The document discusses the role and needs of competitive market makers in European index options markets. It begins by explaining that competitive market makers provide liquidity to exchanges in return for fee reductions, and also actively trade to profit from volatility movements. It then outlines some of the key needs and challenges for competitive market makers, including continuously updating quotes to reflect their views, making rapid trading decisions based on large amounts of market data, and effectively hedging their positions. Technology is seen as crucial to address these needs, with co-location, low-latency trading systems, and pre-calculations helping market makers compete effectively.

1. Regardless of the current state of the financial markets or the ap-
petite for risk, investors participating in derivatives markets have a
need for liquidity providing facilitators. This type of market making
activity has been seen in derivatives markets around the world since
inception and the market making role has continuously evolved and
been adapted to developments in listed derivatives trading.
Imagine standing there in the pit 20 years ago - feeling the flow of
the market; looking in the other traders’ eyes trying to read their
mind on the next move to make; split second decisions to quote a
market or spot a good deal and trade on it. Then go home at the
end of the day having made thousands of trading related decisions
solely based on your sense of the market and your head as number
cruncher and risk tracker.
Trying to make a living this way; a nostalgic lost world? A daunting,
scary thought? Or simply just an outdated description of what to-
day’s market makers experience?
Was there a change in success fac-
tors for a market maker at CBOE
in 1993 when they started using
handhelds?1
Would a profitable
market maker in today’s markets
been equally (or even more) suc-
cessful in those days?
These questions are difficult or
even impossible to answer. But
still, it is tempting to say that the core qualifications required to be
a successful options market maker today are not far from what was
needed in 1993; a developed sense for the market dynamics and an
analytical mind looking for the next trading opportunity. The tech-
nology requirements seen on market makers today would then be
seen as a natural consequence of global technology development
since the early nineties.
1 CBOE website
Being a market maker in 2010 almost always means utilizing and
relying on technology. Market makers that manage to get enabled
by (rather than forced to use) technology and fully exploit the capa-
bilities of computerized trading are in a good position to succeed in
today’s derivatives markets. It is important to remember, though,
to never try to replace what can only be stored inside the trader’s
mind with trading algorithms; technology is a key criterion to be-
come a successful market maker today, but it takes a lot more than
just technology to excel.
Market development in European index options
products
In times of uncertainty and financial turmoil investors tend to seek
safer ground and increase their focus on equity index derivative
products while reducing interest in single-stock products. Exam-
ining the average daily traded volume since the beginning of 2006
in the five most actively traded
index options products in Europe
clearly shows that the last four
years have been no exception to
that rule of thumb.
EURO STOXX 50® index options
on Eurex have traded a daily av-
erage volume of approximately
1-1.5 million contracts since the
fall 2007. During the fall 2008
when the credit crunch (or the fear of it) was running at its peak,
the EURO STOXX 50® options had three very strong months with
an all time high (+55%, excluding Sep 2008) average daily turnover
in October of 2.6 million contracts. After those three months the
average daily turnover started to balance back to pre-crisis levels
and has stayed there since.2
2 Futures And Options Intelligence website
INDEX OPTIONS MARKET MAKING -
Staying competitive in today’s markets
Orc Software examines the criteria for profitable market making on the major European index options markets.
Senior Product Manager Markus Kämpe describes key needs in competitive index options market making; looks at
challenges and key success factors in the most liquid European index options products and identifies how market
advancements are placing new requirements on market making software solutions.
Being a market maker in 2010 almost always
means utilizing and relying on technology.
Market makers that manage to get enabled by
(rather than forced to use) technology and fully
exploit the capabilities of computerized trad-
ing are in a good position to succeed in today’s
derivatives markets.
