Value of Optionality, Asymmetry &
Flexibility
A Practitioner’s Guide
Last update: December 2, 2019
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Table of Contents
Preface.................................................................................................................................................................4
Introduction ........................................................................................................................................................5
Benchmarking with Competition ....................................................................................................................6
Definitions ...........................................................................................................................................................7
Intrinsic Versus Extrinsic Value ........................................................................................................................8
What Is Optionality ............................................................................................................................................8
What Is Asymmetry ........................................................................................................................................ 10
Market Attributes, Market Structure, and Enablers .................................................................................. 11
Situations Where Optionality Occurs .......................................................................................................... 13
Identification .................................................................................................................................................... 14
Valuation .......................................................................................................................................................... 15
Method 1: Probability Weighted Model ................................................................................................. 16
Method 2: Binomial (Trinomial, Lattice) Model ..................................................................................... 18
Method 3: Black Scholes model and Kirk’s approximation .................................................................. 21
Method 4: Monte Carlo Simulation Model ............................................................................................. 24
Method 5: Stochastic Optimization Model ............................................................................................ 30
Method 6: Machine Learning and Predictive Price Modeling.............................................................. 32
Method 7: Experience Based Volatility Probability Analysis ................................................................ 33
Summary table of pros and cons and potential applications of various methods ........................... 35
Software Tools for Evaluation of Options .................................................................................................. 36
Integration with Opportunity Evaluation .................................................................................................... 37
Integration with Scenario Analysis........................................................................................................... 37
Presentation of Results .............................................................................................................................. 38
Key OE tools integration ........................................................................................................................... 39
Integrating Extrinsic Value into OE .............................................................................................................. 40
Accountability.............................................................................................................................................. 41
Implementation & Operationalization Plan (I&OP) ............................................................................... 41
Implementation & Operationalization ..................................................................................................... 42
Plan & Stewardship .................................................................................................................................... 42
Fundamental shifts ..................................................................................................................................... 43
Lessons Learned and Challenges ................................................................................................................. 43
Appendix 1: Optionality & Flexibility Identification Aid ............................................................................. 44
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Appendix 2: Enabler & Operationalization Checklist ................................................................................ 45
Appendix 3: Tools & Contacts ...................................................................................................................... 47
Appendix 4: Subject Matter Experts ............................................................................................................ 48
Appendix 5: Examples .................................................................................................................................... 49
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Preface
Imagine that, by some twist of fate, you become the ruler of an oil-rich state.
A crash in the oil price has left a hole in its budget. You are forced to consider
selling the kingdom’s assets. Among them is a mothballed oilfield in a remote
part of the country—so remote that it costs $90 to retrieve each barrel of oil.
That is above the prevailing price of $70 a barrel. Even so, you are advised to
try to sell a license to operate the field.
Who would buy such a license? It is valuable only if a barrel of oil sells for at
least $90. Yet there is always value in a right—if it carries no obligation. The
greater the chance that prices will rise above $90, the more the license can be
sold for. The price will be higher if the license is for a long period. Crucially,
the price also depends on how changeable the oil price is. The more volatile,
the likelier it is that it will hit a level where it is profitable to restart production.
Volatility is normally something to fear. People prefer a stable income to an
erratic one, for instance, and they feel the same way about their wealth. In
this regard, the jumpiness of stock prices is a source of discomfort. But where
you have rights without obligations—options, in other words—things are
different. Here, volatility is welcome.
Look closely, and the hypothetical oil license has the features of a “call”
option, a particular kind of financial contract. A call option is the right to buy
an asset—a barrel of oil or a basket of stocks, say—at a specified price (the
strike price) on or before a specified maturity date. The owner of a call option
profits if the price of the underlying asset goes above the strike price. The
owner is not obliged to buy at the strike price; she will do so only if it is in her
interests. Anyone who buys the oilfield license is essentially buying a call
option on the oil price. If it goes above $90 the buyer makes a profit; if it stays
below $90 for as long the license is valid, the option expires worthless.
Putting a value on options is a fiddly business. The key ingredients in the BlackScholes model, the industry formula, are time, volatility and the gap between
the asset’s strike price and its current price. A small gap is more likely to be
closed than a large one, so options with strike prices close to prevailing prices
cost more. Call options with a strike price above the prevailing price are said
to be “out of the money” and are cheaper. The more violently prices fluctuate,
the more chance there is that an out-of-the-money option, like the
hypothetical oil license, becomes a winning lottery ticket at some point before
it matures.
Excerpted from “When you have options, volatility is your friend,” The Economist, May 11, 2019
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Introduction
The purpose of this white paper is to guide the identification and valuation of optionality in opportunities.
While many people have an intuitive sense for optionality, as a corporation we are still increasing our
awareness of and ability to quantify the value of flexibility and how it can be captured. Historically, this
value was not routinely and consistently included in project economics but it can be a very significant
source of value for many EM assets. Unfortunately, we often traded it away without realizing the potential
embedded value. Management has put an increased focus on the identification, valuation and capture of
optionality. Fundamentally, this requires a change in our thought process. We must be able to value
flexibility relative to its cost, and accept a range of outcomes rather than just success or failure.
This approach focuses on market-driven uncertainty, primarily expressed through prices, and applies
across the entire value chain, from the Upstream to the Downstream and Chemicals. It has applications
in a variety of scenarios, from exploration acreage bidding to clean products marketing. All investing,
divesting, and commercial opportunities are in the scope. However, uncertainty from other sources, such
as technical feasibility and geological factors, are excluded from this paper.
This guidance integrates with the existing Opportunity Evaluation (OE) framework. The value of
optionality is not just a sensitivity but should be considered and if material evaluated for including in base
economics.
The audience for this approach is anyone involved in economic analysis where flexibility could have
significant value that would impact the decision. This applies to analysts, advisors, and managers involved
in preparing and evaluating the analysis. This paper was written to guide and inform decision framers at
multiple levels in the organization.
By the end of this paper you should be able to gain the high level knowledge about optionality, make
connections to the optionality examples included in the appendix to help recognize the optionality in the
opportunities being evaluated, understand the method and tools to be used to evaluate the optionality,
and most importantly, know the right person to seek advice and support from. After evaluating the
optionality, it’s also important to come up with the correct procedure to ensure capture of the optionality
through the decision making process, which is the ultimate goal. Moreover, this document should help
develop the mindset that regards volatility as an opportunity and not just a risk.
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This paper is not just about the optionality valuation, it incorporates expectations to be met for including
the associated (extrinsic) value in the economics. In essence, as shown in the above flow chart, after
identification and valuation, we only integrate into the OE assessment if we also have a clear plan on
operationalization, stewardship and accountability for capturing the value.
Benchmarking with Competition
The concepts explained in this paper are not new and have been around for decades. While from timeto-time we have successfully used them in ExxonMobil, there was no broad consistent understanding
nor application. When we benchmarked we found competition have embraced what is often referred to
as Decision Analysis, and started the journey years ago. Their acceptance of these tools was not an
overnight process; the companies said it took years (if not decades) for this way of thinking to really
permeate the culture.
Examples of what we found while benchmarking:







Competition adopted advanced analytics approaches enabling real option / asymmetry
valuations
IOCs structurally address decision support and quantitative modeling competency
‘Decision Support’ often independent from business, though close relationship (e.g.
‘embedded’)
Advanced Analytics / Stochastic modeling widely accepted; standard in Trading decision support
Cumulative Distribution Function (aka ‘S-Curve’) used by competition in Management
Presentations
Structural investment in the organizational capability (people, processes, systems, tools, etc.)
Training on key concepts across the organization, including management / leadership
The bottom line is that, when benchmarked against competition, EM underinvested in the processes,
methods and tools and we are lagging the industry.
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Definitions
Intrinsic Value – This is the fundamental value of an asset or opportunity given its proposed characteristics
today evaluated without consideration of any future flexibility (optionality).
Extrinsic Value - This is the value above intrinsic value that comes from flexibility (i.e. embedded
optionality) in the face of volatility. This value may be embedded in assets and therefore not recognized
at first glance. Extrinsic value is the value that arises from the flexibility to take different actions depending
on movement (volatility) in external variables (often future price changes).
Volatility – This is the tendency of an observable but uncontrolled variable such as price to fluctuate. The
price movements may be entirely random, drifting along a trend, mean-reverting toward an equilibrium
balance, or may follow some other statistical distribution that can be measured and forecasted.
Asymmetry – The probabilities of a random variable, such as price, to go up or go down are different. This
also occurs when a distribution of outcomes (e.g. NPV) is skewed to one side or the other rather than
evenly (normally) distributed. The asymmetry may be naturally occurring (e.g. as a result of market
structure) or due to the exercise of optionality or flexibility to capture upside and avoid the low side.
Optionality – The flexibility to act differently because of the availability of alternative actions you can
take, given the volatility of variables which affect your decision.
Option Cost (Premium) – The cost to acquire flexibility.
Real Option – A choice made available with respect to business/investment opportunities. It is referred
to as “real” because it typically references projects involving a tangible asset instead of a financial
instrument, though financial instruments (derivatives) may enable capturing the value from real options
in some cases.
Market Attribute – An element of the market’s structure that creates or affects option value.
Market Structure – The combination of organizational and other attributes of a market that result in
competitive characteristics and an outcome which market participants transact in, or against. Market
structure defines the nature of competition and the pricing mode; it influences the behavior of individuals
and firms in the market.
Enabler – What you need to have to capture the value associated with the real option, a value lever.
Expected Value – The value in a probabilistic distribution that represents the mean (average) return of all
possible outcomes.
P50 Value – The value in a probabilistic distribution that represents the median return of all possible
outcomes, this is the point where 50% of the possible outcomes are below this point, and 50% of the
outcomes are above. In most cases, this is not the same as the expected value.
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Intrinsic Versus Extrinsic Value
The long-term price sets (Opportunity Evaluation Scenarios) issued by Planning are typically annual
average, sometimes quarterly average, projections that are used for projects which often last for decades.
However, the market prices exhibit volatility on a much shorter time horizon, changing daily if not by the
minute. There may also be seasonal effects that an annual average will not capture. Once the asset is
operational, there is often value, additional to that identified by the average price set, which can be
extracted if you have flexibility in how the asset is utilized in response to changes in market conditions.
Going forward, EM will include the additional optionality (extrinsic) value in the base project and other
decision economics where material and we have a plan to capture it. Conversely, we may be considering
alternative opportunities that lead to a decrease in optionality, and if so, this should also be included in
decision economics.
By definition, the intrinsic value can be cashed in by immediate transaction of sales and purchases at
currently-quoted prices. For example, you could lock in future sales based on today’s forward price curve
and remove all risk, thereby eliminating volatility from the returns. However the intrinsic value of, for
example, a gas contract does not consider value that accrues in the future operation (optimization) of the
contract. On the other hand, extrinsic value results from the market price changes that will occur in the
future. For example, one can model future price evolution based on past volatility and then evaluate the
contract over market representative volatile price set rather than just one trendline, incorporating how
we exercise the flexibility (option) as prices move. The extrinsic value is considered the value arising from
flexibility.
What Is Optionality
Optionality is the right but not the obligation to take an action. Many people are familiar with financial
options which give the holder the right to buy or sell a stock at a fixed price (strike price) in a set period
of time.
There are several important variables which define the value of a financial option on an equity. Of
particular importance are time to expiration and stock price volatility. The longer before one must act on
the option (i.e. flexibility) and the higher the volatility, the more valuable the option. For a financial option
to have value, the underlying asset must have some volatility. Consider the equivalent variables associated
with a reserve development option as described in the preface (this could just as readily been illustrated
with an exploration example). Real options are similar but instead of applying to stocks, they allow you to
take actions in the real world. Similarly, the longer the period between acquisition of exploration right
and the development of the field and the more volatile the resource prices, the higher the optionality
value.
Options represent flexibility. If a project has few or no options, management is constrained in its ability
to add value. Examples of options in a project are the ability to expand or contract the scope of a project
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(capturing upside or limiting downside); the ability to accelerate a decision (or a whole project) or defer
the commitment; and the ability to establish the timing and amount of investment or production (i.e.,
cash flow).
Clearly a portion of the value associated with a project is the ability to respond to changing conditions or
new knowledge, i.e., flexibility. The value of these options is always available within the project waiting
to be recognized and captured. However identifying and managing the options is how you create and
realize value.
There are many real options we can identify in our business, for example:
 An option to phase development allows you to make a small investment to gather more
information before making the full commitment (e.g. drilling exploration wells to prove up a
resource).
 An expansion option provides the future ability to grow at a lower cost, for example building a
larger pipeline today at a small incremental cost versus adding a second pipeline in the future or
use multiple small LNG liquefaction trains (that can be added) design vs mega train(s).
 An abandonment option allows you to stop work before the project is completed and recover any
residual value if conditions deteriorate.
 A deferral option lets you postpone the project until market conditions change, for example,
drilled but uncompleted wells (DUCs).
 A switching option gives you the ability to change inputs or outputs based on conditions, such as
chemical crackers that can run on a variety of feedstock.
 A diversion option to sell the commodity or products in markets with higher prices than the target
market, for example the ability to divert LNG cargoes to more profitable markets.
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What Is Asymmetry
Asymmetry occurs when the distribution of our returns is no longer symmetric, or normally distributed.
This can occur because the shape of the distribution changes and is skewed in our favor (or against us),
because we can “cut off” the unfavorable part of a normal distribution by actions we can take, or because
counterparties have optionality in a deal that could result in negative asymmetric returns for us. Note that
some returns are almost by definition asymmetric because the uncertainty inputs are asymmetric.
Where an opportunity with symmetrical returns would have an equal probability to deliver a higher or
lower return versus its mean, positive-asymmetric opportunities have a mean return (Expected Value)
above the median (P50). Asymmetry may be naturally-occurring (inherent) in an opportunity,
or we may own or decide to acquire enablers that allow us to minimize negative outcomes or capitalize
only when positive outcomes present themselves, as illustrate in the below chart:
In order to extract value from asymmetry, we need to be able to identify the Market Attributes and
Enables that cause its presence, value it appropriately, and be confident we can consistently exercise the
enablers (flexibility / the option) to capture the value.
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For example, if we control a pipeline that links two markets whose prices fluctuate, we can ship product
from the lower-priced market to the more expensive market when the differential is large enough to cover
our costs (transportation, working capital, etc.) and not ship when the differential is small. The ability to
capture this arbitrage opportunity by only shipping when the differential is large creates an asymmetric
return profile for us.
The mean differential between origin and destination pricing (the difference between the markets) might
be $2/bbl and after logistics costs $1/bbl remains, which represents the intrinsic value we anticipate.
However if the differential is volatile and we have flexibility to ship more when the differential is large,
this creates an asymmetric arbitrage opportunity for us with extrinsic value. Valuing the pipeline only
using the mean differential would not reflect all the potential value. Note that the distribution of
differentials (from the time-series) may be symmetrical or asymmetrical, but it is our ability to exercise
flexibility that creates asymmetrical returns.
When talking about the “shape” of a distribution or volatility it's important to be clear between inputs
(such as prices, margins, differentials, etc.) and the distribution of economic outcomes that you get (your
return on the investment/value) because your actions (calling on the enablers / flexibility) may change
the distribution with intent to skew (further) positively, this is what arbitrage through optionality is about.
Market Attributes, Market Structure, and Enablers
Market attributes contribute to market structures and are the fundamental drivers of volatility. You will
need a precise definition of the market you’re considering and then undertake a thorough examination of
it. You should consider the basic forces acting on the market such as customer and supplier power as well
as the availability of substitutes and the potential for new competitors. These factors all affect the
fundamental balance of supply and demand. You may also wish to examine the cost structure of the
market through a market clearing mechanism, especially if marginal players are a substantial part of the
market. Longer term assessments should be made of the industry’s overall capital cycle (e.g. invest →
oversupply → shrink → repeat) and constraints on it. Time-series data should be examined to look for
cyclicality which may correlate to seasons or other temporal factors. And exogenous or idiosyncratic event
risk should also be considered – for example, unforeseen government limitations on production. It’s also
important to consider what can cause shifts in the market structure and if they are likely; this may change
future volatility and thus impact the value of an option.
Enablers take a variety of forms and we describe some examples here. EM often runs assets at maximum
utilization but having spare capacity, especially on the margin, allows us to take advantage of volatility by
increasing output during favorable conditions. Market connectivity through logistics assets allows us to
capture arbitrage profits when adjacent markets are temporarily dislocated. It may be worth building an
amount of logistics overcapacity for markets where we observe volatility to allow us to capture value.
While ownership of these assets is not always required, we can’t rely on third-parties to make capacity
available in times of volatility as they will prefer to capture this value themselves. Flexibility provided in
our contracts and commercial arrangements is another enabler that allows us to react based on market
conditions rather than being permanently locked in.
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A key enabler is allocation of a resource (e.g. human resource, system, etc.) to monitor the market and
exercise the optionality. For example, EM has employed Optimizers and Refinery Coordination groups to
exercise optionality in refinery Production Planning, by signaling changes to the production plans/feed
slates (leveraging PIMS models that contain known inherent optionality in the manufacturing assets).
As part of your assessment of the enablers, you should consider whether we believe the source of the
value is temporary or structural and understand other relevant facts about the opportunity. For example,
how long the window will be open compared to the time it will take for us to capture it? Are we able to
move fast enough? In some cases we may need to develop our general organizational capability to
exercise flexibility. This could take the form of streamlined endorsement processes increasing our reaction
time, for example. You’ll need to identify signposts that alert us when the opportunity is open if it only
occurs transiently. This may take some analysis to separate the signal from the noise, especially in very
volatile markets. Think of it as follows, when we plan for and value a capital project, we set up the project
team to execute the project and develop a full plan for the operating organization that will run it. When
we include value for optionality, we also need to have worked through how we capture it consistently:
what organization, process, systems, services, activities, resources, competencies and skills, etc. Then
ultimately it must be clear how we steward the value capture and who we hold accountable.
The combination of volatile market attributes (whether they are fundamentally asymmetric or more
normally distributed) in conjunction with unique enablers is what gives rise to asymmetry that we want
to capture. However there are other questions that have to be answered in considering one of these
opportunities. What is our unique source of competitive advantage that allows us to capture this
asymmetry, sustainably? Can anyone copy our strategy with contracts or derivatives, or are our enablers
unique assets that only we have access to? Are other parties able to observe what we’re doing? Will they
react to our move in some way, either by trying replicate what we’re doing or working to prevent it? How
will these reactions alter our ability to capture the asymmetry (think game theory)? These are some of
the questions to consider when evaluating opportunities with optionality.
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Situations Where Optionality Occurs
There are a number of situations where optionality is present at EM. This is not an exhaustive list but
contains some examples and can aid the identification of Optionality & Flexibility:
Area
Category
Enablers
Capital
investments
Phased development (vs. all at
once)
Ability to cancel when new/updated information presents itself
Design not limiting future
expansion capability
Initial outlay to retain capability for lower cost future expansion
Pre-invest for future
expansion
Low cost to "do now" (pre-invest) during project execution for future
capability/optionality
Residual interest
Retain exposure to future margins to sustain option
Portfolio effect
Reduced matrix / optimization flexibility
JVs
Protection from downside if partner interest/business direction diverge; EM sees
more/less value in JV vs partner
Crude or Product pricing
Backward looking pricing option or capture of forward market structure
(Backwardation/contango)
Volume flexibility
Produce non-ratable - pricing vs volume profile
Divestments
Commercial
structures
Ability to adjust volume based on our value in use
Volume deferral based on view of future vs current price
Production sharing
contracts/agreements
Tax
Trading
Origination
Time arbitrage
Differential arbitrage
Geo arbitrage
Quality arbitrage
Execution /
optimization
Dynamic Forward Trading
Destination flexibility
Ability to access and secure rights to limited logistics
Limit or increase exposure to crude price and production volume
Cost basis management
Tax defensibly - Leveraging low tax affiliates
Creation of incremental long and/or short positions that create value by allowing to
capture pricing volatility , producing asymmetric returns
Rights to storage (logistics capability) or drawdown to respectively capture
contango/backwardated market structure, usually of flat price, sometimes seasonal
crack spread
Contango on crude quality differential time arb
Ability to move (or not) molecules (feed/product) to different market under different
pricing
Exploit inherent giveaway in Hydrocarbon batches (e.g. NY harbor blend hub, Crude
Blending)
Volatility in forward derivative market
Vessel/pipeline destination options (or not)
Ability to move molecules (or not) to different market - Crude oil destination
restriction (D-free vs D-restricted)
Sourcing / supply flexibility
Matrix of supply options to purchase from and deliver to
Quality
Ability to switch between sales channel (supply/wholesale/retail) and grade mix
Access to and flexibility to substitute components and capability to blend products to
specific market quality
Ability to nominate crude between fields with differing quality under same price (e.g.
Al Shaheen)
Capability and Flexibility to process wide spectrum of feed - not limited by site
processing or product quality constraints
Access and capability to capture crude quality differential not priced into premium
Second Order Pricing Effects /
Logistic Power balance
Oversupply in market may result in lower prices in the market (converse is true),
Logistics outlets may negate impact
Pricing negotiation leverage for alternate disposition capability
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In addition for ease of reference this table has been included as Appendix 1: Optionality & Flexibility
Identification Aid. The appendix includes a link to a detailed spreadsheet.
Identification
The first step in the process is to identify the explicit or implicit flexibility in the (commercial and/or
investment) opportunities, and understand how to take advantage of them to capture asymmetric
returns. Implicit flexibility comes naturally with the opportunity – we don’t have do anything extra to have
the option. Explicit flexibility requires us to take additional actions in the planning phase to have the option
– for example there could be additional capital required or processes changes. Ideally, there is a structured
process set up to review opportunities for flexibility but you should also be open to identifying this during
the normal course of business if an idea is generated organically. When considering an opportunity there
are a number of questions to ask:

What is the intrinsic value of the asset/opportunity using trend-line assumptions?

