our top 3 most read blog posts
DESCRIPTION
This small E-Book is about our top 3 most read blog posts on Commodity Risk Management.TRANSCRIPT
© 2014 Risk Edge Solutions 1
TABLE OF
CONTENTS
3 Blog 1: The Humble PI of Risk Management
9 Blog 2: Commodity Risk Management beyond
VaR
15 Blog 3: Review: Report on Economics of
Commodity Trading Firms
f int © 2014 Risk Edge Solutions 2
BLOG
1The Humble PI of Risk Management
A lot of approaches and frameworks get discussed in companies
that are relatively new to implementing Risk Management
processes – the types of control processes, committee structure,
risk reports, estimation methods, quantification models, systems,
people, etc. In fact, these discussions sometimes go so intense
and deep that it all becomes a big circle that keeps coming back to
itself, and eventually nothing moves. If you’ve ever been a part of
such an implementation, you too would be painfully aware of this
fact.
One of the ways to simplify things while starting a new Risk
Management Initiative in the company, or while revamping an old
one is, to focus at the heart of a Risk Management Function – its
purpose. And at the core of a Risk Management Function is a
powerful, yet Humble PI of Risk Management – the Probability –
Impact (PI) matrix.
INTRODUCTION
Blog 1: The Humble PI of Risk Management
© 2014 Risk Edge Solutions 4f int
For energy and commodity trading companies, each of the risks
can be positioned in a PI Matrix. The risks could be as subjective
as key-person risk (key trader / senior management leaving the
company) and reputation risk, or more quantifiable like market /
price risk, liquidity risk, currency risk, interest rate risk or
somewhere in-between – like credit risk, default risk, etc. What’s
most important for any organization is identifying which risks are
most relevant to them and try to estimate their probability of
occurrence.
This exercise is usually done based on the past experiences &
data, and future plans of business growth. For quantifiable risks,
it’ll be easier to estimate the probabilities of occurrence as there
are several models available to help you do that. But for other
types of risks, companies generally take the qualitative / subjective
estimation approach. Generally, the first few tries will not be ideal,
and it is important to accept that it is a process, which will
eventually evolve into a useful framework if the entire team keeps
at it.
What is the Humble PI Matrix?
Blog 1: The Humble PI of Risk Management
© 2014 Risk Edge Solutions 5f int
Once the probabilities of those risk events have been arrived at,
the next step is to calculate the impact each of those events would
have on the P&L in case they materialize. It is okay to start with a
good approximation here rather than aiming for precision – which
can only be achieved over a period of time. Combining the
probability and impact of each risk event gives their position in the
PI Matrix as given below:
Understanding the PI Matrix
Blog 1: The Humble PI of Risk Management
© 2014 Risk Edge Solutions
6f int
The policies, control processes, systems and reports should then
be designed as per the position of the risk event – for example, all
risk events falling under Quadrant 1 should be actively managed,
which means there needs to be a sophisticated system, reporting
process, regular review and control process built around those
events.
On the other hand, those falling in Quadrant 2 may just be actively
monitored, which means they too need to have their limits
monitored on a regular basis, with only periodic hedging / de-
hedging / trading decisions taken to reduce these risks.
Risk elements in Quadrant 3 just need passive monitoring, which
means risk events falling under this quadrant need to be assessed
at periodic monthly / quarterly basis or on an as-and-when basis
since for these, both the probability of occurrence and impact on
P&L are low.
Risk Events in Quadrant 4 need to be managed with limits and
some kind of quantification, but not so actively – since these risks
have relatively lesser impact on the P&L.
Understanding the PI Matrix (Contd.)