”
“

2. DAX® index options, also traded on Eurex, show a similar turnover
history with a somewhat lesser fall 2008 effect. Since the spring
2007 the daily average volume has been approximately 300 to 450
thousand contracts, but in October 2008 an all time high (+17%)
turnover of 580 thousand contracts was recorded. September and
November also had good volume, but not to the same extent as
EURO STOXX 50 ®.3
“The significant increase in turnover in our EURO STOXX 50® and
DAX® index options during the fall of 2008 is what you would ex-
pect when the credit crunch emerged,” says Rex Jones at Eurex.
“Since EURO STOXX 50® is a broader index, representing constitu-
ents from more than ten countries of the Eurozone, investors are
more likely to turn to EURO STOXX 50® than other regional indices
and that is reflected in the more pronounced turnover increase in
EURO STOXX 50® compared to DAX®.”
FTSE 100, AEX and CAC 40 index options, traded on NYSE Liffe, are
other examples of European index products that had very strong
months during the fall of 2008, but not as significant as the turn-
over increase seen in EURO STOXX 50®.3
DAX®, FTSE 100 and AEX options all show a volume history for
2009 that is similar to EURO STOXX 50®, i.e. moving back to the
volume range where they traded before the fall 2008, while CAC 40
options are trading at volumes significantly lower than before the
peak in October 2008.3
What are then the needs, challenges and success factors for mar-
ket makers in these products? A necessary first step before elabo-
rating on that question is to choose a type of market makers and
get an understanding their incentives and trading activities.
Competitive market makers
In Europe market making agreements between exchanges and
market makers most of the times give the market maker a sig-
nificant reduction on trading fees at the exchange and in return
the market maker provides liquidity in a set of strikes for a num-
ber of expiry months. There are several types of market makers
in Europe providing liquidity in index options based on this type of
agreements, but in this article
the focus will be on competi-
tive market makers.
Competitive market makers4
are traders providing liquidity
to the market to get fee re-
ductions and in addition the
competitive market makers
are actively trying to get trades
and also enter positions to
trade a view on volatility. A competitive market maker quotes tighter
spreads and often also higher volumes than obliged by the market
making agreement with the exchange. In this way the market maker
has a fairly aggressive trading style and tries to get a significant
market share in terms of traded options volume in the product.
The fee reductions are an important incentive to the competitive
market maker, but assuming that the market share is significant
there will also be considerable profit for successful competi-
3 Futures And Options Intelligence website
4 Also called primary market makers
tive market makers from earning roughly half the quoted spread
on each trade made. To further increase profitability a competi-
tive market maker also runs an electronic eye to detect and trade
on opportunities when order flow enters the market inside the
spread.5 6
In the following sections the view of a competitive market maker is
used to identify needs, challenges and key success factors in index
options market making.
Market making needs
To identify a market maker’s needs, key concerns for quote stream-
ing, hedging and opportunities trading respectively will be dis-
cussed. In addition, analytics will be looked into with specific focus
on volatilities.
Do your quotes reflect your trading view at all times?
The most obvious need for a market maker is to stream quotes
to the market continuously. Since the competitive market maker
uses tight spreads and significant volumes it is imperative that the
quotes in the market are immediately updated when the underly-
ing future moves and reflect the market maker’s view on volatility
at all times.
It might sound like a fairly easy task, but assume it should be done
in the most competitive index options products in Europe and the
challenge at hand is definitively not for the faint-hearted.
Making the trading decisions
Starting from a trading decisions perspective the market maker
needs to work the quotes in the market depending on market view,
trades and changes in market conditions. What that really means
of course differs between traders, but three typical considerations
are:
Do I as a market maker want to fade7
on trades and in that case, how
should I do the fading? Do I want to skew8
my quotes in this situation
or should I work the volatility surface slightly which would affect my
trading levels for electronic eye as well? Do I fade just one side to
widen my spread or do I move both? For a specific market maker
the answers to these ques-
tions might (and most likely
will) differ depending on what
the current situation is in the
market.
Does my current trading risk
correspond to my view on the
market? If not, is there any-
thing I need to change in the
way trading decisions are
made right now to get into the risk profile I want? For a specific
market maker this means taking into account current risk (for ex-
ample delta, gamma and vega for the strikes and months traded)
when generating quotes to send to the market.
