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Snowflake Index Fund and AppNexus Engineering@Scale

Matt Levine, Bloomberg, Financial Innovation Is Depressing, here.  Wow, Levine got Bloomberg to give him actual footnotes.

DealBook has a special section today on ideas and innovation on Wall Street and let’s just say it will not inspire too many idealistic Stanford undergrads to stop work on their iPhone apps and take that financial-engineering job at Wells Fargo. It’s all pretty sad stuff.

My favorite article in the section is of course the one on innovation in fraud, which it turns out is genuinely fertile ground for creativity, though tell that to the Stanford kids. To be fair, though, there is also innovation in catching fraud, specifically a Commodity Futures Trading Commission Rule, adopted in 2011, that outlaws market manipulation. So! Nice work there CFTC.

Favorite footnote so far –

3 Provably false in the aggregate, but always possible for you. You don’t need to settle for average returns, you are a special snowflake, buy our snowflake index fund, etc.

Jessica, appnexus tech blog, AppNexus Engineering@Scale: Building & Shipping a Scalable Product, here.

Few people are more familiar with website scalability problems than Theo Schlossnagle. Not only is Theo the founder and CEO of OmniTI, he is also the author of Scalable Internet Architectures, a book that draws on his 15 years of experience to provide developers with a blueprint for tackling the biggest obstacles to successful scaling. Theo shared his wisdom in a recent AppNexus Engineering@Scale talk.

This 20Nov talk looks interesting as well:

TestOps: Continuous Integration when Infrastructure is the Product
An AppNexus Engineering @ Scale Conversation Series

Join us November 20th for “TestOps: Continuous Integration when Infrastructure is the Product” presented by Barry Jaspan, Senior Architect of Acquia. Continuous Integration and Deployment are powerful approaches for improving software development and release engineering. However, when your product is infrastructure running other people’s applications instead of just your own, different problems arise.

Releases involve reconfiguring daemons and servers, possibly restarting them. Updates must be carefully choreographed to maintain high availability. Upgrading 6000+ servers “at once” is impossible, so running multiple versions simultaneously is required. Rolling back is  difficult, so automated testing is critical. With the rise of configuration management systems like Puppet and Chef, server configuration is now software. Like all software, server configuration needs constant automated testing in order to work, but testing infrastructure-as-software is a substantially different problem from testing normal application software.

puppet labs, What is Puppet? here.

Puppet is IT automation software that helps system administrators manage infrastructure throughout its lifecycle, from provisioning and configuration to orchestration and reporting. Using Puppet, you can easily automate repetitive tasks, quickly deploy critical applications, and proactively manage change, scaling from 10s of servers to 1000s, on-premise or in the cloud.

Opscode, Chef, How Chef Works, here.

Chef is based on a key insight: You can model your evolving IT infrastructure and applications as code. Chef makes no assumptions about your environment and the approach you use to configure and manage it. Instead, Chef gives you a way to describe and automate your infrastructure and processes. Your infrastructure becomes testable, versioned and repeatable. It becomes part of your Agile process.

Peter Wayner, InfoWorld, Puppet or Chef: The configuration management dilemma, here.

Thank goodness for automation. Over the years, smart sys admins looked at the ballooning task list and figured out a way to write scripts that would handle the repetitive tasks. They built their own junior robot sys admin to do the work for them.

The hard work has coalesced into two major factions called Puppet and Chef. There are a number of other notable projects with readable names like Ansible and unreadable names like Bcfg2, but Puppet and Chef seem to have gathered the most excitement for now.

Both are open source stacks of code designed to make it easy for you to reach out and touch the files in your vast empire of virtual machines. Both have open source marketplaces for you to swap plug-ins that extend the framework and handle your particular type of hardware or software. Both are pretty cool, and both are finding homes in the racks of data centers around the world. Both now have companies built around the open source core selling assistance.



Hull and White

John Hull and Alan White, Using Hull-White Interest-Rate Trees, here. So you need this in Cont et. al. for the contingency of collateral posting on the variation margin.

