that if you didn’t win the deal, you did not provide the best price.
Over the years, I have worked with marketers and participated in pricing long-term, non-standard deals. In most cases the counterparty needs either variable volumes, obscure delivery points or an illiquid grade of product. The counterparty has often reached out to several companies to ensure they are getting competitive prices. This is why an active trading desk is needed as part of an overall group. Being active in the physical and financial markets every day gives traders the knowledge needed to understand market dynamics and the performance risk they are being asked to ‘price’.
One thing is for sure: the fewer market-makers and the less liquid the product being priced, the more opportunity there is for adding larger premiums to your markets.
It’s fairly common for markets to have large annual or seasonal transactions that occur due to regulatory requirements, hedging or procurement. Some of these transactions are governed by regulatory rules regarding the type of counterparty that can participate. These rules are typically related to the size and credit-worthiness of the participants. Limitations on the number of participants limits the amount of competition and increases price mark-ups (mark-downs).
Which brings me back to the annual hedge by the Mexican government, as the above speaks to the rumored ‘large fees’ made by those that qualify to make markets for this deal. I believe “large fees” relates to how wide one can make their market.
Due to the size of this deal, and the potential pay-outs, Mexico is interested in dealing with counter-parties that are well collateralized with the ability to pay out large sums in the future should their option go ‘in-the-money’. There is nothing worse than having a winning trade on your books only to have your counterparty go bankrupt or become unable to pay (as happened to many when Enron filed for bankruptcy).
Electricity Market Example
To illustrate that deals like this occur across all industries, let’s take an example from the electricity market that I am familiar with. The electricity market is regional with listed futures contracts for major pricing hubs within each region.
There are two main listed futures for each pricing hub: On-peak and Off-peak think high-sulfur and low-sulfur crude oil grades). In the eastern time-zone, On-peak contracts cover the 16 hours (7 AM-10 PM) on each week-day of the month. The Off-peak contract covers the 8 hours (10 PM-7 AM) on each week-day of the month and also the entire 24 hours for each weekend and holiday in a month. The volume for each of these contracts is, for the most part, 50 MW/hr multiplied by the number of On or Off-peak hours in each month.
An electricity marketer communicates with many different types of customers to originate a deal (large industrial users, builders of new generation, etc.) When a customer is looking to transact, they provide the terms of what they are looking for and the deadline for prices to be submitted. In this example, let’s say that the customer is a large industrial company that is growing and needs to purchase additional electricity to cover that growth. Their plant is located in Pennsylvania and they provide the following estimated usage curve indicating how much they are looking to buy each hour:
The customer has requested a fixed price quote (given this usage curve) for a 5 year term. The volume of the futures contracts traded in the market are for 50 MW/hr blocks, while the volume the customer needs per hour varies throughout the day. Hedging this deal using futures contracts will leave the trader with excess during some hours and shortages in others.
Just as demand varies by hour, so do prices (as illustrated in the graph below).
You can see that using the standardized futures contract as a hedge would leave the trading book with excess length during the hours of the day that prices tend to be the lowest, and with shortages with during the hours of the day that prices tend to be the highest.
The trader will factor this into their model when crafting their offer price.
Here are a few basic hedging scenarios that might take place if they win the deal and it ends up in the trading book (as a sale to the industrial customer):
- Buy a 50 MW block of on-peak futures (7 AM-10 PM, Mon-Fri) and stay short the off-peak hours (nights and weekends),
- Buy a 50 MW block of both On and Off-peak futures which makes the net position fairly flat during on-peak hours, but net long during off-peak hours,
- Do nothing – it’s a nice compliment to the trader’s overall bearish positioning and view that futures prices are headed lower
Of course, there are other options besides using futures contracts. This deal may be somewhat offsetting to an existing position already in the trading book (in which case the trader might have an incentive to offer a better price to the customer because it relieves some headaches and locks in some profit). The trader could also utilize a combination of options, however, options on electricity futures that settle against the hourly price averages are fairly expensive due to the higher volatility of hourly prices versus daily.
Other factors that affect hedge activity relate to corporate-imposed risk limits (volume and VAR limits). Some deals are large enough that they would cause the trading portfolio to exceed their risk limits, if not hedged immediately. Of course, you can see from the example deal above that it’s not always possible to hedge ALL of the risks immediately in some deals (like the variable hourly volumes and prices). However, if the size of the new deal is enough to put the portfolio over volume or VAR limits,action must be taken immediately using liquid futures. Post-mortem analysis will determine what net risk is left in the portfolio to manage (i.e. basis, location, quality, volume, etc.).
