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Will Rhode, TABB Group, US Swaps: The Future is Emerging, here.
Among the key action items we conclude that:
- Dealers need to continue competing for swaps clearing market share. Not only is clearing set to become a healthy revenue stream in its own right but we see an implicit decision by the buy side to send swaps execution flow to their clearing broker;
- In the face of shrinking ticket sizes and lower volumes, dealer revenues will come under pressure with the introduction of SEFs. Each bank will need to decide on how it chooses to implement market share strategies, whether it be via relationships and the phone, via SEFs and algorithmic pricing, or some hybrid of the two;
- The disintermediation effect of Dodd-Frank notwithstanding, dealer relationships with the buy side will continue to be key. The trick will be in knowing where the relationship really lies, across the array of potential business lines and myriad of individuals, and understanding why a buy side firm wants to engage.
Robert Almgren, Quantitative Brokers, Market Microstructure, Quantitative Trading and Interest Rate Markets, here.
Fixed income modeling is a well-established, extensive area of quantitative research. But most of that research focuses on pricing of the products themselves and their interrelationships via yield curves and the like. Here we want to point out some of the features specific to how these products trade in real markets that make them fertile subjects for ongoing quantitative research.
1. Futures products in general trade on single exchanges: competing products on different exchanges are not fungible. And the vast majority of trade activity is now electronic rather than in the pit; market data is extremely accurate and reliable. This is in contrast to equity data, where it can be a challenge to match trades against prevailing quotes.
2. Interest rate products are strongly affected by macroeconomic information announcements such as employment, inflation, etc., as well as by government bond auctions. Unlike equity earnings announcements, these information events happen in the middle of the trading day. To trade these products effectively, it is necessary to have a good understanding of which events are important, and how they will affect different markets. As an example, one finds that US information announcements have strong effects on US and European markets, but European announcements have very small effects on US markets.
3. Interest rates at different maturities are very strongly interrelated, much more than any pair of stocks. The markets are inherently multidimensional. Even if one is trading only a single contract, one must take account of the entire universe. Co-integration, a well-studied but rarely observed property of financial time series, is ubiquitous in this space.
4. Also because of the interrelationships, spread products and basis algorithms are extremely important. One may not have a belief about whether rates will go up or down, but one may be willing to bet on a change in the slope or curvature of the yield curve. Futures exchanges offer a variety of calendar spread contracts, as well as more complicated combinations. These spreads can give rise to “implied liquidity,” which is an important resource for trading. That is, an order in a particular maturity may be filled by a combination of different maturities and spreads.
5. Because short-term products generally have low volatility and large spreads, exchanges commonly use matching algorithms that are more complicated than time priority. For example, in pro rata matching, an incoming market order is matched against every resting limit order at the best price, independent of time of submission but in proportion to the size of the limit order. This greatly changes the optimal strategies for limit order placement, and the market dynamics following large trades. Some exchanges experiment with combinations of pro rata and time priority for products of medium duration, and the actual rules can become quite complex.
Order matching Algos, Algorithmic Trading – an Introduction, here.
Matthew Leising, Bloomberg, Banks Poised to Reduce Rate-Swap Trading as Revenue Seen Reduced, here.
Dealer revenue from negotiating interest-rate swap transactions is poised to plunge about 45 percent as new rules boost trading costs, pressures that may prompt banks to participate less in the $633 trillion over-the-counter derivatives market, Tabb Group LLC estimates.
Banks collect about $3.25 billion a year from trading rate swaps with their customers, Tabb said. That revenue will shrink to $1.8 billion in 2014 as most transactions shift to public markets, according to a research report meant for Tabb customers that Bloomberg News obtained. Dealers will also need to hold more capital to back trades, boosting expenses, said Will Rhode, who wrote the report.
Felix Salmon, Reuters, The truth about Blackstone and Cordere, here.
But that number is gross revenue, not profit. The profit on Blackstone’s CDS position can be looked at as being the difference between that payout, on the one hand, and the amount that it spent buying the CDS in the first place, on the other. (Although in fact, as we’ll see, it’s more complicated than that.) Unless we have some idea of Blackstone’s cost basis on this trade, we have no idea what its profit was. Bloomberg, however, seems to simply assume that Blackstone’s cost basis for the CDS was zero — that it managed to accumulate all that insurance without paying anything for it whatsoever.
To be sure, Blackstone are smart operators, and I don’t doubt that they’re making a profit on this trade. But we really have no idea how big that profit was.
Michael J. Moore & Dakin Campbell, Bloomberg, Wall Street Sweats Out Volcker Rule Impact on Revenue, here.
Wall Street banks, which already shut proprietary trading units that helped fuel record profits, are girding to learn next week how much revenue the Volcker rule may cut from the $44 billion they say comes from market-making.
With U.S. regulators scheduled to vote Dec. 10, the largest firms are getting little detail about the final terms of the Volcker rule’s ban on proprietary trades, and still have basic questions about what kind of market-making will be allowed, said three senior U.S. bankers. They’re also wondering whether they’ll have to change practices or curtail business in some less-liquid markets, the bankers said.
Matt Levine, Bloomberg, EU Is Shocked That Banks Colluded on Libor, here.
