Long Term Capital Management. Note Quote.

Long Term Capital Management, or LTCM, was a hedge fund founded in 1994 by John Meriwether, the former head of Salomon Brothers’s domestic fixed-income arbitrage group. Meriwether had grown the arbitrage group to become Salomon’s most profitable group by 1991, when it was revealed that one of the traders under his purview had astonishingly submitted a false bid in a U.S. Treasury bond auction. Despite reporting the trade immediately to CEO John Gutfreund, the outcry from the scandal forced Meriwether to resign.

Meriwether revived his career several years later with the founding of LTCM. Amidst the beginning of one of the greatest bull markets the global markets had ever seen, Meriwether assembled a team of some of the world’s most respected economic theorists to join other refugees from the arbitrage group at Salomon. The board of directors included Myron Scholes, a coauthor of the famous Black-Scholes formula used to price option contracts, and MIT Sloan professor Robert Merton, both of whom would later share the 1997 Nobel Prize for Economics. The firm’s impressive brain trust, collectively considered geniuses by most of the financial world, set out to raise a $1 billion fund by explaining to investors that their profoundly complex computer models allowed them to price securities according to risk more accurately than the rest of the market, in effect “vacuuming up nickels that others couldn’t see.”

One typical LTCM trade concerned the divergence in price between long-term U.S. Treasury bonds. Despite offering fundamentally the same (minimal) default risk, those issued more recently – known as “on-the-run” securities – traded more heavily than those “off-the-run” securities issued just months previously. Heavier trading meant greater liquidity, which in turn resulted in ever-so-slightly higher prices. As “on-the-run” securities become “off-the-run” upon the issuance of a new tranche of Treasury bonds, the price discrepancy generally disappears with time. LTCM sought to exploit that price convergence by shorting the more expensive “on-the-run” bond while purchasing the “off- the-run” security.

By early 1998 the intellectual firepower of its board members and the aggressive trading practices that had made the arbitrage group at Salomon so successful had allowed LTCM to flourish, growing its initial $1 billion of investor equity to $4.72 billion. However, the miniscule spreads earned on arbitrage trades could not provide the type of returns sought by hedge fund investors. In order to make transactions such as these worth their while, LTCM had to employ massive leverage in order to magnify its returns. Ultimately, the fund’s equity component sat atop more than $124.5 billion in borrowings for total assets of more than $129 billion. These borrowings were merely the tip of the ice-berg; LTCM also held off-balance-sheet derivative positions with a notional value of more than $1.25 trillion.


The fund’s success began to pose its own problems. The market lacked sufficient capacity to absorb LTCM’s bloated size, as trades that had been profitable initially became impossible to conduct on a massive scale. Moreover, a flood of arbitrage imitators tightened the spreads on LTCM’s “bread-and-butter” trades even further. The pressure to continue delivering returns forced LTCM to find new arbitrage opportunities, and the fund diversified into areas where it could not pair its theoretical insights with trading experience. Soon LTCM had made large bets in Russia and in other emerging markets, on S&P futures, and in yield curve, junk bond, merger, and dual-listed securities arbitrage.

Combined with its style drift, the fund’s more than 26 leverage put LTCM in an increasingly precarious bubble, which was eventually burst by a combination of factors that forced the fund into a liquidity crisis. In contrast to Scholes’s comments about plucking invisible, riskless nickels from the sky, financial theorist Nassim Taleb later compared the fund’s aggressive risk taking to “picking up pennies in front of a steamroller,” a steamroller that finally came in the form of 1998’s market panic. The departure of frequent LTCM counterparty Salomon Brothers from the arbitrage market that summer put downward pressure on many of the fund’s positions, and Russia’s default on its government-issued bonds threw international credit markets into a downward spiral. Panicked investors around the globe demonstrated a “flight to quality,” selling the risky securities in which LTCM traded and purchasing U.S. Treasury securities, further driving up their price and preventing a price convergence upon which the fund had bet so heavily.