5 Some competitive market makers don’t run an electronic eye but instead
quote an even tighter spread to capture more volume.
6 Many competitive market makers are also highly active in the OTC market to
make the most out of their trading activity.
7 Generate a less aggressive price
8 Move quoted spread in relation to the fair value of the option
Since the competitive market maker uses
tight spreads and significant volumes it is
imperative that the quotes in the market are
immediately updated when the underlying future
moves and reflect the market maker’s view on
volatility at all times.
”
“

3. Do I have the right safety measures in place to prevent bad trades? As
a market maker the path to profitability seldom goes through few
trades with major edge but rather many trades with smaller edge
adding up to a significant profit over a longer period of time. At the
same time, one mistake can be very costly to a market maker and
without the right safety precautions the profit made up in days or
weeks can be lost in minutes or even seconds.
In addition to the three considerations above an index options market
maker needs to have a volatility model that works well for the prod-
uct traded. This will be discussed in detail in the Analytics section.
Challenge – Quotes in market according to a market maker’s risk
and view on the market with proper safety precautions active
Key success factor – The key success factor for this challenge is the
use of true algorithmic trading solutions
allowing for deployment of complex trad-
ing strategies. Competitive market makers
tend to find themselves somewhat limited
by technology in their trading activity when
using market making solutions based on
pre-defined trading logic or scripting.
Lining up technology with market
making needs
Had the market makers lived in an “ideal”
world without worrying about cost or real-
life technology considerations the quoting
discussion would have ended here. But
even if there has been many groundbreak-
ing technology shifts the last ten years we
are still far from (and likely will not get to) a
situation where competitive market mak-
ers can disregard limitations on technol-
ogy and not be affected by it in the trading
activity.
“The introduction of un-netted data feed by
Eurex at the end of 2006 opened up new op-
portunities for market participants,” says
Rex Jones at Eurex. “Traders using the un-
netted feed could all of a sudden react very fast on single orders
entering the market and at the same time they needed to handle
the significant increase in market data in a good way of course.”
The new market data capabilities obviously opened new oppor-
tunities for market makers by taking advantage of the additional
information that can be derived from the un-netted data and imme-
diately consume and trade on that information instead of trading
based on the netted market data. In very liquid products like DAX®
and EURO STOXX 50® the major increase in data meant (and still
means) major challenges for the market makers. The truly chal-
lenging product in terms of market data is DAX®, likely explained
by DAX® futures trading in half index points9
at an index level close
to 6000 and EURO STOXX 50® futures trading in whole index points9
at an index level close to 30009
at similar volatility levels9 10
, implying
that a market move of 1% will result in many more price updates in
DAX® futures than in EURO STOXX 50® futures.
9 Eurex website
10 STOXX website
There are several aspects of trading based on very intense market
data. Two typical considerations for a competitive market maker are:
How do I make sure I have as low latency as possible when getting
the data and react on it as quickly as possible? For a competitive
market maker this, to a large extent, means investing in technol-
ogy to get low latency delivery of market data. To secure low latency
data on Eurex and NYSE Liffe, where the index products discussed
are traded, co-location is an absolute must. Without co-location the
market maker will give a very significant advantage to other mar-
ket participants, which is hard to make up for by making smarter
trading decisions, especially since the data used when making the
trading decisions is received with higher latency than desired. It is
also important that the co-location set up is capable of handling
temporary technical disruptions between the trading room and the
co-located servers executing the trading decisions in a good way.
“To be truly competitive as a market maker
co-location can be seen as first choice of
connectivity,” says Marc Soeteman at NYSE
Euronext. “This is especially important in
AEX options where the market on screen is
tight and heavily traded, but it is also needed
in FTSE 100 and CAC 40 even though trades
in these products tend to attract a higher
proportion of wholesale business and are
often negotiated by telephone or through
NYSE Liffe’s system Cscreen.”