What decision would you make under static (no flexibility) assumptions?

What are the market attributes driving the volatility?
o

Are we taking a position on future volatility?
Is there naturally-occurring asymmetry or do we need to create/enhance it to capture the value
from it?

What flexibility is needed to capture the volatility or create the asymmetric returns?
o

Think of enablers you would need: assets, business processes, contract terms, etc.
What is the cost (think option premium) for the enablers
o
This is the initial payment plus any costs to maintain the flexibility

Is the flexibility you need binary (off/on, one-time) or continuous?

Is value derived from integration with other EM assets (portfolio effect)?
o
Do we need hard assets to capture it or can we do it synthetically (e.g. with paper trades
or derivatives)?

Are the enablers unique to EM or things anybody can purchase?
o
Especially important to consider on synthetic positions (that is to say, do we have a
competitive advantage over other traders or competitors? Could a hedge fund copy our
strategy?)
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Valuation
Correct valuation must be detailed enough to capture the relevant drivers and be validated by the subject
matter experts but explainable to the general audience and decision-makers who may not be familiar with
all of the math. It is acknowledged that the link between the markets and business drivers for flexibility
(identification) and the actual valuation, require different skill sets and both often are technical within
their own disciplines. Effectively managing the translation between the business and quantitative
valuation disciplines is therefore an important task and often requires a third skill set.
The valuation must be applied consistently across similar projects and included in the base economics for
optimal capital allocation purposes. That said, we recognize that any approach to modeling the
complicated market situation or reality with a few parameters and simplified physical rules will never be
perfectly right. However if we choose the methods and parameters carefully and make deliberate
assumptions to test our thinking process, we may gain useful insight from the modeling:
“All models are wrong; some are useful.” – George Box
Identifying the main drivers of value and possible outcomes is most important. As with any analysis, clearly
laying out the assumptions on which it is based is critical to ensuring decision quality. For example,
determining whether to calculate the volatility of an input over the last year or the last ten years may
make a big difference in your results – you’ll need to rely on business experts to frame the case properly
before doing any math (otherwise, garbage in = garbage out will still apply). And of course, you should be
watchful for cognitive bias as you structure your analysis.
Given the number of situations where optionality is present in opportunities ranging from capital projects
to commercial structure to trading activities, it’s important to understand the methods and tools to be
used to evaluate the optionality embedded in the potential opportunities. Due to nature of different
opportunities, the optionality evaluation methods and tools could be different as well. In the following
section, we will discuss the commonly used methods, with the guidance on the potential application and
constraints. In comparison with the traditional methods of project evaluation, optionality evaluation is
considerably more complex and may require a higher degree of mathematical and statistical
understanding. We do not expect, and it is not required for, the general audience to understand every
detail of the method. However, by providing various illustrative examples on application of different
methods, we expect the audience to understand high level principles and make connection to potential
opportunities being pursed.
In the following section we explain various valuation methods. We use ‘price’ as the example uncertainty,
but this is simply to facilitate the example.
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Method 1: Probability Weighted Model
Before we start explaining the method, let’s describe a simple business situation to use as an example
throughout.
Assuming we are evaluating an opportunity which will require investment of $240M (Capex). Based on
the current assumption on technical and market conditions, the total future net operating cash flow (OCF)
discounted at 8% to today’s value (NPVt0) will be $200M. We are aware that market condition could
change between now and next year – with probability p, say 56%, the NPVt1 of the OCF could rise to
$270M; with probability 1-p, the NPVt1 of the OCF could fall to $150M, as illustrated in the following
diagram:
If management has to make a decision today, we value the opportunity at $-240M (NPV Capex) + $200M
(NPV OCF) = -$40M; the likely decision is not to develop the project given the negative Project NPV.
If management has the option (the right, but not the obligation) to defer their decision to invest by one
year, by when we will have better understanding of how the market condition change, the decision could
be different. If the market condition turns out to be unfavorable (44% chance), instead of incurring a loss
of $150M (NPVt1 of the OCF) -$240M (NPV Capex) =-$90M, we have the right to not develop the project,
hence the value of the opportunity is 0. If the market turns out to be favorable (56% chance), we will
exercise the right to develop the project, the NPV t1 (-$240M + $270M [NPVt1 of the OCF]) = $30M or
$28M ($30M discounted by 8%, i.e. today’s money). Therefore, the total value of the project, considering
the probability of different market conditions, would be weighted average of both situations, i.e., 44%*0
+ 56% * 28 = $16M. Compared to the situation of developing the project right way, the optionality value
of waiting for one year = $16M – (-$40M) = $56M.
In this example, we see that the investment opportunity has negative NPV value as of today without an
option; the optionality to delay one year results in a positive NPV, a logical decision is to delay the project
to capture the optionality value. Moreover, even if the project has a positive NPV if executed today, it
does not necessarily mean that it should be developed now. Delaying the development of a project can
improve its value, which should be considered in evaluation of opportunities. Identifying and valuing the
flexibility therefore is important where a traditional valuation approach may lead to forego the option
value.
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When to use
 simple option
 straightforward probabilities/outcomes
 screening analysis
Benefits
 simple, mechanics transparent
What you need
 empirical estimate of the potential upside and
downside of the value and the probability
associated with each value (outcome)
Drawbacks / Pitfalls
 probability assignment potentially subjective
The above chart is a simple example to illustrate the concept of optionality – by having the option to wait
for one year to get better understanding of the market and its volatility and we can reduce risk stemming
from the market conditions. This method is the basis for the following methods and can be extended to
multiple branches and multiple time periods.
For the probability weighted method, we need knowledge, based on past experience, of the probability
of market going up or down; and the associated NPV value when market goes up or goes down. We only
considered the one-time option of making a decision to develop the project or not. In other situations,
instead of one time period, we may have multiple times to decide whether we develop the project or not.
We may not have information about the actual probability and actual value of the project, but have some
information about the potential volatility, characterized by the standard deviation, of the cash flow. For
such situation, we will evaluate the option through the Binomial Tree Model as explained below.
17
Method 2: Binomial (Trinomial, Lattice) Model
Binomial option valuation models looks like a decision tree in which the possible values of the basic
property change depending on timing of the option's maturity, i.e., when the option to delay or develop
the project is gone. This model tracks the movement of asset prices or project value as a binomial process
in which the value can move in two possible directions, i.e. up or down. The changes in the value are
marked with 𝑢 and 𝑑 factors, where 𝑢 > 1 and 𝑑 <1, and follows the multiplicative binomial process in
discrete time.
The Binomial model shows that as the uncertainty clears in the future, management can make appropriate
decisions at that time by comparing the expected payoff with the investment cost.
For the same business example above, the uncertainty/volatility of the cash flow is characterized by the
following parameters:
INVESTMENT OPPORTUNITY
PARAMETERS
EXAMPLE
Present value of project's Free Cash Flow
S0
$200M
Capital expenditure required to acquire project assets
X
$240M
Length of time that decision may be deferred
T
5 years
Time value of money (risk free rate)
r
8%*
Riskiness of project assets
𝜎
30%
* 8% here is assumed to be the risk free rate, not the standard discount rate that coincidently is 8% in 2019.
Initial point 𝑆0 in the Binomial model shows the current value of the underlying asset. The probability of
the asset’s value going up in each period in the future is indicated as p. Conversely, the probability of the
asset’s value going down is expressed with 1-p. In the first step (node) of the binomial model the asset’s
value can move in two directions, up to 𝑆0 𝑢 [u = up] or down to 𝑆0 𝑑 [𝑑 = down]. The next (second) step
results in three possible asset values (𝑆0 𝑢2, 𝑆0 𝑢𝑑, 𝑆0 𝑑2), the third time step in four (𝑆0 𝑢3, 𝑆0 𝑢2 𝑑,
𝑆0 𝑢𝑑2 , 𝑆0 𝑑3), etc. The last step in the Binomial model indicates the range of possible asset values at the
end of the option’s life, or the latest time that the decision may be deferred.
Up and down factors, 𝑢 and 𝑑, depend on the volatility, , of the underlying asset and can be expressed
as follows:
𝑢 = 𝑒 𝜎√∆𝑡 = 𝑒 0.30×√1 = 1.35
1
1
𝑑= =
= 0.74
𝑢 1.35
In every time period there is a probability p that asset value will grow with percentage 𝑢, and conversely
the probability (1-p) that the assets will fall with percentage 𝑑:
𝑝=
𝑒 𝑟×∆𝑡 −𝑑
𝑢−𝑑
=
𝑒 0.08×1 −0.74
1.35−0.74
= 0.56
p is called the risk-neutral probability based on the no-arbitrage principle. This principle states that,
regardless of whether the asset value goes up or goes down, a portfolio of owning the asset and taking a
risk-free loan will have the same payoff as owning the option itself.
18
With these data we can create a Binomial tree and calculate the asset values on each node of the Binomial
tree, using one-year time interval. At each node or time period, management has an option to invest in