The Hull-White tree building procedure is a flexible approach to constructing trees for a wide range of different one-factor models of the term structure. The tree is constructed in such a way that it is exactly consistent with the initial term structure. In this article we have shown how the basic procedure presented in our earlier paper can be extended. Some of these extensions involve the use of analytic results and some involve changing the geometry of the tree to reflect special features of the derivative under consideration. We have devoted some time in this article to a discussion of what happens when the volatility parameters are made time-dependent. It not difficult to extend the Hull-White tree to incorporate time-dependent parameters so that the prices of caps or swap options (or both) are matched. However, this is liable to result in unacceptable assumptions about the evolution of volatilities.

FFT in Finance

A. Cerny, Introduction to Fast Fourier Transform in Finance, here.

Abstract. The Fourier transform is an important tool in Financial Economics. It delivers real time pricing while allowing for a realistic structure of asset returns, taking into account excess kurtosis and stochastic volatility. Fourier transform is also rather abstract and therefore off-putting to many practitioners. The purpose of this paper is to explain the working of the fast Fourier transform in the familiar binomial option pricing model. We argue that a good understanding of FFT requires no more than some high school mathematics and familiarity with roulette, bicycle wheel, or a similar circular object divided into equally sized segments. The returns to such a small intellectual investment are overwhelming.

Carr and Madan, Option valuation using the fast Fourier transform, here.

The Black±Scholes model and its extensions comprise one of the major developments in modern ®nance. Much of the recent literature on option valuation has successfully applied Fourier analysis to determine option prices (see e.g. Bakshi and Chen 1997, Scott 1997, Bates 1996, Heston 1993, Chen and Scott 1992). These authors numerically solve for the delta and for the risk-neutral prob- ability of ®nishing in-the-money, which can be easily combined with the stock price and the strike price to generate the option value. Unfortunately, this approach is unable to harness the considerable computational power of the fast Fourier transform (FFT) (Walker 1996), which represents one of the most fundamental advances in scientific computing. Furthermore, though the decomposition of an option price into probability elements is theoretically attractive, as explained by Bakshi and Madan (1999), it is numerically undesirable owing to discontinuity of the payoff€s.


Automation Anxiety, Shadow Banking

Daniel Akst, The Wilson Quarterly, Automation Anxiety, here.

In Ulysses (1922), it’s been said, James Joyce packed all of life into a single Dublin day. So it shouldn’t be surprising that he found room in the novel for Leopold Bloom to tackle the problem of technological disruption:

A pointsman’s back straightened itself upright suddenly against a tramway standard by Mr Bloom’s window. Couldn’t they invent something automatic so that the wheel itself much handier? Well but that fellow would lose his job then? Well but then another fellow would get a job making the new invention?

Notice Bloom’s insights: first, that technology could obviate arduous manual labor; second, that this would cost somebody a job; and third, that it would also create a job, but for a different person altogether.

Pozsar, Federal Reserve Bank of New York, Shadow Banking, here.

The rapid growth of the market-based financial system since the mid-1980s has changed the nature of financial intermediation. Within the system, “shadow banks” have served a critical role, especially in the run-up to the recent financial crisis. Shadow banks are financial intermediaries that conduct maturity, credit, and liquidity transformation without explicit access to central bank liquidity or public sector credit guarantees. This article documents the institutional features of shadow banks, discusses the banks’ economic roles, and analyzes their relation to the traditional banking system. The authors argue that an understanding of the “plumbing” of the shadow banking system is an important underpinning for any study of financial system interlinkages. They observe that while many current and future reform efforts are focused on remediating the excesses of the recent credit bubble, increased capital and liquidity standards for depository institutions and insurance companies are likely to heighten the returns to shadow banking activity. Thus, shadow banking is expected to be a significant part of the financial system, although very likely in a different form, for the foreseeable future.


Starving the Squid

Brad DeLong, Project Syndicate, Starving the Squid, here. Frame automated trading as alpha  driving economic growth. The technology employed and it’s features is a side-show of debatable value. Rebuild locally, from the ground up, with Mercer County Cash Register.