None of LTCM’s sophisticated theoretical models had contemplated such an internationally correlated credit market collapse, and the fund began hemorrhaging money, losing nearly 20% of its equity in May and June alone. Day after day, every market in which LTCM traded turned against it. Its powerless brain trust watched in horror as its equity shrank to $600 million in early September without any reduction in borrowing, resulting in an unfathomable 200 leverage ratio. Sensing the fund’s liquidity crunch, Bear Stearns refused to continue acting as a clearinghouse for the fund’s trades, throwing LTCM into a panic. Without the short-term credit that enabled its entire trading operations, the fund could not continue and its longer-term securities grew more illiquid by the day.

Obstinate in their refusal to unwind what they still considered profitable trades hammered by short-term market irrationality, LTCM’s partners refused a buyout offer of $250 million by Goldman Sachs, ING Barings, and Warren Buffet’s Berkshire Hathaway. However, LTCM’s role as a counterparty in thousands of derivatives trades that touched investment firms around the world threatened to provoke a wider collapse in international securities markets if the fund went under, so the U.S. Federal Reserve stepped in to maintain order. Wishing to avoid the precedent of a government bailout of a hedge fund and the moral hazard it could subsequently encourage, the Fed invited every major investment bank on Wall Street to an emergency meeting in New York and dictated the terms of the $3.625 billion bailout that would preserve market liquidity. The Fed convinced Bankers Trust, Barclays, Chase, Credit Suisse First Boston, Deutsche Bank, Goldman Sachs, Merrill Lynch, J.P. Morgan, Morgan Stanley, Salomon Smith Barney, and UBS – many of whom were investors in the fund – to contribute $300 million apiece, with $125 million coming from Société Générale and $100 million from Lehman Brothers and Paribas. Eventually the market crisis passed, and each bank managed to liquidate its position at a slight profit. Only one bank contacted by the Fed refused to join the syndicate and share the burden in the name of preserving market integrity.

That bank was Bear Stearns.

Bear’s dominant trading position in bonds and derivatives had won it the profitable business of acting as a settlement house for nearly all of LTCM’s trading in those markets. On September 22, 1998, just days before the Fed-organized bailout, Bear put the final nail in the LTCM coffin by calling in a short-term debt in the amount of $500 million in an attempt to limit its own exposure to the failing hedge fund, rendering it insolvent in the process. Ever the maverick in investment banking circles, Bear stubbornly refused to contribute to the eventual buyout, even in the face of a potentially apocalyptic market crash and despite the millions in profits it had earned as LTCM’s prime broker. In typical Bear fashion, James Cayne ignored the howls from other banks that failure to preserve confidence in the markets through a bailout would bring them all down in flames, famously growling through a chewed cigar as the Fed solicited contributions for the emergency financing, “Don’t go alphabetically if you want this to work.”

Market analysts were nearly unanimous in describing the lessons learned from LTCM’s implosion; in effect, the fund’s profound leverage had placed it in such a precarious position that it could not wait for its positions to turn profitable. While its trades were sound in principal, LTCM’s predicted price convergence was not realized until long after its equity had been wiped out completely. A less leveraged firm, they explained, might have realized lower profits than the 40% annual return LTCM had offered investors up until the 1998 crisis, but could have weathered the storm once the market turned against it. In the words of economist John Maynard Keynes, the market had remained irrational longer than LTCM could remain solvent. The crisis further illustrated the importance not merely of liquidity but of perception in the less regulated derivatives markets. Once LTCM’s ability to meet its obligations was called into question, its demise became inevitable, as it could no longer find counterparties with whom to trade and from whom it could borrow to continue operating.

The thornier question of the Fed’s role in bailing out an overly aggressive investment fund in the name of market stability remained unresolved, despite the Fed’s insistence on private funding for the actual buyout. Though impossible to foresee at the time, the issue would be revisited anew less than ten years later, and it would haunt Bear Stearns. With negative publicity from Bear’s $38.5 million settlement with the SEC regarding charges that it had ignored fraudulent behavior by a client for whom it cleared trades and LTCM’s collapse behind it, Bear Stearns continued to grow under Cayne’s leadership, with its stock price appreciating some 600% from his assumption of control in 1993 until 2008. However, a rapid-fire sequence of negative events began to unfurl in the summer of 2007 that would push Bear into a liquidity crunch eerily similar to the one that felled LTCM.