How do I make sure my trading decisions are
executed in the market as fast as possible
during peak load? For a European market
maker peak load in terms of market activ-
ity is normally experienced when the US
markets open in the afternoon. Especially
during this time it is important to have a
quoting activity that is optimized to meet
the needs of the market maker in terms of
latency and throughput. During peak load
it is very unlikely that a market maker can
update all quotes in the market immediately
for all moves in the underlying future.
The server-based algorithmic trading solutions used by market
makers today are capable of making an enormous amount of trad-
ing decisions per second implying that by using trading solutions
optimized for performance the actual generation of quotes based
on each market update is possible, but then the quotes need to be
delivered to the exchange. If the market maker tries to update all
quotes for all price moves, technical or exchange limitations (like
the max number of messages per second on NYSE Liffe or Eurex
VALUES API) might cause quotes to get queued (or even rejected)
before entering the exchange system during peak load.
Efficient use of market data coalescing; load balancing; proper pri-
oritizations of quotes; discarding suggested quotes with minor dif-
ferences to what is already quoted in the market; and smart use of
flushing and exchange quote messages (like asynchronous quoting
on Eurex or packaging of many quotes into one message on NYSE
Liffe and Eurex) are ways to make sure the quotes in the market are
as much up to date as possible.

4. To reduce the overall response time from receiving market update
to sending quotes to the market as much as possible of calcula-
tions required for quoting should be pre-calculated and the actual
trading strategy should be executed as fast as possible. Scalable
algorithmic trading solutions designed for low-latency and high
throughput server-based trading are suitable to meet those re-
quirements.
A competitive market maker that does not have a trading solution
that meets the technical aspects of competitive market making is
exposed to a significant risk of slow requoting, likely resulting in the
market maker getting picked-off on the quotes frequently. In that
situation it is hard for the competitive market maker to be aggres-
sive; resulting in the market maker being limited to wider spreads
and lower volumes in the quoting activity.
Challenge – Handling mas-
sive amounts of market data
during peak load, make in-
stantaneous trading decisions
based on the data and have
the quotes in the important
options updated immediately
Key success factors – There
are two main key success
factors for this challenge; co-
location of trading decisions handling temporary technical disrup-
tions and the use of true algorithmic trading solutions designed for
low-latency and high throughput trading with flexibility to optimize
performance according to a specific market maker’s needs. In ad-
dition fast market data and execution connections between the ex-
change and the co-located server executing the trading decisions
are important together with as much pre-calculations as possible
in the trading strategy.
Do you get the hedges you should?
Hedging is an integral part of the trading activity to make sure
that the market maker fully benefits from good options trades and
doesn’t take on any unwanted risk. Two typical considerations in
the hedging activity are:
When do I as a market maker want to hedge? A basic approach to
hedging is to automatically hedge all options trades immediately,
but the efficiency of the hedging activity can likely be improved by
taking existing positions and risk into account and/or let low-delta
options trades stay on the book until a number of trades have been
received and hedge the net of them together. This leads us back to
automation of non-trivial trading decisions that was the starting
point of the quoting discussion with similar market maker require-
ments.
How do I as a market maker make sure to get the best price for my
hedge? Many times hedging is done by hitting the BBO11
in the fu-
ture, but adding a bit more sophistication to the hedging activity
could improve the hedging result. By placing a limit order in the
spread it is possible for the market maker to not pay the full spread
in case the hedge order gets traded. This of course introduces a
risk of not getting the hedge, but by allowing the order to get more
11 Best Bid/Offer
aggressive over time and eventually hit the BBO if not traded the
risk of not getting hedged at a reasonable price is significantly re-
duced. Many market makers’ view on hedging is that the hedge
should be executed immediately to increase the likelihood of get-
ting the hedge at the price used for the traded option quote and
lock-in the volatility traded. In this case the market maker is back
in a situation with a need to get exchange data (trade notification)
as fast as possible and react on it immediately to hit the BBO and
not pay any ticks to get the hedge.