the project at that point or delay its development and wait until the next time period. Note that all the
numbers in the tree are in million: the upper numbers on the Binominal tree present expected future
asset values at each node and bottom numbers indicate option values.
T=0
T=1
T=2
T=3
T=4
T=5
896
𝑆0 𝑢5
664
656
𝑆0 𝑢4
492
𝑆0 𝑢
364
𝑆0
270
289
𝑢2
270
114
148
200
81
143
270
𝑆0 𝑢3 𝑑 2
200
30
𝑆0 𝑢 𝑑
45
148
𝑆0
110
𝑆0 𝑑
252
𝑢3 d
2 2
𝑆0 𝑑
25
364
𝑢2 𝑑
𝑆0 𝑢𝑑
𝑆0
70
𝑆0
492
𝑆0 𝑢4 d
𝑆0
183
𝑆0 𝑢
200
444
3
16
8
2
𝑆0 𝑢2 𝑑 3
110
𝑆0 𝑢𝑑
4
148
𝑢𝑑 2
81
0
3
0
𝑆0 𝑑 3
0
81
𝑆0 𝑢𝑑 4
60
𝑆0 𝑑 4
0
0
45
𝑆0 𝑑 5
0
When preparing a Binomial tree it is necessary to present the value of expected cash flows arising from
investing in the project, 𝑆0 , multiply with the up factor u and down factor d to obtain 𝑆0 𝑢 and 𝑆0 𝑑. Moving
to the right, with the same procedure it is necessary to calculate the expected value of cash flows for
every node of the Binomial tree until the last step. For example:
 At the end of the first year: there are two branches, 𝑆0 𝑢 = $200𝑀 × 1.35 = $270𝑀, 𝑆0 𝑑 =
$200𝑀 × 0.74 = $148𝑀.
 At the end of the second year: there are 3 branches, with expected value from the investment
ranging from $110𝑀 to $364𝑀
 At the end of the fifth year: there are 6 branches, with expected value from the investment
ranging from $45𝑀 to $896𝑀
19
Once we have calculated the value of expected future cash flows at each node of the Binomial tree which
are shown in the form of the above values, we can calculate the value or price of the options (below italic
values in the scheme). The option values are calculated from the extreme right values in the schematic
according to the initial values to the left ("backward induction"). On each node there is the possibility of
investing or deferral of investment to further.
 At node 𝑆0 𝑢5 expected asset value is $896𝑀. If the option is exercised in the fifth year, and
investment cost of developing the project is $240𝑀, then net asset value of the investment is:
$896𝑀 − $240𝑀 = $656𝑀. But if we delay realization of option and wait until next time period,
the revenues will be zero because option expires (becomes worthless) at the end of the fifth year.
Hence, at node 𝑆0 𝑢5 the option value is $656𝑀 and the rational decision will be not to wait but
invest in the development of the project.
 Expected asset value at node 𝑆0 𝑢2 𝑑3 is $148𝑀 but the option value at this node is zero because
the investment of $240𝑀 is resulting in a net loss of $92𝑀. In these circumstances, the rational
decision is not to develop the project.
 Stepping one year back at the intermediate node 𝑆0 𝑢4 we can calculate the expected asset value
for keeping the option open as discounted weighted average of potential future option value. If
the option is exercised and we invest $240𝑀 in developing the project, the net asset value would
be $424𝑀($664𝑀 − $240𝑀). However, holding the options open until the next period (fifth
year) gives possibility of realizing higher asset value ($444𝑀 = (0.56 × $656𝑀 + 0.44 ×
$252)/1.08).Therefore, it is better to continue to wait, rather than to exercise the option to
invest at the 4th period.
 Repeat the same procedure backwards until the time 0, where the option of delaying the
investment up to 5 years has a value of $70𝑀. Therefore, the rational decision is to defer the
investment decision until year 5.
When to use
 Have data on volatility of project value (return)
 Evaluating American options (exercise any time)
 When Geometric Brownian Motion applies
 Distribution is skewed (drift up versus down)
Benefits
 Transparent
 Can be visualized to be part of a discussion
What you need
 Measurable volatility of project value (return)
Drawbacks / Pitfalls
 Potentially laborious (e.g. long time to expiry)
Based on the same principles the Black Scholes model was developed in 1970s to calculate the option
value using a closed formula, assuming the decision time frame is continuous instead versus discrete in
the Binomial Tree.
20
Method 3: Black Scholes model and Kirk’s approximation
The Binomial Tree method divides the time to expiry of an option into multiple time periods and calculates
the asset value at each discrete period to derive the option value, this is called discretization in
mathematics. Instead of discretizing the time periods, the Black Scholes model is based on continuoustime value using the “finite difference method” and the assumption of pricing the underlying asset
following the Geometric Brownian Motion (described below). That leads to a closed-formula analytical
solution to calculate the value of option as following:
𝑋
𝐶 = 𝑆0 × 𝑁(𝑑1 ) − 𝑟𝑇 × 𝑁(𝑑2 )
𝑒
𝑠0
1
ln ( ) + (𝑟 + 𝜎 2 ) × 𝑇
𝑋
2
𝑑1 =
𝜎√𝑇
𝑑2 = 𝑑1 − 𝜎 × √𝑇
In the above equations:
𝐶: value of call option;
𝑑1 , 𝑑2 : deviation from the expected value of the normal distribution,
𝑁(𝑑1 ) and 𝑁(𝑑2 ): the probability that a standardized, normally distributed random variable will be less
than or equal to 𝑑1 and 𝑑2 ;
𝜎: volatility of the asset value;
ln: natural logarithm e = 2.71828, base of natural logarithm.
In theory, if the discretization of the binomial model is very fine toward infinity, or the time period
between the binomial tree nodes is infinitesimal, the option value derived from the Binomial Tree model
will be similar to that derived from Black Scholes model which is based on a continuous stochastic
differential equation.
The Black Scholes model was initially developed for assessing the value of call options on a stock, whereas
the price of the stock follows lognormal distribution and the change of the price follows random walk
process (Geometric Brownian Motion). The theory of financial options, within which Black Scholes model
is developed for valuing financial options, can be easily adapted to be applied to real assets, since the
investment opportunity, to a certain degree, is similar to a call option because it allows the right, but not
the obligation to invest. The analogy of the parameters between real options and financial options are
illustrated in the following table:
INVESTMENT OPPORTUNITY
Present value of project's Free Cash Flow
PARAMETERS
S0
CALL OPTIONS
Stock price
EXAMPLE
$200M
Capital expenditure required to acquire project assets
Length of time that decision may be deferred
Time value of money (discount rate)
Riskiness of project assets
X
T
r
Exercise price
Time to expiration
Risk-free interest rate
Standard deviation
$240M
5 years
8%
30%
𝜎
21
After identifying the S0, X, T, r and  variables, the option value can be calculated using the above formula
as following:
𝑑1 =
𝑠
1
200
1
ln( 0)+(𝑟+ 𝜎 2 )×𝑇 ln( )+(0.08+ 0.302 )×5
𝑋
2
𝜎 √𝑇
=
240
2
0.30√5
= 0.66
𝑑2 = 𝑑1 − 𝜎 × √𝑇 = 0.66 − 0.30 × √5 = −0.01
𝐶 = 𝑆0 × 𝑁(𝑑1 ) −
𝑋
240
× 𝑁(𝑑2 ) = 200 × 𝑁(0.66) − 0.08×5 × 𝑁(−0.01) = $69𝑀
𝑟𝑇
𝑒
𝑒
As you can see, the option value from the Black Scholes model is close to the value calculated from the
binomial model; and if the discretized time period of binomial tree is infinitesimal, the value from binomial
model will converge to the value from Black Scholes model.
A modification or extension of the Black Scholes model is Kirk’s approximation. It is used when evaluating
a ‘spread option’ where the payoff is based on the difference in price between two underlying assets, with
the volatility of the underlying prices as well as the correlation between these two prices are known. Kirk’s
approximation is expressed through the following formula:
𝐶𝐾 = 𝑒 −𝑟𝑡 × [𝑋1𝑁(𝑑𝐾,1 ) − (𝑋2 + 𝐾) × 𝑁(𝑑𝐾,2 )]
Where 𝑁() denotes the standard normal cumulative distribution function, and 𝑑𝐾,1 and 𝑑𝐾,2 are given by:
𝑋1
1
ln (
) + 𝜎𝐾2 𝑇
𝑋2
+
𝐾
2
𝑑𝐾,1 =
𝜎𝐾 √𝑇
𝑑𝐾,2 = 𝑑𝐾,1 − 𝜎𝐾 √𝑇
𝜎𝐾 = √𝜎12 − 2
𝑋2
𝑋2 2 2
𝜌𝜎1 𝜎2 + (
) 𝜎
𝑋2 + 𝐾
𝑋2 + 𝐾 2
𝜎1 , 𝜎2 : volatility of the underlying prices;
𝜌 : correlation coefficient between the return of the underlying prices;
𝐾 : the strike price of the spread option, i.e. the call spread option would be in the money if the spread is
higher than 𝐾.
This has been used to evaluate the option value of transportation or storage given the optionality is driven
by the spread between the prices in two markets, or between the prices at two different times.
Although Black Scholes and Kirk’s approximation are built on a rigorous mathematical and statistical
foundation, and it’s easy to calculate the option value through the closed-form formula, it’s difficult to
explain the formula in layman’s terms. It cannot incorporate compound options, operational constraints
or multi-stage investment opportunities. The volatility (𝜎1 , 𝜎2 ) and the correlation coefficient (𝜌 )
between different stocks can be found in the financial markets or in the historical data. However, when
evaluating real options for investment opportunities, determining the value of these parameters is more
difficult. Consequently modeling the risk factors that underlie an investment opportunity, such as price,
and then calculating the asset value considering the volatility of the risk factors (through simulation) often
enables better understanding of the uncertainty and real option value.
22
When to use
 When you have data project value volatility
 Evaluating an European option
 American through layering options
 When Geometric Brownian Motion applies
 Distribution is skewed (drift up versus down)
 When evaluating ‘liquid market’ spreads (kirk)
Benefits
 Simple application
 Little compute power required
What you need
 Data on volatility of project value (return) and
correlation
Drawbacks / Pitfalls
 Not transparent
 Applies simplification of markets (e.g. efficient)
Black Scholes and Kirk’s approximiation (i.e. an extension of Black Scholes) are forms of ‘analytical’ or
‘closed-form-models’, as opposed to the other (probabilistic) methods described here. There are more
closed-form-models not described there than can be used if a situation warrants it.
23
Method 4: Monte Carlo Simulation Model
As mentioned earlier, both the Binomial and Black Scholes models require the estimation of the volatility
(standard deviation 𝜎) of the project value (return), which is difficult to obtain, compared to financial
products such as stock prices. But knowing one of the main “risk factors” behind the project value being
prices, we can model the price through Monte Carlo simulation and then calculate the project values using
multiple realizations of price. This will generate a distribution of the project values (returns), which, on
one hand, provides additional insight about risk and uncertainty of the project value due to volatility of
the price; and on the other hands, allows to build in operational constraints / flexibility in the economic
model to understand the value of optionality and impact of complicated commercial structures on the
project value. On top of that, simulation can be readily applied to address situations that need to address
multiple factors, in addition to price, such as geological setting, that may have impact on the project or