High Frequency Markets and Leverage


Leverage effect is a well-known stylized fact of financial data. It refers to the negative correlation between price returns and volatility increments: when the price of an asset is increasing, its volatility drops, while when it decreases, the volatility tends to become larger. The name “leverage” comes from the following interpretation of this phenomenon: When an asset price declines, the associated company becomes automatically more leveraged since the ratio of its debt with respect to the equity value becomes larger. Hence the risk of the asset, namely its volatility, should become more important. Another economic interpretation of the leverage effect, inverting causality, is that the forecast of an increase of the volatility should be compensated by a higher rate of return, which can only be obtained through a decrease in the asset value.

Some statistical methods enabling us to use high frequency data have been built to measure volatility. In financial engineering, it has become clear in the late eighties that it is necessary to introduce leverage effect in derivatives pricing frameworks in order to accurately reproduce the behavior of the implied volatility surface. This led to the rise of famous stochastic volatility models, where the Brownian motion driving the volatility is (negatively) correlated with that driving the price for stochastic volatility models.

Traditional explanations for leverage effect are based on “macroscopic” arguments from financial economics. Could microscopic interactions between agents naturally lead to leverage effect at larger time scales? We would like to know whether part of the foundations for leverage effect could be microstructural. To do so, our idea is to consider a very simple agent-based model, encoding well-documented and understood behaviors of market participants at the microscopic scale. Then we aim at showing that in the long run, this model leads to a price dynamic exhibiting leverage effect. This would demonstrate that typical strategies of market participants at the high frequency level naturally induce leverage effect.

One could argue that transactions take place at the finest frequencies and prices are revealed through order book type mechanisms. Therefore, it is an obvious fact that leverage effect arises from high frequency properties. However, under certain market conditions, typical high frequency behaviors, having probably no connection with the financial economics concepts, may give rise to some leverage effect at the low frequency scales. It is important to emphasize that leverage effect should be fully explained by high frequency features.

Another important stylized fact of financial data is the rough nature of the volatility process. Indeed, for a very wide range of assets, historical volatility time-series exhibit a behavior which is much rougher than that of a Brownian motion. More precisely, the dynamics of the log-volatility are typically very well modeled by a fractional Brownian motion with Hurst parameter around 0.1, that is a process with Hölder regularity of order 0.1. Furthermore, using a fractional Brownian motion with small Hurst index also enables to reproduce very accurately the features of the volatility surface.


The fact that for basically all reasonably liquid assets, volatility is rough, with the same order of magnitude for the roughness parameter, is of course very intriguing. Tick-by-tick price model is based on a bi-dimensional Hawkes process, which is a bivariate point process (Nt+, Nt)t≥0 taking values in (R+)2 and with intensity (λ+t, λt) of the form


Here μ+ and μ are positive constants and the functions (φi)i=1,…4 are non-negative with associated matrix called kernel matrix. Hawkes processes are said to be self-exciting, in the sense that the instantaneous jump probability depends on the location of the past events. Hawkes processes are nowadays of standard use in finance, not only in the field of microstructure but also in risk management or contagion modeling. The Hawkes process generates behavior that mimics financial data in a pretty impressive way. And back-fitting, yields coorespndingly good results.  Some key problems remain the same whether you use a simple Brownian motion model or this marvelous technical apparatus.

In short, back-fitting only goes so far.

  • The essentially random nature of living systems can lead to entirely different outcomes if said randomness had occurred at some other point in time or magnitude. Due to randomness, entirely different groups would likely succeed and fail every time the “clock” was turned back to time zero, and the system allowed to unfold all over again. Goldman Sachs would not be the “vampire squid”. The London whale would never have been. This will boggle the mind if you let it.

  • Extraction of unvarying physical laws governing a living system from data is in many cases is NP-hard. There are far many varieties of actors and variety of interactions for the exercise to be tractable.

  • Given the possibility of their extraction, the nature of the components of a living system are not fixed and subject to unvarying physical laws – not even probability laws.

  • The conscious behavior of some actors in a financial market can change the rules of the game, some of those rules some of the time, or complete rewire the system form the bottom-up. This is really just an extension of the former point.

  • Natural mutations over time lead to markets reworking their laws over time through an evolutionary process, with never a thought of doing so.