Challenge – Optimizing hedging from a cost perspective at as low
risk as possible
Key success factors – The two main key success factors for this
challenge have already been discussed in the quoting discussion;
co-location of trading deci-
sions and the use of true al-
gorithmic trading solutions.
Do you get the trading op-
portunities your electronic
eye detects?
Order flow that goes into the
market spread presents an
opportunity for any market
participant. A competitive
market maker quotes fairly
tight spreads and tries to get a good turnover by being tight on the
quotes, but still many competitive market makers are quoting a
bit wider than the minimum edge they require to make a trade.
If orders enter the market inside the spread but still would give a
market maker the required minimum edge there is an opportunity
for the market maker to trade and make a profit. Since the market
maker already has a good set up for pricing and quoting, starting a
market taking activity to capture further profit opportunities is fairly
straightforward.
In the liquid index options products discussed there is significant
competition to trade on the opportunities that are seen in the mar-
ket. Assuming that a market maker has a clear view on what edge
they want before an order should be classified as a trading oppor-
tunity, they need to determine if and in that case how to execute on
the opportunity. Do they want to vary the volume they’re executing
depending on the amount of edge they get? What is the lowest vol-
ume they require before executing at all (and disclose their trading
limits to the market)?
Once the identification of trading opportunities and how to execute
on them have been defined, the opportunity trading activity leads us
back to a similar discussion to that on hedging.
How does a market maker make sure to get trades on opportunities
detected by their electronic eye? To detect a trading opportunity as
fast as possible they need to get data (information on order enter-
ing the spread) as quickly as possible, identify and execute on the
opportunity as fast as possible to get the size they’re looking for
and reduce risk of someone else getting there before them.
Challenge – Getting as high execution rate as possible on detected
opportunities
A competitive market maker that does not
have a trading solution that meets the techni-
cal aspects of competitive market making is
exposed to a significant risk of slow requoting,
likely resulting in the market maker getting
picked-off on the quotes frequently.
”
“

5. Key success factors – The two main key
success factors for this challenge have
already been discussed in the quoting and
hedging sections: co-location of trading
decisions and the use of true algorithmic
trading solutions designed for low-latency
trading.
Analytics
Almost all trading decisions for a competi-
tive market maker are based on options
analytics giving fair values and risk for the
instruments traded and trading risk for the
positions in the book. Good analytics suit-
able for the options traded is therefore very
important. The four index products dis-
cussed are all of European style and are
priced using a Black&Scholes approach,
but a major challenge is how to model the
volatility surface.
A trader needs a volatility surface that re-
flects the trader’s view on the market and that gives correct fair
values and trading risk when the market moves. Managing a vola-
tility surface for EURO STOXX 50® is challenging due to the nar-
row options spreads in terms of implied volatility and the frequently
moving futures market.
Starting with the actual shape of the volatility curve the first ques-
tion is what level of flexibility the volatility model should have.
Should it be more rigid, limiting the impact of minor local variations
in implied volatility, as typically seen in parameter based volatility
models or is a higher degree of flexibility required as typically seen
in spline based volatility models? Both approaches (and also a mix
of them) are used in European index options market making.
The real difficulty in managing volatilities is to capture the dynam-
ics of the volatility curve when the index future moves. Should the
volatility model be based on sticky-strike, sticky-delta or some
other strike parameterization like standard deviation or a variation
of log moneyness? Should the shape of the curve be changed in ad-
dition to the natural change imposed by the chosen strike param-
eterization and in that case how should it change? These questions
are truly challenging to answer and require a lot of experience of
the specific index product traded before building an own view on
the dynamic behavior of the volatility curve.