This is when a trader assesses their ‘net risk’. Ending up long basis (spread) as a result of hedging is still a trade. Would you buy that basis (go long) regardless? If not, sell basis.
Deconstructing the Mexican deal:
Using the knowledge of the overall dynamics reviewed above, let’s look at how that might apply to the Mexican deal.
We know that roughly 95% of the crude oil that Mexico sells to the US is Maya heavy crude for use by Gulf Coast refiners. Pemex (Mexico’s state-owned petroleum company) calculates their sales as a derivative of other market prices. Their current formulas are:
Since over 95% of Mexico’s crude oil sales to the US are of Maya crude, that’s the formula we will look at for this analysis. Maya’s value in the Gulf Coast is indexed to a basket of 3 crude oil grades and 1 fuel oil grade, plus a constant (K) commonly called the K-factor. This constant is updated monthly and posted on their website (found here) as seen below:
There are a lot of moving parts, each having different sets of price influences and drivers. Also interesting to note, WTI is not used directly in any of these formulas. This is what Pemex states on their website regarding their pricing formulas:
First, let’s use what we have learned so far to evaluate the deal that was done for 2017 (the deal runs annually from Dec 1 – Nov 30).
The term of the deal has historically been 12 months running from December to the following November. Using all of this information, it’s fairly straight-forward to pull together a back of the envelope analysis of the 2017 performance of $38.00 options against the market:
I pulled monthly posted prices (for US delivered Maya crude) from the EIA website for the beginning of 2017 and used the Pemex formula for the balance of the year. It doesn’t appear that any of these puts would be in the money. Given that market prices are only modestly above the strike, this is not great news for Mexico since they are net long oil. The premium they paid would be much easier to stomach if oil prices had moved much higher. Cashing in on their puts is not actually the goal here since this is basically catastrophe insurance. Any ‘gain’ derived from this insurance really just represents an overall loss of revenue on their total oil production.
With the Pemex formula and the futures curves for each of the 4 benchmarks, you can get a feel for 2018 Maya ‘futures’. What strike level, if any, will they consider for next year?
For reference, futures curves can be seen in the table below:
For those who are more ‘visual’, here are the futures curves as of July 5, 2017:
The unknown in the calculations above for future terms (besides movements in outright price) is the Pemex published “K-factor”. I used the posted K-factor for June 2017 (shown earlier) as a constant for the balance of the year and 2018. While Pemex is responsible for setting the K-factor, it seems to be loosely correlated to the LLS/Brent spread.
Analyzing the Risks to the Market Participants in the Annual Mexican Hedge Deal
A recent Bloomberg article shed a lot of light onto this transaction. One thing the Bloomberg author noted was that changes in bank regulations may be impacting post-deal activities:
Putting aside that a bank may have multiple trading groups and portfolios that ‘take the other side’ of any hedges they transact, I will focus on the market risk that a single portfolio involved in this deal might face rather than the corporate-level net portfolio.
We know that in the past the options purchased by the Mexican Government are primarily based on the underlying price of Maya, and to a lesser-extent, Brent. I will assume that would again be the case for 2018.
As already mentioned, we know that Maya’s price is derived as a percentage of WTS, High sulfur fuel oil (HSFO), LLS and Dated Brent prices plus the variable constant set by Pemex. This gives us the ability to understand the risks the seller of these options will need to lay-off once the deal is done.
Primary price exposure:
- USGC HSFO
- Dated Brent
Secondary price exposure created as a result of hedging (since it’s highly unlikely the seller can equally offset their exposure using Maya futures):
- WTI/Brent Spread
- LLS/Brent Spread
- Brent/Oman Spread
- USGC LSFO
- Dated Brent/Brent Futures
- Light/Heavy Crude Oil Spread
- WTS/Canadian Heavy Spread
For example, if risk were entirely laid-off using WTI futures the book is now exposed to any significant change in WTI’s relationship to Brent. Another risk the seller of the options might incur relates to the expiration differences between monthly and average price options (it’s been noted that the deal is comprised of a basket of Asian, or average-price options). To the extent that European or American options are purchased as a hedge, the option seller will still have to manage the difference between hedges using monthly strikes and the average of daily Index postings that are used to settle Asian options.
Looking at the market risks identified above, you get a sense of how complicated a hedge strategy could become. Any unexpected shift-change in the relationship of the hedge contract to the underlying products used to price Maya (i.e. WTI suddenly trading at a premium to Brent) could seriously impact the hedge effectiveness.