Thus, in this approach, Nt+ corresponds to the number of upward jumps of the asset in the time interval [0,t] and Nt to the number of downward jumps. Hence, the instantaneous probability to get an upward (downward) jump depends on the arrival times of the past upward and downward jumps. Furthermore, by construction, the price process lives on a discrete grid, which is obviously a crucial feature of high frequency prices in practice.

This simple tick-by-tick price model enables to encode very easily the following important stylized facts of modern electronic markets in the context of high frequency trading:

  1. Markets are highly endogenous, meaning that most of the orders have no real economic motivation but are rather sent by algorithms in reaction to other orders.
  2. Mechanisms preventing statistical arbitrages take place on high frequency markets. Indeed, at the high frequency scale, building strategies which are on average profitable is hardly possible.
  3. There is some asymmetry in the liquidity on the bid and ask sides of the order book. This simply means that buying and selling are not symmetric actions. Indeed, consider for example a market maker, with an inventory which is typically positive. She is likely to raise the price by less following a buy order than to lower the price following the same size sell order. This is because its inventory becomes smaller after a buy order, which is a good thing for her, whereas it increases after a sell order.
  4. A significant proportion of transactions is due to large orders, called metaorders, which are not executed at once but split in time by trading algorithms.

    In a Hawkes process framework, the first of these properties corresponds to the case of so-called nearly unstable Hawkes processes, that is Hawkes processes for which the stability condition is almost saturated. This means the spectral radius of the kernel matrix integral is smaller than but close to unity. The second and third ones impose a specific structure on the kernel matrix and the fourth one leads to functions φi with heavy tails.

Financial Forward Rate “Strings” (Didactic 1)


Imagine that Julie wants to invest $1 for two years. She can devise two possible strategies. The first one is to put the money in a one-year bond at an interest rate r1. At the end of the year, she must take her money and find another one-year bond, with interest rate r1/2 which is the interest rate in one year on a loan maturing in two years. The final payoff of this strategy is simply (1 + r1)(1 + r1/2). The problem is that Julie cannot know for sure what will be the one-period interest rate r1/2 of next year. Thus, she can only estimate a return by guessing the expectation of r1/2.

Instead of making two separate investments of one year each, Julie could invest her money today in a bond that pays off in two years with interest rate r2. The final payoff is then (1 + r2)2. This second strategy is riskless as she knows for sure her return. Now, this strategy can be reinterpreted along the line of the first strategy as follows. It consists in investing for one year at the rate r1 and for the second year at a forward rate f2. The forward rate is like the r1/2 rate, with the essential difference that it is guaranteed : by buying the two-year bond, Julie can “lock in” an interest rate f2 for the second year.

This simple example illustrates that the set of all possible bonds traded on the market is equivalent to the so-called forward rate curve. The forward rate f(t,x) is thus the interest rate that can be contracted at time t for instantaneously riskless borrowing 1 or lending at time t + x. It is thus a function or curve of the time-to-maturity x2, where x plays the role of a “length” variable, that deforms with time t. Its knowledge is completely equivalent to the set of bond prices P(t,x) at time t that expire at time t + x. The shape of the forward rate curve f(t,x) incessantly fluctuates as a function of time t. These fluctuations are due to a combination of factors, including future expectation of the short-term interest rates, liquidity preferences, market segmentation and trading. It is obvious that the forward rate f (t, x+δx) for δx small can not be very different from f (t,x). It is thus tempting to see f(t,x) as a “string” characterized by a kind of tension which prevents too large local deformations that would not be financially acceptable. This superficial analogy is in the follow up of the repetitious intersections between finance and physics, starting with Bachelier who solved the diffusion equation of Brownian motion as a model of stock market price fluctuations five years before Einstein, continuing with the discovery of the relevance of Lévy laws for cotton price fluctuations by Mandelbrot that can be compared with the present interest of such power laws for the description of physical and natural phenomena. The present investigation delves into how to formalize mathematically this analogy between the forward rate curve and a string. We formulate the term structure of interest rates as the solution of a stochastic partial differential equation (SPDE), following the physical analogy of a continuous curve (string) whose shape moves stochastically through time.