option value. Therefore, simulation is a useful alternative to binomial or traditional finite difference
methods (basis for Black Scholes model) and has many advantages as a framework for valuing and
optimally exercising real options, due to its nature of being simple, transparent, and flexible.
For a Monte Carlo Simulation, the first step is to model the risk factor, or price movement (if that is the
main risk), through a stochastic process which is set up to represent the dynamics of the underlying price
as close as possible. We will explain, in high level, the main stochastic processes used to model the price
below (there are more variants, or expansions, of the following processes to represent different
characteristic of the price movement which we will not cover here):
Geometric Brownian Motion (GBM):
One simple process to model price volatility is GBM which is described by the following continuous
Stochastic Differential Equation (SDE):
𝑑𝑆
= 𝜇 × 𝑑𝑡 + 𝜎 × 𝑑𝑧
𝑆
Where:
𝑆: price of commodity;
𝑑𝑆
: differential return or change of the price;
𝑆
𝜇: the drift term (upward or downward trend);
𝜎: the volatility of price S(t);
𝑑𝑧: Wiener process, which denote a random walk process in which you just randomly sample a
realization out of standard normal distribution at every time step.
In other words, the GBM represents the return or change of price with two elements: the first term is the
trend and the second term is the random or stochastic process. In theory, the distribution of stochastic
term can be of any type (normal distribution, uniform distribution, Poisson, etc.). Due to the fact that
normal distributions can be characterized by limited number of parameters (mean and standard
deviation) with a closed-form formula, normal distributions are frequently used for the stochastic term.
𝑑𝑆
Given above equation, the return or change of the price ( 𝑆 ) follows normal distribution and the price
itself follows lognormal distribution – this is the base assumption of the price model for the Black Scholes
model to calculate the option value.
To simulate the price movement, the above continuous differential equation is discretized as following,
(after applying ito’s lemma for the natural logarithmetic transformation on the price 𝑆):
24
ln(𝑆𝑡+∆𝑡 )−ln(𝑆𝑡 ) =[𝜇 − 𝜎 2 /2]∆𝑡 + 𝜎√∆𝑡𝜀
Where:
𝑆𝑡 : price at time 𝑡;
∆𝑡: time step of price path;
𝑆𝑡+∆𝑡 : the price at time 𝑡 + ∆𝑡;
𝜇 : rate of “drift”;
𝜎: standard deviation or volatility of the random process (measurement of noise)
𝜀: random number with N(0,1) distribution
Parameters to be calibrated from historical data are the drift term 𝜇 and standard deviation 𝜎 , which will
be discussed more later.
Mean Reversion Model:
Another stochastic process to describe the dynamics of price movement is called Mean Reversion Model,
by which the assumption is that, for example, the commodity price has a tendency to return to some
“normal” or equilibrium level (i.e. the market clearing price), although the prices will occasionally spike to
very high or low prices (i.e. market reality of disruptions, etc.). The stochastic process must be designed
in a way that this return-to-normal-level characteristic is represented. This characteristic has more
economic logic for commodity prices than the GBM described above. Mathematically the stochastic
process used to characterize this dynamics is called Mean Reversion Model represented by the following
stochastic differential equation (SDE):
𝑑𝑆
= 𝜂 × (𝑀 − 𝑆)𝑑𝑡 + 𝜎 × 𝑑𝑧
𝑆
Where:
𝜂: the speed (strength) of reversion to the defined mean, or mean reversion rate,
𝑀: reverted mean, or long-run equilibrium level that the prices tend to the revert to.
The other terms in above equation have the same meaning as in the GBM process.
The difference between a mean-reverting process and the GBM is the drift term: in GBM, the drift term
is fixed at 𝜇; in mean-reverting process, the drift is positive if the current price level 𝑆 is lower than the
equilibrium level 𝑀, and negative if 𝑆is greater than the equilibrium level 𝑀 . In others words, the
equilibrium level 𝑀 attracts the prices to its direction. The analogy is with a spring: the more distant prices
are from the equilibrium level, the higher is the tendency to revert back the level 𝑀.
To simulate the price movement, the above continuous stochastic differential equation is discretized as
follows (after applying ito’s lemma for the natural logarithmetic transformation on the price 𝑆):
ln(𝑆𝑡+∆𝑡 )−ln(𝑆𝑡 ) =[ 𝜂𝑀𝑅 (𝑀 − 𝑙𝑛𝑆𝑡 )−𝜎 2 /2]∆𝑡 + 𝜎√∆𝑡𝜀
Where:
𝜂𝑀𝑅 :rate of mean reversion;
𝑀: mean value that the log of the price reverts to;
The other terms have the same meaning as in the GBM process.
25
Parameters to be calibrated from historical data are the mean reversion rate 𝜂𝑀𝑅 , the reverted mean 𝑀,
and the volatility 𝜎.
Similar to Kirk’s approximation which models two underlying correlated prices to derive the option value
from spread volatility, in both GBM and Mean Reversion model, we can model multiple prices or assets
such that they are correlated with each other. To do that, another parameter 𝜌, the correlation coefficient
is needed to enter the simulation to generate correlated price movement.
In addition to the Mean Reversion Model described above, there are many variants to the process to
account for additional characteristics of the price movement. One variant is the Mean Reversion with
Jump Model which incorporates volatilities that are not explained through normal volatility but go beyond
the normal diffusion process, e.g., high natural gas prices in winter months (seasonality) correlated to
weather and primary commodity use. Another modeling variant is Two Factor Mean Reversion Model to
address near term and long term uncertainties separately.
In order to model the prices through the stochastic process described above, some key questions to be
addressed are as following:
How to estimate the parameters?
There are different parameters in the stochastic differential equations describing for example natural gas
prices on different hubs, oil and other commodity prices. When the past is a good predictor of the future,
those parameters can be calibrated from historical data sets. In the course of such calibration, we still
need to make fundamentals based judgments calls, for example: what historic time window that best
describes our view of the future; where there fundamental market shifts or unique events that make a
data set less accurate, etc. As a rule of thumb, the time period needs to be long enough to provide a
statistically sufficient sample size, but must not reach too far back into the past as the business
environment or market fundaments may have changed. Selecting and calibrating the data is an effort that
requires both the quantitative and markets experts.
In some situations, the parameters can be set to reflect changes over time consistent with fundamental
analysis. For example, if over time fundamental marginal price layers change due to changes in industry
production capacity or demand, the reverted mean can changed over time to reflect the market
fundamental change. Similarly, if there is indication of different volatility in different period, we can
incorporate those in the model as well.
How to choose the right stochastic process?
One question that’s often asked is which stochastic process, Geometric Brownian Motion or Mean
Reversion, should be used to model for example the commodity prices? The answer is “it depends” – it
depends on what you have observed historically and what you want to model the future.
26
Take the example of historical crude price movement from year 1996 to recent year of 2015:
From the period of 1998 to 2006, it seems that the price was following an upward drift with some
randomness, which can be characterized by Geometric Brownian Motion stochastic process. However,
from the period of 2007 to 2015, the prices went high and low, but there was a tendency for the price to
move towards a balancing range, which is characterized well by Mean Reversion stochastic process. Then
the question is what’s going to happen in the future? We have fundamentals based views resulting from
our internal processes (Energy Outlook (EO), Industry Gas Outlook (IGO), Liquids Supply Outlook (LSO),
Spreads & Differentials, etc.) and market insights. These fundamentals need to be reflected in the choice
of method as well as the parameters to model the process.
Parameter implications from modelling (market) scenarios?
Scenario planning aims to define your critical uncertainties and develop plausible scenarios in order to
discuss the impacts and the responses to each one of them for an opportunity, project or strategy under
consideration (reference S-BORE). By its very nature it tries to identify what drives a certain way the
world, region or local situation can develop (for example big shifts in society, economics, technology and
politics). These fundamental shifts will have impact on the uncertainties, and as a consequence on the
input parameters (assumptions). For this very reason, each scenario may have has its own assumptions
and therefore its own set of parameters such as volatility and revered mean, causing different valuations
for real options in opportunities, projects and strategies.
The stochastic process is fully complementary with and can be integrated with scenario analysis:
 Identify the assumption that go with each scenario, reflecting the uncertainty fundamentals
(which may be represented by different period of history);
 Calibrate the parameters using the specific period of historical data, and make appropriate
adjustment based on proprietary knowledge or market insight;
 Generate the uncertainty models for each distinct scenarios using parameters consistent with the
distinct scenario.
The following table and graph illustrates the example of five different business environments, the period
of historical data chosen to calibrate the parameters, and the adjustment made based on expert
knowledge or proprietary insight.
27
In addition, we can use different parameters for different modeling period (combination of scenarios) to
reflect the change of business environment.
The process of simulation doesn’t take away the need for experts and decision makers to apply expert
insight and judgment. However, by mimicking the history (modeling the future) based on intelligent
assumptions, we can support the decision making process by framing the risk and uncertainty embedded
in the potential opportunities, hence facilitate informed decision making.
After we model the price paths using any of the above processes, we can (utilizing for example excel
macros) run the price paths through the economic model to generate the distribution of the project value.
Compared to the conventional economic analysis using single trend line prices, the Monte Carlo
simulation approach provides additional insight in the following:

The distribution of the project value through multiple realizations of price paths indicates the risk
and uncertainty of the project’s cash flows due to uncertainty about future price movement, and
support management decision based on the portfolio strategy – we may want to have a mix of
high-risk high return projects and low-risk low-return projects.

In the economic model we can build in any operational options or constraints as appropriate. For
example, to evaluate the economics of a LNG liquefaction plant which liquefies gas from HH
(Henry Hub, a gas hub in US)) and sells to other market(NBP in Europe, JKM in Asia), we can model
the prices at different hubs and calculate the economics of the project assuming the following
options: i) we will sell the LNG to the market/hub which has higher price to capture the uplift; ii)
we will shut down the plant if the LNG selling price (in the best market) is lower than the total
variable cost, including gas purchase price at HH and other logistic costs (shipping, regas., etc.),
to prevent the downside.
28
The following charts illustrate the application of the method:
The cumulative distribution functions on the lower left shows that the risk and uncertainty associated
with the opportunity. Furthermore, the shifting of the blue curve to the right indicates the value of
optionality of being able to sell the LNG to the higher market.
Compared to the analytical solution of Black Scholes model, the simulation method is more “versatile” in
evaluating different investment opportunities and optionality due to its capability to include various
operational constraints/flexibilities and complex commercial structures in the evaluation. However, the
Monte Carlo simulation method assumes the optionality applied at different stages are independent from
each other. In reality, the decision made earlier could have impact on the options in the future, such as
storage model, flexible contract model. In those situation, we have to apply global optimization
considering the uncertainty, which is Stochastic Optimization Model.
When to use
 Real options in investment opportunities
 Options mimic our behavior
 Bespoke options
 Create insight in the Risk and Uncertainties
 You do not have value (return) volatility data
 Longer term flexibility (capital investments)
 Market fundamentals change over time
 No market data, but statistical assumptions can
be generated
Benefits
 Transparent, not a black box (closed formula)
 Assumptions can be tailored to markets
 Can deal with complex real options
 Enables evaluation of Risk and Uncertainty
What you need
 Sound fundamentals market understanding
 Ability to translate input risk and uncertainties
into meaningful statistical parameters
Drawbacks / Pitfalls
 Requires compute capacity for speed and/or
complex problems
 Its usefulness for complex options can drive
complex models
29
Method 5: Stochastic Optimization Model
As mentioned above, simulations works well in evaluation of optionality where options are independent
from each other, i.e., the decision to shut in a LNG facility in one month does not impact the decision to
liquefy gas and sell LNG to the Asian market in the next month. In many real option situations this does
not hold.
For example, gas contracts have long term volume restrictions (min and max of Daily Contract Quantity,
Annual Contract Volume, Annual take-or-pay obligation). A decision to take less this period will have
impact on the decision for the next period. This means that whenever gas is lifted from the contract, the
flexibility within the remaining operation period of the contract is reduced. Similarly for storage, the
decision to inject more volume at this period will prevent from injection in the next period, even the
injection opportunity in the next period is better. This dependency of future flexibility on today’s decision
requires the stochastic optimization method which optimizes the actions at each period taking into
consideration the uncertainty in the future period such that on expected value basis, the objective
function is met.
Rather than generating multiple separate Monte Carlo price realizations, Stochastic Optimization is based
on the generation of scenario trees, where each node represents the state of the world. Different
scenarios share a common history before they branch apart. Each single path (from root to end)
represents a price scenario. Each path of the scenario tree represents a joint discrete evolution of
uncertainty, which altogether result in a multidimensional scenario tree.
The tree structure appears similar to the Binomial Tree method above, but the calculation of the option
values are different. In the Binomial Tree, there is only one decision to be made across the whole period;
whereas in the stochastic optimization tree, there are decisions to be made at each time period to
optimize to the objective.
A simple summary of the tree structure which is explained in more detail below:
 Create a discrete tree that matches the price behavior as observed in all the generated price
scenarios
 The contract duration is split into relevant time periods which form the ‘nodes’ of the tree (i.e.
where the tree branches)
 Each node is associated with a price, derived from the price scenarios
 At each node a decision needs to be made on the quantity to be lifted under the contract and
each node branches to multiple equal probability price scenarios every time
 Contract constraints (like max/min DCQ, ACQ, ToP) are fed into the optimization model and need
to be met exactly after the optimization is done (volume allocation)
 Solving a decision tree is a linear programming example whereby volumes are allocated such that
the average of all profits at the end of the tree is maximized, subject to the constraints above
 The profit results at the very end of the tree are equal in probability, and form a cumulative
probability distribution function
The following chart illustrates the process of the Stochastic Optimization which, similar to Monte Carlo
simulation, starts from the model of the price path, followed by setting up the price scenarios trees with
decision variables and constraints at each node to be solved by linear programming solver. Note that the
30
decisions at each node from the optimization solver do not give one optimum objective value, but a
distribution of possible profits, as indicated in the cumulative distribution curve on the lower left hand
side, such that the average or expected value from the distribution is optimized. The difference between
the expected value of the distribution considering the uncertainty of future price movement and the value
that can be locked in based on today’s market view of future price is the extrinsic value, or the optionality
value of not locking in the decisions today.
A noticeable problem in stochastic tree based optimization is the so-called “curse of dimensionality”,
which is the exponential growth of the scenario tree with the number of branching time steps throughout
the planning horizon. Obviously, it is not possible to branch the tree in a daily or even hourly granularity,
which will make the numbers of scenarios too large to solve. Usually, the optimization can be solved in
reasonable time with up to 8-10 branches.
Another optimization method considering price uncertainty is stochastic dynamic programming which
uses a time/state space which discretizes both time and state (e.g., the inventory state in the case of
storage is discretized to 0%, 10%, 20%...etc.). When the asset becomes more complicated, the time/state
space becomes so large that it will run into computational issues.
When to use
 When exercising real options restricts or
changes our future options and choices that
require optimization
What you need
 Sound fundamentals market understanding
 Clarity on the dependencies and logical
optimization decision protocols
Benefits
 Can deal with dependencies in future choices
Drawbacks / Pitfalls
 Often criticized as “black box” due to nonintuitive result
 Requires linear programming
 Can require very material compute power
31
Method 6: Machine Learning and Predictive Price Modeling
Lastly, another method to improve price forecast to support business decisions for opportunities such as
MoGas arbitrage and price forecasting for refinery feed/product slate optimization is through machine
learning algorithms. It basically identifies the “external” variables that correlates with price movement,
extract the relationships and trends between those contributing variables and price from historical data
(“machine learning”), and then apply that relationship for future price prediction, using all available data.
The difference between machine learning and the stochastic process is that machine learning tries to
identify the “external” variables, as well as features from historical time-series data, that impact the
future prices using data-driven statistical analysis approach; whereas stochastic process tries to calibrate
the statistical characteristics of price from its own historical data (or other inputs).
There are various modeling algorithms readily available from open sources in various script languages.
The key is to identify the right “external” variables, or the “predictors”, for the price and uncover the
“hidden insights” through learning and training from historical data, to derive the relationship that can be
used to predict future prices. To characterize uncertainty of the forecast, there are two branches of
methods: i) parametric methods which add a confidence band normally based on the assumption of
normal distribution and the parameter of standard deviation; ii) non-parametric methods which builds
prior and posterior probability distribution based on data and Bayesian theorem without assumption of
the distribution and parameters.
The following chart illustrates the process of machine learning – training, testing and forecasting:
After extracting the relationship through learning and testing, we can apply that relationship to forecast
and guide the trading decisions depending on the certainty bounds of the forecast.
When to use
 Estimate / Forecast of near-term price change
to support trading decisions
What you need
 Reliable historical data, especially the
variables/”predictors” that have impact on the
price
Benefits
 Allows to uncover “hidden insight” though
learning from historical data
 Various modeling algorithms readily available
from open sources
Drawbacks / Pitfalls
 Constraint to trends and relationships observed
in historical data
 Critical to include the right “predictor” and
heavily rely on historical data
32
Method 7: Experience Based Volatility Probability Analysis
Experienced people, based on fundamental analysis, insights and experience, can identify the typical
‘modes’ a market will be in; resulting in a limited set to describe all likely market ‘modes’. Each mode
comes with an operating choice modeled to generate a value for the Mode. These ‘modes’ are assigned a
probability of occurrence and by multiplying by the assigned mode values it generates a risk adjusted
value:
•
•
•
•
•
Each mode may use any of the 6 prior methods to help with evaluation
The valuation involves assessing the probability (e.g. how long a mode is present) , i.e. how long
and how many times will we exercise the flexibility in the contract / facility and how much value
will be in the exercise.
Important to be explicit about the variables/assumptions, which may be “hidden” in the other
methods
Allows you to identify “key spreads” driving the value, the “killer variables” destroying the value,
asymmetric upside enhancing the value, and ultimately manage the risk and uncertainty
associated with these factors which has the most impact on the value (easy to be visualized
through the tornado chart)
Probabilities for each mode could change from year to year
A mode has the characteristics of a scenario, but the difference is that a mode reflects a ‘state’ (e.g.
applicable market clearing mechanism) a market may be in from time to time, whereas a scenario is a long
term fundamentals view.
An example of Baton Rouge crude optionality – coke morphology: the team found a $50M/yr flexibility
incentive for envelope to cover all cases, in addition to a $60M/yr incentive to move to Opportunity Priceset slate. The volatility was assessed by looking at historical margins. The 2013-2017 margin of the best
heavy crude in any month is $2.7/B better than the average of 10 likely crudes, however it is unlikely we
can always capture the best of the best so the $2.7/B was discounted to $2/B for likelihood of capture:
33
The “experienced team” assigned probability to 10 different crude slates developed as perturbations of
the long term crude strategy cases:
BASE
20%
10%
2017 Actual
15%
10%
14%
8%
Lead Case
6+ Yr Crude
Typical 2017 Study - Lead
Slate
Case
8%
7%
High TAN
4%
4%
No Rail - Base
6+ Yr Crude
Study - Lead
Case Hard
6+ Yr Crude
Study - Lead
Case HardEST
6+ Yr Crude
Study - Easy
High TAN Base
High TAN Hard
High TAN Easy
No Rail - Base No Rail - Hard No Rail - Easy
Base Slate
Lights
90
121
121
121
121
151
151
151
166
166
166
Dom Mediums - Sour
165
110
110
110
110
89
89
89
112
112
112
Dom Mediums - Sweet
140
161
161
161
161
124
124
124
131
131
131
AG Crude
28
40
40
40
40
40
80
47
47
30
47
47
40
20
50
40
80
15
15
35
15
35
55
15
35
23
55
23
23
13.5
2.0
4.5
1.0
1.9
5.7
26.8
Variable Slate
Kearl
Access Western Blend
Cold Lake
Castilla
DCO
SRR (Good Resid, e.g. Achinsk)
SRR (Bad Resid, e.g. Basrah SRR)
Basrah Heavy
Ostra
Import resid
Joliet resid
Baytown rock
Total Fuels MVP
Total Fuels API
Total Fuels CCR
25
30
10
22
23
35
23
23
3.0
1.0
7.1
24.1
1.4
6.0
25.2
1.6
5.7
25.8
3.0
1.0
1.7
5.7
25.4
20
23
30
30
2.0
4.0
40
25
1.0
7.3
23.5
23
30
2.3
3.0
1.0
1.9
5.6
25.5
1.4
5.8
24.4
1.0
7.0
22.9
1.3
6.4
26.2
55
8.7
0.9
7.6
23.8
Three cases (summing to 22% probability) lie in the current Operating Envelope (OE), one 20% case is
already covered in the Opportunity Price Set slate (6+ year Study lead case), leaving 58% outside the
Operating Envelope and Opportunity Price Set slate, across the spectrum pictured below. If the project
opened the OE to cover all the cases there would be an additional 58% x $2/B x 140kbd x (1-22% Tax) =
$50M/yr of credits for Optionality:
When to use
 Smaller dollar investments / opportunities that