Blog 1: The Humble PI of Risk Management
© 2014 Risk Edge Solutions 7f int
The PI Matrix, therefore, is not a solution in itself for Risk
Management, but it certainly gives companies a good starting
point. But far more importantly, what it does is send a message
across the organization to focus on a few risk events rather than
all of them. Unless the organization has a well-established Risk
Function, it is rarely ever possible for them to manage all the risks
equally – those who attempt to do so, realize rather painfully that
they are actually managing none of the risks. The PI matrix helps
you choose your battle, the most important ones first – and that is
what makes it one of the most powerful starting points in Risk
Management.
CONCLUSION
Blog 1: The Humble PI of Risk Management
© 2014 Risk Edge Solutions 8f int
BLOG
2Commodity Risk Management beyond VaR
But it doesn’t tell you what your position / portfolio could lose
beyond that confidence. If you are the one managing risks in your
organization using the VaR framework, chances are you would
already know this. So the real question is, is there and extension
of Commodity Risk Management beyond VaR that solves this
problem? Expected Shortfall (ES) is part of the answer to this
question. Let us take a look at the Issues with using only VaR and
how Expected Shortfall can help us overcome those issues to a
certain extent.
INTRODUCTION
Blog 2: Commodity Risk Management beyond VaR
First, the bad news –
Value at Risk (VaR) of
a position / portfolio
just gives the
maximum loss you
can have, with a
certain confidence.
© 2014 Risk Edge Solutions 10f int
Let’s start by putting some numbers around this problem.
Consider a simple, 1 commodity portfolio below:
The portfolio is currently worth $ 250 mn with a M2M P&L of $ 4.1
mn. However, the VaR, which is calculated for 1-day Holding
period and 95 percentile confidence level, is greater than M2M
P&L, and is $ 4.5 mn. This means that on 19 out of 20 days the
position is not expected to have a M2M Loss exceeding $ 4.5 mn
(which is fairly bad by itself, since it can easily wipe out the entire
M2M P&L !). But on 1 out of 20 days (on an average) this M2M
loss will exceed that figure. But, to what extent can this loss be
above $ 4.5 mn? This is a question that VaR does not answer. On
that 1 very bad day, the loss could be anything above $4.5 mn.
Issue with using only VaR
Blog 2: Commodity Risk Management beyond VaR
© 2014 Risk Edge Solutions
11f int
Now, while it is comforting (and in compliance with regulations for
a lot of industries) to know what portfolio’s worst loss could be on
19 out of 20 days (on an average), it is severely discomforting to
NOT know how much could the portfolio lose on that 1 very bad
day ! Could it be $ 5mn, or 10, or 50? It makes a huge difference,
right?
Issue with using only VaR (...contd.)
Blog 2: Commodity Risk Management beyond VaR
© 2014 Risk Edge Solutions
12f int
And now for some good news, this is where Expected Shortfall
(ES) comes to our rescue. ES, also sometimes known as
Conditional VaR or Expected Tail loss, tells us how big the number
could be on that 1 very bad day. To represent it mathematically,
where,
X is the random variable of loss and
α is the confidence percentile, 0<α<1, (α=0.05 in our case, since
we are using 95th percentile for VaR)
The above equation is just a complicated way of saying that ES for
a position at any confidence level is an expected to be greater
than or equal to the VaR for that position at that confidence level !
And here is how the ES is calculated:
This equation too, is just a complicated way of saying that ES is
an average of VaRs, between 0 and α confidence levels !
Commodity Risk Management beyond VaR :
Expected Shortfall
Blog 2: Commodity Risk Management beyond VaR
© 2014 Risk Edge Solutions 13f int
With this knowledge, let’s look at the same table again:
Now, with this new knowledge it is easier for any Risk Manager to
commit about the loss on that 1 very bad day, which is $ 7.8 mn. It
might be less or more for the organization, depending upon
several factors including risk appetite, but this knowledge surely
extends our comfort to an area where VaR doesn’t reach.
There are still a lot of questions left unanswered though, like:
• How can I be sure that the worst case loss (under any
circumstance), will not exceed ES?
• How do I determine whether the ES amount is less or more for
my organization?