The equation of motion of macroscopic physical strings is derived from conservation laws. The fundamental equations of motion of microscopic strings formulated to describe the fundamental particles derive from global symmetry principles and dualities between long-range and short-range descriptions. Are there similar principles that can guide the determination of the equations of motion of the more down-to-earth financial forward rate “strings”?

Suppose that in the middle ages, before Copernicus and Galileo, the Earth really was stationary at the centre of the universe, and only began moving later on. Imagine that during the nineteenth century, when everyone believed classical physics to be true, that it really was true, and quantum phenomena were non-existent. These are not philosophical musings, but an attempt to portray how physics might look if it actually behaved like the financial markets. Indeed, the financial world is such that any insight is almost immediately used to trade for a profit. As the insight spreads among traders, the “universe” changes accordingly. As G. Soros has pointed out, market players are “actors observing their own deeds”. As E. Derman, head of quantitative strategies at Goldman Sachs, puts it, in physics you are playing against God, who does not change his mind very often. In finance, you are playing against Gods creatures, whose feelings are ephemeral, at best unstable, and the news on which they are based keep streaming in. Value clearly derives from human beings, while mass, charge and electromagnetism apparently do not. This has led to suggestions that a fruitful framework to study finance and economy is to use evolutionary models inspired from biology and genetics.

This does not however guide us much for the determination of “fundamental” equa- tions, if any. Here, we propose to use the condition of absence of arbitrage opportunity and show that this leads to strong constraints on the structure of the governing equations. The basic idea is that, if there are arbitrage opportunities (free lunches), they cannot live long or must be quite subtle, otherwise traders would act on them and arbitrage them away. The no-arbitrage condition is an idealization of a self-consistent dynamical state of the market resulting from the incessant actions of the traders (ar- bitragers). It is not the out-of-fashion equilibrium approximation sometimes described but rather embodies a very subtle cooperative organization of the market.

We consider this condition as the fundamental backbone for the theory. The idea to impose this requirement is not new and is in fact the prerequisite of most models developed in the academic finance community. Modigliani and Miller [here and here] have indeed emphasized the critical role played by arbitrage in determining the value of securities. It is sometimes suggested that transaction costs and other market imperfections make irrelevant the no-arbitrage condition. Let us address briefly this question.

Transaction costs in option replication and other hedging activities have been extensively investigated since they (or other market “imperfections”) clearly disturb the risk-neutral argument and set option theory back a few decades. Transaction costs induce, for obvious reasons, dynamic incompleteness, thus preventing valuation as we know it since Black and Scholes. However, the most efficient dynamic hedgers (market makers) incur essentially no transaction costs when owning options. These specialized market makers compete with each other to provide liquidity in option instruments, and maintain inventories in them. They rationally limit their dynamic replication to their residual exposure, not their global exposure. In addition, the fact that they do not hold options until maturity greatly reduces their costs of dynamic hedging. They have an incentive in the acceleration of financial intermediation. Furthermore, as options are rarely replicated until maturity, the expected transaction costs of the short options depend mostly on the dynamics of the order flow in the option markets – not on the direct costs of transacting. For the efficient operators (and those operators only), markets are more dynamically complete than anticipated. This is not true for a second category of traders, those who merely purchase or sell financial instruments that are subjected to dynamic hedging. They, accordingly, neither are equipped for dynamic hedging, nor have the need for it, thanks to the existence of specialized and more efficient market makers. The examination of their transaction costs in the event of their decision to dynamically replicate their options is of no true theoretical contribution. A second important point is that the existence of transaction costs should not be invoked as an excuse for disregarding the no-arbitrage condition, but, rather should be constructively invoked to study its impacts on the models…..