do not justify investment in a full quantitative
methods
What you need
 Sound fundamentals market understanding
 Clarity on the dependencies and logical
optimization decision protocols
Benefits
 Simple, does not require advanced analytics
Drawbacks / Pitfalls
 Relies heavily on experts and preventing bias
34
Summary table of pros and cons and potential applications of various methods
Method
Probability
Weighted
Model
Binomial Tree
(Trinomial,
Lattice) Model
Input Assumptions
Potential project
values and the
probability
Expected project value
and volatility – like
Pros
 Simple, easy to
understand
Cons
 Purely experience based
Potential Applications
Screening analysis of
potential opportunities
 Simple, Versatile to
include operational
constraints in the model
 Approximate continuous asset
value and time to expiry of
opportunity with limited
discretization;
 Burdensome to set up the tree;
 Difficult to estimate the volatility
of project value
Evaluation of real options
of Investment
opportunities
Black Scholes
Model / Kirk’s
approximation
Expected project value
and volatility (same as
Binomial Tree)
 Close-form formula; Easy
to calculate
Financial options;
simple spread options
Simulation
Model
Statistical
characteristics of
prices: volatility, drift,
reverted mean, mean
reversion rate –
usually calibrated form
historical data with
adjustment made
based on experience
and market insight
Same as Simulation
Model; with objective
functions, operational
constraints and
flexibility laid out
clearly
 Well documented and
applied by industry;
 Can test different
scenarios based in
market insight;
 Can handle complex
operational constraints
and commercial
structures
 Formula different to understand;
 European-style option;
 cannot handle compound options
and complex
constraints/flexibility
 Complex theory and mathematics
behind the model;
 Need to get used to the
distribution results for risk and
uncertainty;
 Requires adjustment using expert
judgment; won’t simplify the
decision making process, but
promote more informed decision
making
 Often criticized as “black box”;
 Computational intensive while
considering the uncertainty;
 Output often difficult to interpret
for those uncomfortable with
probabilistic and statistical results
Predictive Price
Modeling
(Machine
Learning)
Appropriate external
variables as
predictors; period of
historical data for
learning and training
 Often criticized as “black box”;
 Critical to include the right
“predictors” for the training and
heavily relied on historical data
availability;
 Assumption of no fundamental
shift from history
Near term arbitrage
opportunity; refinery
feedstock/product slate
optimization;
Maximize fuels margin
from custom base
Experienced
Based Scenario
and Volatility
Probability
Analysis
Potential scenarios,
and probability
associated with each
scenario
 Allows to uncover
“hidden Insights” through
learning from historical
data
 Various modeling
algorithms readily
available from open
sources in various script
languages
 Simple concept, easy to
understand and apply
 Requires “smart people in the
room” – very much based on
empirical experience to derive the
assumptions
Screening analysis,
mature project with good
understanding of the
potential scenarios and
probabilities
Stochastic
Optimization
 Most advanced method
in terms of optimal
decisions from the model
capturing the real option
value with consideration
of the uncertainty of
parameters
For subject matter experst refer to Appendix 4: Subject Matter Experts
35
Exploration bidding;
project development;
Infrastructure evaluation;
long term contracts; LNG
pricing structure
Storage
Near term trading
decision;
Software Tools for Evaluation of Options
Tools to enable method implementation come in a large variety, but can be bucketed in two mainstreams:
-
Analytical tools - Closed-formula models
Methods:
Black-Scholes; Kirk’s approximation, etc.
For example: Spread option on a pipeline or tank commitment
Tools:
‘FEA’ industry software; Python
-
Probabilistic tools - Stochastic simulation models
Methods:
Binomial; Monte Carlo; Stochastic Optimization
For example: Facility shut in option valuation
Tools:
Excel with embedded macros; Matlab/C++ (3rdP build); Python
For consistency, efficiency and ability to apply to ‘unique’ business situations, the medium term objective
is to develop an in-house Python based core modelling library approach. Until such time there are a
variety of tools already available and used within ExxonMobil and Appendix 3: Tools & Contacts provides
a sample list and respective Subject Matter Expert contacts.
For stochastic simulations excel based models have been developed to (i) model the price paths and (ii)
enable all those path to be incorporated in existing economic models to understand the risk, uncertainty
and optionality due to volatility of price.
36
Integration with Opportunity Evaluation
The approach described by this paper integrates with the Opportunity Evaluation framework. Historically
we valued the deterministic intrinsic value. Now we seek to value, include and separately identify, the
value from asymmetry & optionality: the ‘Extrinsic Value’, when material in opportunities we are framing
and presenting for a decision.
The discussion and debate, encouraged in the OE framework, naturally extends into the asymmetry &
optionality area. The linkage between Market Attributes, our business Enablers, competitive behaviors
and our competitive advantage are all key elements in opportunity evaluation, and fundamental to the
value of asymmetry & optionality.
Integration with Scenario Analysis
Scenario planning aims to define long term and large scale critical risks and uncertainties and develop
plausible scenarios in order to discuss the impacts and the responses in each scenario relevant for an
opportunity, project or strategy under consideration (reference S-BORE). By its very nature it tries to
identify what drives a certain way the world, region or local situation can develop (for example big shifts
in society, economics, technology, supply & demand and politics). These fundamental shifts will have
impact on the uncertainties, and as a consequence on the assumptions. For this very reason, each
scenario may have its own assumptions and therefore its own set of parameters such as volatility, mean
reversion, etc., causing different valuations for real options and asymmetry in opportunities, projects
and strategies for each scenario. Therefore each scenario may deliver a distinct Extrinsic Value.
37
Presentation of Results
Where possible the results from the analysis should be reflected in a standardized format. Standardization
is to help both decision framers and makers in articulating and understanding the output of the analysis
and make comparisons easier. As Extrinsic Value methods are essentially all founded in the principles of
probabilistic analysis, the recommended presentation is as shown in the three graphs below.
Cumulative Distribution Function (CDF, aka S-curve, first and third graphs) is the primary approach,
supported by Probability Density Function (PDF, middle chart). The examples display the results for a
reference case and scenarios. While a PDF has more immediate visual interpretive power for many, a
CDF has the benefit that it is visually easier to identify the differences between multiple scenarios.
Extrinsic Value, as an addition to the Intrinsic Value is identified in the third graph through the dotted
lines.
38
Key OE tools integration
Metrics
S-Bore
Red/Blue
Metrics should include extrinsic components if part of the evaluation
Scenarios are key to asymmetry & optionality, enriching the discussion
Extrinsic Value should be under the same scrutiny as the rest of the opportunity
39
Integrating Extrinsic Value into OE
Experience shows that identification and valuation isn’t a guarantee to success. This sections articulates
best practices to better assure value capture.
Our business, and the opportunities we pursue within it, in many cases include asymmetric inputs (market
attributes and enablers) and/or outcomes (returns) caused by or providing opportunity to be captured by
executing on the flexibility in our contracts and physical systems. When we evaluate opportunities we
should include, when material or relevant, the value of and the discussion around the asymmetry and
optionality. However, being able to articulate the Extrinsic Value and its drivers does not mean we are
assured of our ability to capture it (realize the value).
Compare this to a project proposal to build a tank: When we present the opportunity for an investment
decision, (FID/Gate 3), we have a design and execution plan on how to build it, a Project Management
Team to oversee the development, a plan and process to establish startup and operations, and operations
sustainment to capture the value from the physical asset we build. There is clear accountability and line
of sight across the evaluation, planning, project execution (implementation) and operations phases on
how we will realize the value.
So when we justify an opportunity which includes a (material) component of Extrinsic Value, we need to
have assured ourselves that we understand how we will capture the value. The below workflow shows
expectations for the three phases (i) Opportunity Evaluation; (ii) Execution / Implementation; and (iii)
Operations. This includes clarity and alignment around who is accountable for the business value and
how we steward it.
The next sections look at the key steps, post-valuation, that need to be thought through and planned for
prior to passing the Gate 3 (or equivalent) gate for an opportunity.
40
Accountability
The concept of accountability is quite simple: Who ultimately do we turn to for the overall result the
opportunity delivers because she or he has all the means to enable the results, weights the risks and
rewards, supports (or takes) the decision and is the business (P&L) owner. While in a big organization
responsibility of components may be within different organizations (e.g. project execution with Global
Projects, operations within the Operations organizations, etc.), ultimately there should be a single
business owner that assures alignment with the proposal, the implementation plan, operations,
commercial execution, etcetera. The accountable person own the consequential Profit & Loss and is
accountable for the opportunity to his or her superiors. He or she has the opportunity to steward the
results to measure progress and course correct; he or she also ensures alignment across responsible
departments and functions. It is key that early on there is alignment on who is accountable and what
groups have responsibility for delivering on the parts and pieces that enable capturing the results.
The accountable business leader must ensure that:
-
The assumptions are sensible and based on fundamentals, market insights and expertise
-
There is a clear Execution and Operationalization Plan to capture the Extrinsic Value
The valuation is understandable and justifiable and reflects the business case to be pursued
The flexibility that enables the optionality or asymmetry exists or will be enabled in our system
so that it can be deployed to capture the extrinsic value
We have alignment on how the results will be stewarded
The following sections address key assurances / components to be in place to give credibility that we
can capture the extrinsic value.
Implementation & Operationalization Plan (I&OP)
When contemplating to move forward with pursuing the Extrinsic Value during the Opportunity
Evaluation Phase, assurances need to be in place that we can realize the value. During this phase, prior
to a go ahead decision and after having identified the Accountable Business Leader, a plan should be
developed that outlines what is required to get ready for operations (the Implementation Plan) and how
we will Operationalize it (Operationalization Plan). While Operations logically follow the implementation,
only when the team has a clear handle on how operationalization looks, can an Implementation plan be
credibly created. Elements of an I&OP are, but not limited to, and part worked in detail during the
execution/implementation phase:
-
Data and information requirements (internal and external sources)
Models and tools
Systems and processes
Organization, positions and interfaces (cross-functional)
Roles & responsibilities
Planning & Stewardship approach
The accountable business leader ultimately owns the governance and must be satisfied the I&OP will
enable value capture.
41
Implementation & Operationalization
Throughout the implementation and operations startup a project team, or designated individuals, are
responsible for enabling the future organization to consistently manage the designed flexibility. A key
element here is the sustainment and preservation of all that drives the flexibility, the foundation, together
with consistent optimization of contracts, assets and other enablers. Just like changes in the design of a
facility changes its functions, so do changes to what makes up the enablers to capture the value from
optionality and asymmetry. The accountable business owner provides the oversight and enables problem
resolution governance to deliver to intent or better.
Plan & Stewardship
The fundamental challenge in planning and stewarding the results from optionality and asymmetry lies in
the by nature unpredictable outcomes as measured in a set time frame (month, quarter, year).
Optionality and asymmetry generate their value as a result of volatile environments, therefore in many
cases the value is not captured ratably. In some cases no option may have been exercised, although in
others it worked significantly consistently in our favor.
This fundamental nature makes stewardship more difficult than, for example, reviewing simply ‘asset
uptime’. Such a metric in itself may not work, and needs to be combined with when the ‘signal’ is that
value can be created in a volatile market, versus when not. If a pipeline ships oil only when the price
differential (hopefully materially) exceeds the variable costs by design, then throughput is a function of
the markets differentials and our ability to ship incremental oil. These profit opportunities are unlikely to
nicely match stewardship fixed time measurement periods.
Ultimately there are three core methods to stewards:
Identified Options