Such a massive diversion of resources “away from goods and services directly useful this year,” I argued, “is a good bargain only if it boosts overall annual economic growth by 0.3% – or 6% per 25-year generation.” In other words, it is a good bargain only if it collectively has a substantial amount of what financiers call “alpha.”

That had not happened, so I asked why so much financial skill and enterprise had not yielded “obvious economic dividends.” The reason, I proposed, was that “[t]here are two sustainable ways to make money in finance: find people with risks that need to be carried and match them with people with unused risk-bearing capacity, or find people with such risks and match them with people who are clueless but who have money.”

Macro Man, Jeff Birnbaum Talk, Jeff Gundlach Slides

Macro Man, about/faqs, here. Solid.

Q. Why do you write the blog?

A. A number of reasons, but we have found the discipline required to set down thoughts on a daily basis, subjected to public scrutiny, to be highly useful. It provides an archive of our thinking at any particular point in time, and enforces a quality control of ideas. The comments section has proven to be a valuable resource, both in terms of providing feedback to our own thinking and in the ideas that readers occasionally share: a few of the latter have ended up in our portfolios. Finally, it provides a forum for the occasional rant, which is always useful to let off steam.

Jeff Birnbaum, 60 East Technologies, AMPS, here. hat tip sp.

Join our CEO, Jeffrey M. Birnbaum, on June 10th, 2013 as he delves into the world of big data and high performance covering the cutting edge of CPUs, Storage, and networking.

Sam Ro and Rob Wile, BI, What in the World is Going On? here. My Treasury position needs some improvement.

“Something happened in the middle of May,” said investing god Jeff Gundlach as he began his latest webcast on the state of the global markets and the economy.

He was referring to how global interest rates quietly rallied and how the Japanese stock market fell spectacularly.

He notes that the magnitude of the interest rate rally isn’t unusual.  Having said that, Gundlach believes rates will stay low thanks to a “put” by the Federal Reserve. Should rates rise, Gundlach believes the Fed would actually expand quantitative easing. This is because high interest rates would put too much pressure on the economy, and it would cause Federal interest expenses to become too onerous.

“I certainly think the Fed is going to reduce quantitative easing,” he said. But he attributes the reduction to the shrinking Federal deficit.

“I’m starting to like long-term Treasuries,” said Gundlach as he predicted the 10-year Treasury yield would end the year at 1.7%.


Swap Futures Patents and Swap Valuation Conventions

Cameron and Wood,, CME’s new swap future uses Goldman Sachs patent, here.

The patent for the product – Method and apparatus for listing and trading a futures contract that physically settles into a swap – was filed on June 12, 2007 by Oliver Frankel, a managing director in the securities division at Goldman Sachs in New York, and was granted on April 19 last year. The arrangement between CME Group and Goldman Sachs had been the subject of rumours among dealers in the weeks leading up to the announcement of the contract. CME confirmed to Risk it had licensed the patent from Goldman Sachs, but declined to comment on the fee. Goldman’s Frankel did not respond to requests for comment.

Google, Method and apparatus for listing and trading a futures contract that physically settles into a swap, here. See if anyone did this in ETF form then roll the OTC contracts and the portfolio of OTC contracts into the ETF patent. File, troll, repeat. This might only be something that works for CME futures since the CME has such a vested interest in non-fungible securities.

According to some embodiments, a futures contract is listed on a futures trading exchange. The futures contract physically settles upon expiration into a reference swap. The reference swap is cleared by a clearing house so that the physical settlement requires that the holder of a position in the futures contract upon expiration takes a specified side of the reference swap against the clearing house. The reference swap may for example be a credit default index swap, a single-name credit default swap, an interest rate swap or a yield curve swap.

Marco Bianchetti, Two Curves, One Price, here. Avellaneda would know more precisely where the exact valuation conventions are at this point. I see Bruce Tuckman is at NYU as well – he would be on top of this. Probably makes sense to estimate the computational cost on Haswell for IR swap curve construction and valuation.