With a view on the strike parameterization and the shape of the
curve when the market moves we would be at our goal if implied
volatilities didn’t have memory. The at-the-money (ATM) volatility
path is frequently discussed, i.e. the way the at-the-money volatil-
ity varies with price in the underlying future. Assuming the front
month EURO STOXX 50® future has been trading at 2900 for some
time and still does; what is your ATM volatility path? Or more spe-
cifically, if the future moves to 3100 what is your ATM volatility? It is
tempting to do a quick approximation in one’s head based on the
curve at 2900 and answer something like, “Well, that’s 200 points
on the future that should lower the ATM volatility by roughly two
points.” Now, is that really how the question should be answered?
Not likely, since implied volatilities tend to have memory and take
history into account. To give a more educated
estimate of the ATM volatility when the future
is trading at 3100 there is one piece of infor-
mation missing, the way we got to 3100. Was
there a fairly quiet trending from 2900 to 3100
during the cause of the day or was there a sud-
den major upwards move to 3300 followed by a
rapid decline to 3100? Even though we end up
at 3100 in both scenarios it is unlikely that the
implied volatility level at 3100 will be the same
and in this respect implied volatilities tend to
take history into account.
Challenge – Volatilities reflecting traders view
on volatility in a moving market resulting in ac-
curate pricing and risk at all times
Key success factors – A trading strategy adopt-
able to the speed of the market and a volatility
model that works well for the dynamics of the
implied volatilities in the product traded.
Index options market making – a case study
Caerus Trading BV is a market making firm in equity and index
options providing liquidity in the Dutch and French derivatives mar-
kets. As a young and dynamic company Caerus Trading aims to be-
come a leading trading firm on several exchanges with initial focus
on NYSE Liffe Amsterdam and Paris.
“Our goal is to fairly quickly become a top ten market maker on the
exchanges where we are members,” says Bas Walraven, Partner
at Caerus Trading. “Today that means NYSE Liffe Amsterdam and
Paris, but we expect to expand significantly in terms of markets
traded in the future.”
Caerus Trading deployed an automated market making solution,
Orc Trading for Market Making, in the beginning of 2010 and are
actively market making since February 22. In an interview with Bas
Walraven he comments on what puts them in a good position to
stay competitive in today’s markets.
Starting with the key concern questions discussed earlier in this
article:
Do your quotes reflect your trading view at all times? Do you get the
hedges you should? Do you get the trading opportunities your elec-
tronic eye detects?
“Building a good strategy to use in competitive market making is
a major task and flexibility in the algorithmic trading engine is key
to success,” says Walraven. “When looking at the market making
solutions available among ISVs, Orc proved to have the most flex-
ible algorithmic trading engine and at the same time it makes us
very competitive in terms of latency when trading on opportunities
or hedging options trades. Looking at our market making activity
the answer is yes to all three questions.”
On the analytics aspect of market making Walraven comments:
“The fact that we also can do our trading risk management in a
good way in Orc gives us a complete trading solution for competi-

6. www.orcsoftware.com Please visit our website
for more information
Take Advantage
tive market making, but still we will continue to improve our market
making activity. One example is that we will do significant research
in terms of volatility modeling for index options going forward and
since we can add our own volatility model to Orc we know that we
will be able to fully profit from our findings in that work.”
After discussing needs, cha­ll­enges and key success factors in
competitive market making and also looking at a case study we
might still not be able to answer the questions posed in the ini-
tial section of this article. Many of the key success factors in
competitive index options market making are related to technol
ogy, which means a market maker not only needs to excel in terms
of options trading but also has to have the right IT development
team and technology. Today market makers compete on several
levels and market making firms need to make significant invest-
ments in IT to make sure their traders get enabled by the technol-
ogy used in the market making activity.
Would a successful competitive market maker in European index
options today survive one day in the pits 20 years ago? It’s not cer-
tain, but market makers today are a tough breed and after some
time in the pit they would likely not only adapt but excel.
When looking at the market making solutions available among
ISVs, Orc proved to have the most flexible algorithmic trading engine
and at the same time it makes us very competitive in terms of latency
when trading on opportunities or hedging options trades.
”
“