Part 1
WAC or any other mutation of it sounds soap and it largely depends on TRPs, or takers/viewers. So far, so good, so what? Assuming deregulation from the governments and many of the giant corporations could be pushed down the hill, a homicide that would bring smiles to millions. Right? Yes, partly, but there is a dark trajectory, an obscured world that is omnipresent and omniscient touching everyone of us in more malignant ways than could be even remotely imagined. Where am I heading here? Maybe into oblivion as thats already designated. But, pause I will and ask this. Have you heard of Vitol, Archer Daniels, Mercuria, Noble and Wilmar? What about Glencore? Well, probably not. These are not gamers, or porno-pharmacopeia of sorts swarming the Internetwork and looking for hosts and nodes to sneak into the surveillance bazaars, or even tiers in heaviness of metal sounding junk. These are “commodity firms”, trading into commodities and fixing prices of the most basic commodities from food to energy sources to pharmaceuticals, and what have you. So, what I pay is linked with mathematical calculations wrought by often young, arrogant and brilliant number crunchers. 
Let us explore, with a view to comprehend a world of finance as dark as the world of Internetwork, the Darknet, where one could not just make hay while the sun shines, but merry when the moon phases in and out. This is particularly ugly and compels me to put forth the argument that major financiers from IFIs, NFIs, and Investment Bankers are much too benign in comparison.
Rolling Stone magazine once said this for Goldman, “a great vampire squid wrapped around the face of humanity.” Such a qualification could fit any of the commodity traders, and especially Glencore, who operate out of a wealthy Swiss village, and has annual revenues of $214 billion, that is 60x FB’s or 5x what Google manages to pull in. And, this is not small tributary that swells economies, but maybe, in the most extreme analogical manner an underground tributary with a shadowy existence, since outside the stocks that trade on it, a lot of what it does is not liquid and done off the balance sheets. There goes the challenge of mapping, transparency and accountability. With an IPO of $11 billion, this price fixer has the potential to spark off riots, destabilise economies, and still manage to stay stealthy, for who would take them on radars?
With a truly frightening knowledge of the flow of commodities around the world, incredible performance culture, the firm hefts a fear factor of 3x investment banking, to say the least. With a clientele that is a roll call of world’s largest corporations viz. BP, Exxon Mobil, Chevron, ArcelorMittal, Sony and the national oil companies of Iran, Mexico and Brazil, and public utilities in France, China, Japan and Spain, to name a few, the ideal philosophy they bank on is simple: make money by finding customers for raw materials and selling them at a mark-up by concocting complex hedge funds, market swings, piracy and regime change. Oh!, this is simple huh! In other words, the simplicity lies in one word: CONTROL. They want it and they get it in high-risk environments, reproducing an ugly baby with a parentage of meshed-up financial engineering and old-fashioned conservative/orthodox/traditional commodity trading. With just two designated classes of employees: “thinkers” – who massacre numbers and “soldiers” – who seal the deal/negotiations, the quiet cognitariats release a juggernaut of extreme arrogant efficiency.
The firm was started by Marc Rich, who escaped the Nazis, set shop for spot market for crude oil, evaded taxes, sold oil to Iran during the hostage crisis in the dying 70s and growing 80s, apartheid SA, assisted Mossad and the icing on the cake: engineering a deal for a secret pipeline through which Iran could pump oil to Israel during Shah’s rule. The rule of notoriety ended when he smashed hard to ground in a valiant attempt to control Zinc market by splurging $1 billion. The reins were handed over to a German metal trader, Willy Strothotte, who translated the majority stake into $600 million, making it close to $100 billion in worth today. Glencore, often referred to as an acronym of Global Energy Commodities and Resources (though, this could be some linguist’s word play exercise) often found itself implicated in controversial dealings, but never lost sight of prowling for opportunities.
Part 2
Moving on from part 1, which painted a historical notoriety for commodity trading firms, this part deals borders on some operational aspects and a view of dashing financial moves in a stealthy manner.
Way back in 1993, I picked up from the flea market in Poona, Kerrang!, world’s largest read rock and metal magazine. I still remember the words then: From the Quaint Swamps of Milwaukee, comes a force that can be described in one word: Viogression, redefining music and speed spiced up with aggression. This was a death metal band from Wisconsin, and the advertisement was for Milwaukee Death Fest, a converging point for Death Metal fans. Obviously, the speed has been surpassed exponentially ever since. But, why this? As an analogy, Glencore comes brutalizing financial sways and swings, upsides and downsides from a quaint village of Baar in Switzerland, unleashing its ferocity on the London-stock exchange listing and a registered office in Saint Helier, Jersey. As brutal as the band could get on the Milwaukee music scene then, the commodity traders have unleashed their fury on the financial stage and continuing to accelerate its spawn. 