We revisit the problem of pricing and hedging plain vanilla single-currency interest rate derivatives using multiple distinct yield curves for market coherent estimation of discount factors and forward rates with different underlying rate tenors.

Within such double-curve-single-currency framework, adopted by the market after the credit-crunch crisis started in summer 2007, standard single-curve no-arbitrage relations are no longer valid, and can be recovered by taking properly into account the forward basis bootstrapped from market basis swaps. Numerical results show that the resulting forward basis curves may display a richer micro-term structure that may induce appreciable effects on the price of interest rate instruments.

Marc Henrard, The Irony in the Derivatives Discounting Part ii: The Crisis, here.

Libor derivative pricing has changed with the crisis; Libor is not anymore one unambiguous curve as a large basis has appeared between different Libor tenors. A previous approach to derivative discounting is reviewed at the light of those changes. The valuation of so called linear derivatives, the yield curve construction and the valuation of vanilla options is analyzed.

Liquidity Trap, Number Theory News, and WDML

Paul Krugman, Brookings, It’s Baaack: Japan’s Slump and the Return of the Liquidity Trap, here.

THE LIQUIDITY TRAP-that awkward condition in which monetary policy loses its grip because the nominal interest rate is essentially zero, in which the quantity of money becomes irrelevant because money and bonds are essentially perfect substitutes-played a central role in the early years of macroeconomics as a discipline. John Hicks, in intro- ducing both the IS-LM model and the liquidity trap, identified the assumption that monetary policy is ineffective, rather than the assumed downward inflexibility of prices, as the central difference between Mr. Keynes and the classics. ‘ It has often been pointed out that the Alice in Wonderland character of early Keynesianism-with its paradoxes of thrift, widows’ cruses, and so on-depended on the explicit or implicit assumption of an accommodative monetary policy; it has less often been pointed out that in the late 1930s and early 1940s it seemed quite natural to assume that money was irrelevant at the margin. After all, at the end of the 1930s interest rates were hard up against the zero con- straint; the average rate on U.S. Treasury bills during 1940 was 0.014 percent.

Peter Woit, Not Even Wrong, Number Theory News, here. Woit’s link to Caroline Chen’s The Paradox of the Proof (below) is pretty good for a Mochizuki/ABC conjecture summary and update.

A special seminar has been scheduled for tomorrow (Monday) at 3pm at Harvard, where Yitang Zhang will present new results on “Bounded gaps between primes”. Evidently he has a proof that there exist infinitely many different pairs of primes p,q with p-q less than17,000,000 70,000,000.

Whether this proof is valid should become clear soon, but there still seems to be nothing happening in terms of others understanding Mochizuki’s claimed proof of the abc conjecture. For an excellent article describing the situation, see here.

Ingrid Daubechies, What’s new, Planning for the World Digital Math Library, here.

This guest blog entry concerns the many roles a World Digital Mathematical Library (WDML) could play for the mathematical community worldwide. We seek input to help sketch how a WDML could be so much more than just a huge collection of digitally available mathematical documents. If this is of interest to you, please read on!

Moby Ben

Brad DeLong, Grasping Reality, Moby Ben, or, the Washington Super-Whale: Hedge Fundies, The Federal Reserve, and Bernanke-Hatred, here. So, when History is written about the London Whale – he was just on the wrong side of a convergence trade in CDX IG 9 identified by a bunch of smart hedge fund folks. I guess that’s what they mean when they say, by not knowing history you are condemned to repeat it?

In February 2012, a number of hedge fund traders noted one particular index–CDX IG 9–that seemed to be underpriced. It seemed to be cheaper to buy credit default protection on the 125 companies that made the index by buying the index than by buying protection on the 125 companies one by one. This was an obvious short-term moneymaking opportunity: Buy the index, sell its component short, in short order either the index will rise or the components will fall in value, and then you will be able to quickly close out your position with a large profit.

But February passed, and March passed, and April rolled in, and the gap between the price of CDX IG 9 and what the hedge fund traders thought it should be grew. And their bosses asked them questions, like: “Shouldn’t this trade have converged by now?” “Have you missed something?” “How much longer do you want to tie up our risk-bearing capacity here?” “Isn’t it time to liquidate–albeit at a loss?”