Material options to a large business line should be identified for
individual stewardship and tracked
Did we triggered the option (was prescribed action taken or not), time
delay in response to trigger, value generated after action taken, value
lost if action not taken or due to delay
Data is consolidated and results reviewed to evaluate o the effectiveness of the trigger and the response by looking at
value generated
o ability to respond to triggers in a timely manner
o additional value created by newly identified options
Evaluation o Value generated from project identified triggers will be compared
to the value estimated during project valuation
o Reasons for non-responses or delayed responses will be evaluated
along with value losses for potential issues with the original design
of the trigger and responses
 Triggers and the responses will be continuously improved based
on learnings
42
o
Portfolio Earnings


Investment Reappraisal







Options identified post project will be evaluated to see what could
have been done to anticipate these triggers during project
definition phase for a more efficient design for future projects
In many cases options may be smaller (or a collection of them) and
managed at a lower tier in the organization
Such groupings of options are most efficiently managed against
earnings; there are enough that some ratability sets in and portfolios
can be understood
By design a IR will review versus the project basis, and should thus
evaluate how the initial assumptions, valuation and I&OP were handled
Quality of assumptions in Opportunity Evaluation versus what actually
transpired; is the volatility similar to the expected
Was the I&OP properly implemented
Is the optionality being captured
Trigger response ability evaluation along with value implications versus
original understanding and design
Have new options been effectively identified and pursued
Options identified post project will be evaluated to see what could have
been done to anticipate these triggers during project definition phase
Ultimately optionality value is based on actionable events that can either generate additional income or
prevent potential loss. Stewardship must include – where material – if we actioned appropriately.
Fundamental shifts
Markets evolve and so does our competition as it responds like other market players do. Therefore the
accountable business, aided by – but cannot be solely based on - stewardship, needs to understand when
the market attributes have changed and/or our enablers don’t provide an advantage anymore to capture
the value. This requires a business from time-to-time to step back, understand the data, information and
market insights, etc. over a longer period and what that does to the forward looking Extrinsic Value. This
is part of taking ownership of our business throughout the value chain.
Lessons Learned and Challenges
Many companies have struggled with an effective implementation of what is described in this guide,
however those who had the leadership commitment to it, build the capabilities successfully. Historically
EM has tried and ‘failed’ in consistent evaluation of Asymmetry & Optionality. Our culture historically
preferred certainty over uncertainty, our management systems may have encouraged this, stewardship
in particular. A natural bias to overweight downside risk versus upside risk doesn’t help. We anticipate,
just like it did within competition and others outside our industry, that it will take time to build our
organizational capability in this area. Decision framers, practitioners of the valuation techniques, and
specialists are key in driving the valuation principles in an unbiased manner based on their technical or
specific expertise and building on the strength of the collective competencies of all of our functions!
43
Appendix 1: Optionality & Flexibility Identification Aid
Area
Category
Enablers
Capital
investments
Phased development (vs. all at
once)
Design not limiting future
expansion capability
Ability to cancel when new/updated information presents itself
Pre-invest for future
expansion
Low cost to "do now" (pre-invest) during project execution for future capability/optionality
Residual interest
Retain exposure to future margins to sustain option
Portfolio effect
Reduced matrix / optimization flexibility
JVs
Protection from downside if partner interest/business direction diverge; EM sees more/less
value in JV vs partner
Crude or Product pricing
Backward looking pricing option or capture of forward market structure
(Backwardation/contango)
Volume flexibility
Produce non-ratable - pricing vs volume profile
Divestments
Commercial
structures
Initial outlay to retain capability for lower cost future expansion
Ability to adjust volume based on our value in use
Volume deferral based on view of future vs current price
Ability to access and secure rights to limited logistics
Production sharing
contracts/agreements
Tax
Limit or increase exposure to crude price and production volume
Cost basis management
Tax defensibly - Leveraging low tax affiliates
Trading
Execution /
optimization
Origination
Creation of incremental long and/or short positions that create value by allowing to capture
pricing volatility , producing asymmetric returns
Time arbitrage
Differential arbitrage
Rights to storage (logistics capability) or drawdown to respectively capture
contango/backwardated market structure, usually of flat price, sometimes seasonal crack
spread
Contango on crude quality differential time arb
Geo arbitrage
Ability to move (or not) molecules (feed/product) to different market under different pricing
Quality arbitrage
Dynamic Forward Trading
Exploit inherent giveaway in Hydrocarbon batches (e.g. NY harbor blend hub, Crude
Blending)
Volatility in forward derivative market
Destination flexibility
Vessel/pipeline destination options (or not)
Ability to move molecules (or not) to different market - Crude oil destination restriction (Dfree vs D-restricted)
Sourcing / supply flexibility
Matrix of supply options to purchase from and deliver to
Quality
Ability to switch between sales channel (supply/wholesale/retail) and grade mix
Access to and flexibility to substitute components and capability to blend products to specific
market quality
Ability to nominate crude between fields with differing quality under same price (e.g. Al
Shaheen)
Capability and Flexibility to process wide spectrum of feed - not limited by site processing or
product quality constraints
Access and capability to capture crude quality differential not priced into premium
Second Order Pricing Effects /
Logistic Power balance
Oversupply in market may result in lower prices in the market (converse is true), Logistics
outlets may negate impact
Pricing negotiation leverage for alternate disposition capability
This page refers to page 13 (and to Spreadsheet link)
44
Appendix 2: Enabler & Operationalization Checklist
Purpose
Consider the additional requirements (above any for intrinsic value) that will be necessary to capture the
extrinsic value credits included in the OE. These additional requirements should be documented and
(where applicable) costed and included in the OE cost basis. The accountability for implementation of
the additional requirements and subsequent capture of the extrinsic value should be clearly defined.
Pricing volatility
Be able to vary the volume (produced/shipped/purchased/sold/refined etc.) with time on a short-term
basis (day-to-day or month-to-month) in order to capture the most advantageous pricing. This should
produce an asymmetric (positively skewed) return even if the original pricing distribution is symmetric
Considerations
Examples of potential requirements or additional costs
Logistics - additional
capability
Additional tankage to allow volume variability with time (e.g. pipeline storage,
longer tank-hold times if non-ratable)
Increased capability above base design requirements - e.g. increased pump or
line sizing to allow volume variation
Pre-investment for future optionality to expand
Wider refinery or plant operating envelope for (e.g.) TAN, sulfur, coke
morphology to capture spread volatility with time
Increased operating
costs
Typically if costs are non-linear with the necessary volume variability - e.g. unit
start-up or shut-down, power, additives, overtime, additional storage, tankage
throughput
Additional staff
resources
Monitor the required (e.g. pricing) data and decide when and how to exploit
the optionality giving the extrinsic value
Additional operating resources if capturing extrinsic value would increase
operational complexity (refinery, pipelines, scheduling)
Additional commercial or sales resources to manage non-ratable volume flows
Training to
develop/reinforce
skillsets necessary
How to exercise volume tolerance optionality given flat price or spread
evolution
How to exercise settlement optionality (e.g. true-up or volume sale) on
(product) exchange deals given pricing evolution
Additional process
inputs to allow
monetization
Additional data (e.g. real time market data for a specific item)
Ability to trade derivatives not currently authorized by EM’s Trading Authorities
(but available in market)
Additional technical expertise (e.g. on a specific blending process) beyond that
present in EM
45
Improved analytical
tools
Follow flat price evolution through to decision point when optionality exercised
Predictive or monte-carlo analysis to assess price path from decision point to
price-out completion
Track monthly indicators (e.g. TAN in crude slate) versus annual or turnaroundturnaround cycle if variation of these gives extrinsic value
More extensive
project
documentation
Ensure that optionality on long-term contracts does not become forgotten or
ignored by organization over time
Change decision timing on (e.g.) investments or divestments
Phased development - make multiple small decisions versus one initial
Considerations
Examples of potential requirements or additional costs
Additional costs
incurred
Additional multiple review steps
Future escalation costs above inflation for materials (e.g. catalysts)
Loss of ‘buying in bulk discount’ - e.g. commitment length of a time charter
Contractual terms
Changes to contractual terms for this contract
Potential changes to terms for other contracts that would offset
46
Appendix 3: Tools & Contacts
Tool
Contacts
Internally Developed
Excel / Matlab based Stochastic Modeling of Price
(Mean Reversion, GBM)
Excel based macros or Matlab code to run
through multiple price path in economic models
Excel based Black Scholes model and Kirk’s
Approximation
Script language based Machine Learning
UIS RTD Customer Tools (Gas / LNG)
Tingting Yao; Obinna Duru
Obinna Duru
Hui Zhang; Neil Gupta
Shiva Kameswaran; Zimin Lu
Bora Tarhan; Kevin Furman
External Tools - Licensed
C++ FlexTool with user interface by DecisionTrees
•
•
•
•
C++ with Excel Add-on tools by FEA
•
•
•
•
Jean Romersheuser Guy; Andrew Fleming
Evaluation of value of optionality in flexible contracts
Evaluation of infrastructures (storage, pipeline,
terminal)
Decision support on asset planning, contracting,
marketing, trading activities
Expertise in stochastic optimization
Hui Zhang; Neil Gupta
Evaluation of financial options
Evaluation of storage and swing contract
Decision support on contracting, marketing, trading
activities
Expertise in evaluation of financial options
External Tools - Third-party Platform
(requires building the applications)
@Risk (Excel plug in)
CrystalBall (Excel plug in)
SIPMath (Excel plug in)
JMP
AIMMS
Matlab
R
Python
Bora Tarhan (RTD)
Obinna Duru
PIN (The Python Interest Network)
Price volatility & fundamentals stochastic modeling: The Upstream Opportunity Evaluation & Analysis
(OEA) team is working closely with UIS Research Technology Development (RTD) to engage both internal
and external experts to improve the methods for price modeling.
47
Appendix 4: Subject Matter Experts
Function
UIS - OE&A
Name
Tingting Yao
UIS - OE&A
SM Suresh
F&L Optimization
Tim Suter
F&L - C&T QAS
David Dalton
F&L - C&T QAS
CSP
Treasurer’s - Chemicals
Treasurer’s – F&L
Hui Zhang
Drew Bishop
Ted Postula
Jorn Sturkenboom
UIS - GTO&T
UIS – RTD
F&L – DAS
UIS – OE&A
Neil Gupta
Bora Tarhan
Shiva Kameswaran
Obinna Duru
Specialty
Statistics & Stochastic Modeling
Monte Carlo Simulation
Valuation & Concepts
Upstream Senior Economics Consultant
OE Integration
Business identification
OE Integration
Bridging the identification into quantitative
valuation
Quantitative Analytics & Structuring
OE Management, Process & Integration
Valuation & Concepts
Valuation & Concepts
OE Integration
Black Scholes; Kirk’s approximation
Stochastic Optimization
Predictive Price Modeling
Probabilistic Simulation
48
Appendix 5: Examples
49
50
51
52
53
54
55
56
57
58