• How does knowledge of ES impact my Risk Policy?
These and many other such questions will be dealt with in our
future posts.
CONCLUSION
Blog 2: Commodity Risk Management beyond VaR
© 2014 Risk Edge Solutions14f int
BLOG
3Review: Report on Economics of Commodity
Trading Firms
According to Reuters, “The firm approached Craig Pirrong, a well-
known professor of finance and commodity markets commentator
at the University of Houston, last July to commission an
independent review of the commodity trading industry, with the
goal of “demystifying” it. The resulting 63-page report, based on
public filings and interviews with around 10 senior Trafigura
traders and a number of C-level executives last September,
reached a conclusion similar to several previous reports: relative
to Wall Street banks, merchant trading companies’ size, function
and balance sheets make them far less likely to be sources of
systemic risk.” (contd.)
Our Review
Blog 3: Review: Report on Economics of Commodity Trading Firms
The recent release of Report on
Economics of Commodity Trading
Firms is a bold step from a well-
respected Commodity Trading Firm
(CTF) – Trafigura, in giving an
insight and “demystifying” the
secretive commodity trading
industry. And it is indeed a very
impressive read.
© 2014 Risk Edge Solutions 16f int
The Report focusses on how Commodity Trading Firms are
exposed to various risks – Price, Basis, Spread, Liquidity, Credit,
etc. and how they manage those risks. Risks are interspersed
across the value chain of any commodity business, and the report
emphasizes the need for commodity trading firms to have Risk
Management practices in place. Notably, following points are very
unique to this report:
• There is very low, at times negative, correlation between
quantities of various commodities (2001 – 2011). Given that
Commodity Trading Firms are exposed to volumes more than
prices, it shows that there are huge benefits from
Diversification that firms can achieve – to reduce the
variability of firm’s risk.
• Integration across the value chain gives firms the ability to
self-hedge and absorb the shocks to a certain extent.
• Trafigura has invested USD 550 mn over the last 3 years in
Risk Management and Measurement Systems. According to
the report “The increasing data and analytical intensity of
trading and risk measurement modeling is tending to
increase the degree of these scale and scope economies.”
(contd.)
© 2014 Risk Edge Solutions
Blog 3: Review: Report on Economics of Commodity Trading Firms
17f int
• Trafigura measures Risk using Monte-Carlo method that
combines 5000 risk factors, at 95% confidence level, for 1
day holding period and its VaR limit is less than 1% of Group
equity.
• Along with VaR, the firm uses Expected Shortfall (Conditional
VaR) along with qualitative measures to broaden the scope of
Risk Management.
These along with several such data points and perspectives make
this a must read for all Commodity Trading Firms. You can read
the original report here:
We have put the original report on our site for your benefit.
Read it here.
© 2014 Risk Edge Solutions
Blog 3: Review: Report on Economics of Commodity Trading Firms
18f int
What is Risk Edge?
A Publication of
Software
Risk Consulting
Define and Implement Risk Policy, with an aim to
lower the Total Cost of Risk (TCoR). Customized Risk
Training programs to align internal people with their
role in risk management better
RiskEdge Software
An Integrated Risk Analytics Platform for Commodity
companies. It is Easy-to-use, highly Configurable, and
really Cost – Effective. It automates Risk Processes
and enables Deeper Business Insights.
Consulting
© 2014 Risk Edge Solutionsf int
A Giant leap in Commodity
Risk Management
Try RiskEdge !
Why RiskEdge?
1. Advanced Risk Platform built specifically for Commodity Companies.
2. It is easy to integrate with existing systems
3. Flexible to configure and easy to use.
4. For most companies, it can be set-up in under 3 weeks !
5. Multi-Dimensional Analysis capabilities
6. RiskEdge Algorithm Library (REAL) - To integrate your own Algorithms
7. Web-based system, built with latest technology
© 2014 Risk Edge Solutionsf int