So the hedge fund traders began asking who their counterparty was. It seemed that they all had the same counterparty. And so they began calling their counterparty “the London Whale”. They kept buying. And the London Whale kept selling. And so they had no opportunity to even begin to liquidate their positions and their mark-to-market losses grew, and the risk they had exposed their firms to grew.

So they got annoyed.

And they went public, hoping that they could induce the bosses of the London Whale to force him to unwind his possession, in which case they would profit immensely not just when the value of CDX IG 9 returned to its fundamental but by price pressure as the London Whale had to find people to transact with. And so we had ‘London Whale’ Rattles Debt Market, and similar stories

The London Whale was Bruno Iksil. He had been losing, and rolling double or nothing, and losing again for months. His boss, Ina Drew, took a look at his positions. They found they had a choice: they could hold the portfolio and thus go all-in, or they could fold. They could hold CDX IG 9 until maturity–make a fortune if a fewer-than-expected number of its 125 companies went bankrupt, and lose J.P. Morgan Chase entirely to bankruptcy if more did. Or they could take their $6 billion loss and go home. They could either take their losses, or sing “Luck, Be a Lady Tonight!” and bet J.P. Morgan Chase on a single crapshoot. After all, what could they do if the bet went wrong and they had to eat losses at maturity? J.P. Morgan Chase couldn’t print money. So Drew stood Iksil down, and the hedge fund traders had their happy ending.

Apple Bonds, CBOE Shutdown, CFTC, and Algo P&L

Matt Levine, DealBreaker, Apple Sold Some Bonds, here.

So those rates are low, no? Assuming a 35% tax rate hahahah, the after-tax cost of the 30-year is around 2.5%, and the average after-tax cost is around 1.25%, compared to Apple’s (entirely after-tax) dividend yield of 2.75%.2 So every $1,000 bond they issue to buy back stock saves them around $15 a year in cash that they’d otherwise be paying out to stockholders.

Jon Shazar, DealBreaker, The CBOE Will Make Up Those Three-Plus Hours To You – And Then Some, here.

The Chicago Board Options Exchange said preparations to extend trading hours led to last week’s shutdown of one of the largest U.S. stock-options markets, even though staff were aware of potential problems ahead of time.

The exchange operator said Monday in a note to clients that an internal review left it “fully confident” that it had addressed the software bug, though it would be assessing how it handled the 3½-hour outage last Thursday….

CBOE said the root cause of the outage lay in “preliminary staging work” in preparation for longer trading hours on CBOE’s futures and options markets, according to the notice sent by CBOE executives….

The staging work being done at CBOE “exposed and triggered a design flaw in the existing messaging infrastructure configuration,” according to the notice. People briefed on the CBOE’s investigation said late last week that the problem was seen related to the exchange’s software for the handling of complex, multipart options orders. A spokeswoman for CBOE declined further comment Monday.

Eric Lipton, NYT, Banks Resist Strict Controls of Foreign Bets, here.

Banks and overseas regulators are resisting an agency proposal, intended to go into full effect as early as mid-July, that would require overseas offices of American-based banks, foreign institutions and hedge funds to turn over information on foreign trades if they involve United States customers, or are guaranteed by a financial institution with American ties, requirements that the industry calls redundant and excessive.

Kissel and Malamut, JPM,  Feb 2005, Understanding the Profit and Loss Distribution of Trading Algorithms, here.

To best understand the algorithmic deci- sion making process, however, it is im- portant to understand the basics behind transaction cost management. Seminal transaction cost research is primarily due to Treynor (1981), Perold (1988), Ber- kowitz, Logue, & Noser (1988), Wagner (1990), and Hasbrouck (1991). More re- cently, however, Bertsimas & Lo (1996), Almgren & Chriss (1999), and Kissell, Glantz, and Malamut (2004) expanded this work to provide a decision making framework to manage transaction costs. Accordingly, this work now serves as the basis for algorithmic decision making.