The contingency of operations make for a smart move, for in contingency is the scent of an opportunity rather than the stench of risk. This ain’t the twisted version, but rather a crude philosophical one for the commodity trading firms (CTFs hereafter). The opportunity is built over offtake deals, where other financial institutions fear to tread for uncertainty lying over repayment for whatever gets invested. This is as much a part of the risk investment. Such deals materialise, mostly in natural resources, when with significant capital costs involved in extraction of the resources forces the company to have a guarantee that its product will be sold, that there shall be a secure market. Such a situation is promised, and if the company were to slide into a financial quandary, a likely debt-burden gets slashed by bringing in swapping loans-for-rights/ownership issues, thus offloading the equity for uploading it to the CTFs. In financial parlance, such a move is termed prosaically: right to convert debt into equity in the tail. ‘In the Tail’ connotes tail risks, which are low probability events that have an outsized impacts on prices, more than often inordinately large. In present times, the nightmarish tail risk is the perennial China hard-landing, which, if it were to occur would exponentially rise costs of basic commodities, diverge them due to supply disruptions rather than demand overflow, thus churning faster the global economy and sickening consumption in course as a result of shrinking supply.
When Merrill Lynch conducted a survey in the beginning of this year asking about the biggest tail risk for the global economy, fund managers answered China hard-landing/collapse in commodity prices. And yet, technically the commodity index is breaking out on the upside. Interestingly, only about 5% of fund managers really worried about inflation risks. 
Merrill Lynch observed that a net 23% of investors are currently underweight commodities, which is only a slight improvement from net 31% underweight in December 2013. Current readings in exposure are extremely under-owned at 1.7 standard deviations below its decade long average. The situation as it stands today resembles 2008, when just about every fund manager and policy maker was pessimistic on commodities and inflation right before the prices bottomed out and rallied powerfully in coming quarters. But, if inflation was to surprise the markets to the upside, then stocks could get oblivious, since history has time and again proved that commodities have always been the best hedge against inflation. Sigh!, Huh!
Moving on, CTF’s operational key lies in flotation, listing its shares on the stock market, albeit in a split manner as was talked off in part 1. The issue of safeguards and adherence to strict guidelines is robust here, for potential investors are not to be misled. Once new shares are issued on the primary market, trading sets off in the secondary market, in that trading transactions occur between investors without any involvement of the CTF. This still is procedurally under check, but CTFs invent a twist, for they list with the clause for a permanent capital base. Rationale is simple here: In private partnerships, payouts to departing partners shrink the capital base, while public companies’ equity remains intact even if the shares change hands at dizzying speeds. Most cardinally, such a move injects reassurance in credit agencies that not only shy from keeping at bay any relegation of CTFs to junk on the one hand, but allows the CTFs enough manipulability with flexible capital structures going in for the kill, meaty acquisitions on the other. Getting back to Glencore, this example for once sets up a sneak peek into what the CTF is capable of, and how it is well-nigh difficult to locate movements in such financial transactions. Transactions that make possible coming out clean and acquit being cornered to a dock. When speculations were rife in 2010 of a possible merger between Glencore and London-listed Xstrata, shareholders of the latter opposed it, arguing that the valuation of the former be dictated by market forces and not dealt with behind closed doors. To force things to a head, Glencore set the clock ticking on a change in its set-up by issuing a convertible bond. These types of bonds not only can be converted into a predetermined amount of the company’s equity at certain times during its lifespan, but also helps facilitate the CTFs to alleviate any negative investor interpretations of its corporate actions. From investors’ point of view, the bond has a hidden stock option, and helps her with a lower rate of return in exchange for the value of the option to trade the bond into stock. The package was set thusly: convertibles pay a staid interest rate of 5% every year till their maturity in 2014, but are laden with incentives for Glencore to transform itself. In other words, the package contains this: If by December 2012, Glencore does not float or merge with another company, bondholders can sell their bonds back to Glencore at a price which would give investors an annualised return of 20%, in line with the sorts of returns one might expect from equities. By this, the CTF will not be penalised if markets turn lower and if the IPO turns to be unattractive. A smirk invades faces!!! 