So the banks got together and decided: Let’s create a composite of our borrowing costs and all sell swaps against that composite. We’re all talking to each other anyway as we go about borrowing from each other, so let’s all just write down how much we’re paying to borrow, send our costs to a trade association, take a trimmed average, call it the London interbank offered rate, and write all our swaps against Libor. We can even set up a different trade association to make sure that we all have the same documents for our swaps, so that all our swaps work the same and use the same rate that we all more or less agree on.
So they did that, and it was great. I mean, it was, for them. You can complain because Libor has fallen into some disrepute of late, and they can complain because “fallen into disrepute” really means “has racked up some enormous fines for Libor banks,” but I don’t want to hear it from any of you. U.S. banks — U.S. banks alone – made $2.8 billion just last quarter from trading interest rate derivatives. That decision to have a standardized thing that they all agreed on as the basis for those derivatives worked out just plain great for them.
Felix Salmon, Reuters, The $5 trillion dilemma facing banking regulators, here.
Last month, I wrote about bond-market illiquidity — the problem that it’s incredibly difficult to buy and sell bonds in any kind of volume, especially if they’re not Treasuries. That’s a big issue — but it turns out there’s an even bigger issue hiding in the same vicinity.
The problem is that there’s only a certain amount of liquidity to go around — and under Dodd-Frank rules, a huge proportion of that liquidity has to be available to exchanges and clearinghouses, the hubs which sit in the middle of the derivatives market and act as an insulating buffer, making sure that the failure of one entity doesn’t cascade through the entire system.
Craig Pirrong has a good overview of what’s going on, and I’m glad to say that Thomson Reuters is leading the charge in terms of reporting about all this: see, for instance, recent pieces by Karen Brettell, Christopher Whittall, and Helen Bartholomew. The problem is that it’s not an easy subject to understand, and most of the coverage of the issue tends to assume a lot of background knowledge. So, let me try to (over)simplify a little.
Fergus Perry, Citi, Collateral Optimization, here.
Recent industry papers have estimated the additional collateral burden demanded by the Dodd-Frank and European Market Infrastructure Regulation (EMIR) legislation to be over $2 trillion globally.1 A major component of this increase results from the progressive move to central clearing of OTC derivatives and the corresponding requirement for all market participants to post initial margin to the central counterparties (CCPs) as part of the protection required by the CCPs against broker default.
Ari Balogh, Google Developers Blog, Google Compute Engine is now Generally Available with expanded OS support, transparent maintenance, and lower prices, here. Curious how much of the floating point instruction execution capacity is preserved when you execute your optimized code through a VM. A couple of the benchmarks that I came across on google compile some code -O2 and then present the Non-VM versus the VM performance. Not necessarily the most illuminating test. Other benchmarks simply show the relative performance of various VMs doing executing some floating point code. Again does not really give a handle on the problem.
New 16-core instances
Developers have asked for instances with even greater computational power and memory for applications that range from silicon simulation to running high-scale NoSQL databases. To serve their needs, we’re launching three new instance types in Limited Preview with up to 16 cores and 104 gigabytes of RAM. They are available in the familiar standard, high-memory and high-CPU shapes.
James Sweeney, Credit Suisse, Reshaping the Financial System, here. Starts on page 51.
Shadow banking has survived the crisis. But the financial system is being reshaped, just as the commercial banking system was in the 1930s. In order to understand the evolution now under way, it is helpful to focus first on how the mid-century bank-based financial system created liquidity.
Felix Salmon, Reuters, The government-dominated bond market, here.
JP Morgan’s Nikolaos Panigirtzoglou put a fascinating report out last week, looking at supply and demand in the global bond market in 2014. And although I consider myself something of a bond nerd, I was genuinely astonished by some of the charts he put together, starting with this one:
Manoj Narang, Tradeworx, Inc. Public Commentary on SEC Market Structure Concept Release, Apr 2010, here. Fairly singular presentation, that I haven’t seen before. Must be the outline for the book?
BASIC ECONOMICS OF HFT IN US EQUITY MARKET
net profit margin: approx 10 mils per share (i.e. 0.1 cents per share) source: Tradeworx high-frequencyproprietary trading,year 2009, source: Traders Magazine Oct 2005 Q&A With Dave Cummings, source: Knight Trading (Nasdaq: NITE) 2009 10K filing
brokerage fees: 0.25 – 5 mils per share – source: Tradeworx, various brokerage firms
sec fees: 5.4 mils / share - assumes rate of $16.90 per million - assumes average stock price: 63.8 (stock prices weighted by quarterly dollar volume, as of 3/19/2010)
average trading market share: 40% – source: Tradeworx estimate; industry estimates range from 30% – 60%
total annual profits: $2Bn / yr - assumes: 8 Bn shares / day of HF volume: 40% market share * 10 Bn shares/day * 2 assumes 10 mils/share of net profit (8B shares) X (10 mils per share) equals $8M/day of profit ($8M per day) X (250 trading days) equals $2Bn per year
Matt Levine, Bloomberg, McKinsey Tells Banks to Focus on Making Money, here.
One of my great skills as an investment banker was saying no. Clients or other bankers would come to me and say “oh, can we do this ridiculous thing?” and I would say “no” or “no you dummy” or “that’s a really interesting idea, let me take it back to my team,” or whatever the context-appropriate version of “no” was. And then the ridiculous idea would never darken my door again and I could get back to the important business of pitching people on my ridiculous ideas.