It’s unlikely that there is a deep, liquid market for over-the-counter Asian options on Mayan Crude. Therefore, hedging this deal will require the use of a mix of more liquid futures and options markets. Determining the optimal mix of products to use is an art and a science. The ‘art’ being any market bias regarding price direction and spreads. The ‘science’ being the use of statistical tools and models.
How would you decide to hedge this trade? Two statistical tools that are often used when evaluating effective hedge markets are ‘correlation’ and ‘r-square’. Correlation measures the strength and the direction of a linear relationship between variables. R-square measures the proportion of the fluctuation of one variable that is predicted from another variable(s).
Shown below are two simple correlation tables for the markets used in the Maya pricing formula (based on 2 different sets of historical price data):
In both time series, one thing that stands out is the high correlation between Maya and LLS oil price moves.
Since WTI and Brent are the two most liquid futures contracts, I went on to include historical WTI prices in the mix (even though WTI isn’t specifically included in the Maya price formula) and ran correlations again, using the same two historical time periods:
With the addition of WTI, we reveal a high correlation between WTI and WTS (which is a main component in the Maya price formula).
Price correlations provide useful insight. To get more specific however, the R-square coefficient is used to define the usefulness of those correlations. R-square is a measure of how much the variance of ‘y’, or in our case “Maya”, is explained by the model of continuous predictors “x” (in our case WTI, Brent, LLS, WTS, HSFO)
R-square outputs (using the same historical time series as presented in the correlation matrix) are shown below:
Each bar represents the historical price series used as well as which “index” variable is being compared to Maya. For example, the first blue bar on the far left is the R-square of Maya and LLS prices using historical prices from 2015 through 2017.
Notice the difference in the results of the two historical time series used. Specifically, the decline in the r-square of Maya vs HSFO (labeled R^2 HSFO above) in recent years.
This is obviously one of the problems with using historical time series data. The changing nature of spread relationships can be significant and render older data useless. Two years ago, heavy crude oil was pricing significantly below lighter grades, however, those relationships have changed with the OPEC cuts that started last year (which took the production of heavier grades off the market raising their price relative to lighter grades).
We could go on and on using various statistical models, but you get the idea. The point is to get a sense of how those involved in the deal may look to hedge. With both LLS and Brent showing the highest correlation and therefore, r-square values with respect to Maya, WTI may not be the hedge of choice. With WTI as one of the more depressed light grades in the market lately, using it as a hedge leaves the portfolio essentially “long” Brent and LLS (the result of selling puts on Maya crude) and short WTI.
Said another way, the trader who has hedged with WTI has now made a market call on the Brent/WTI spread. Selling put options (i.e. to the Mexican government) creates ‘length’ in a portfolio. Think of this length as being related to Brent and LLS (and also WTS and HSFO). Selling WTI futures to reduce
this length leaves the portfolio with a spread, namely long the Brent/WTI spread or long the LLS/WTI spread.
The knowledge of how such a large deal may be hedged can lead to trading opportunities. For example, if I believed the initial hedges might be placed in the most liquid markets such as WTI, I might expect the Brent/WTI spread to widen in response to heavier-than-normal WTI futures selling. Therefore, it might pay to go long that spread before hedging begins. The trader responsible for hedging this deal might also do the same hoping to pull some more profit out of the market from trading positions. You can extrapolate this concept to High vs Low sulfur fuel oil, Gulf coast vs New York harbor spreads, etc. My point is that outright price movement isn’t the only outcome here, and you might be well-served to look at how the various spread relationships are behaving.
Since the initial move in spreads after the OPEC cuts last year, the entire oil complex has remained closely linked. A break-out move in any of these spread relationships might be a signal that significant hedging volume has entered the market. Pay close attention. Anyone who has significant volume to sell in the market might hit those that are less liquid first to ensure they can get some sales off before the market senses what’s coming. Since selling pressure in WTI and Brent can pull the entire complex down with it, one trick traders use is to sell or go short some off the less obvious markets (WTS, HSFO, etc.) beforehand in hopes of capturing additional profit created by said hedge activity.
Just as spreads may widen, or prices may go lower when large selling pressure comes in to the market, remember that the counterparties that were directly involved in the deal may be left with significant spread risk in their book that will need to be managed. Unexpected changes in price relationships can be exacerbated in the market as a result.
Bottom-line, there is more to this deal than meets the eye. A simple expectation of lower prices due to hedging activity may not be the only market opportunity!
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