CSOs can take up the archaeologist’s role, and dig they will in order to turn opacity into at least translucency, if not outright transparency, for CTFs deal with a chain that is particularly vulnerable to mismanagement, and therefore scrutiny becomes the sine qua non to bring these onto the radar screens. There is an actionaid page on Glencore’s tax dodge in Zambia here and the cover up this tax probe here by none other than European Investment Bank. These wolves make their money at the margins, and profits by working in the global margins, margins of what is legal, erecting walls of shell corporations, weaving complex webs of partners, offshoring accounts in order to obscure transactions, and working with shady intermediaries (Financial Intermediaries in the case of IFIs could be coaxed into differential calculus of presenting themselves into the developing world, but as it holds true invest far and between into repressive political regimes: safeguards and recourse mechanisms at least on paper guarantee this) to obfuscate what is legally corrupt and what is not. No wonder, titularly, the post makes sense: what does one do these? 
Part 3
I apologize for the length of this concluding part and the series thats been flooding your inboxes for the last three days. The idea behind the series emanated after a twitter discussion with a couple of friends, where we wanted to understand the dark movements of commodity trading financing and its ramifications for a political habitat where notions of post-industrial capitalism could be brought to light in at least comprehensibility, if nothing more. Thanks for the patience.
Leveraging information in times of wild commodity-economic swings is cashed on. Trading and hedging all the way, it is akin to a casino, where the ‘house’ always wins, a spot of volatility, where eagles dare, nah, where the wolves dare. In one of the most interesting euphemisms ever from Deutsche Bank on Glencore, the German Bank said, “Key drivers of growth: copper in the Democratic Republic of the Congo, coal in Colombia, and Gold in Kazakhstan. All are places with a heady, dangerous mix of extraordinary wealth and various degrees of instability, violence and strongman leaders. But, these guys need to adapt as well, and adapt they do undermining transparency. In a pretty hubristic manner, Marc Rich said, “Discretion is an important factor of success in the commodity business. They probably don’t have a choice. Transparency is requested today. It limits your activity, to be sure, but it’s just a new strategy to which they have to adapt.” (Italics/emphasis mine). 
Hedge: an investment position intended to offset losses/gains that may be incurred by a companion investment. Hedging is the practice of taking a position in one market to offset and balance agains the risk adopted by assuming a position in a contrary or opposing market or investment.
Derivatives: special contracts that derives its value from the performance of an underlying entity, which could be an asset, index, or interest rate. Derivatives as used here are used in insuring against price movements/fluctuations (hedging), increasing exposure to price movements for speculation or getting access to otherwise hard to trade assets or markets. Thanks Wiki.
Moving on backwards for a time, CTFs chiefly perform arbitrages, while facing a wide array of risks, which are often times managed by hedging, insurance and/or diversification. Probably taking a leaf from Richard Morgan, a force in himself on the science-fiction space, I’d have no second thoughts in underlining that these guys are adept in transferring risks to the financial markets using instruments of hedging in derivatives or purchasing insurance. The principal funding comes through mixing debt and debt maturities, to which we shall turn shortly. Upon emaciating my bitterness as exhibiting in parts 1 and 2, the suggestion that CTFs are potentially the sources of systematic risks like the banks, and hence be open to regulations, the faltering point comes with the fact that these are not too big to fail, and at most of the times keep themselves in check from engaging in kinds of maturity transformations that make banks highly susceptible to run. Moreover, these are not major sources of credits like the  IFIs and their ilk, and thus are not very leveraging entities, and if at all these encounter any financial distress, these simply transfer the distress to others.
In the penultimate section, let us deliberate on risk factors, before concluding with modes of financing. I’d try to keep mathematics to the bare minimum, and would circle on attempts at popularising. Risks have numerous paths of departure from the way IFIs and their ilk define, negotiate and deal with. Traditionally, CTFs deal with Flat Price Risks, where flat price is the absolute price level of the commodity to be traded. The firm transacts a commodity, and hedges the relative commodity position through derivatives transaction, by for e.g. selling future contracts to hedge inventory in transit. This is carried out with the intention of transforming the exposure to commodity’s flat price into an exposure to the basis between the price of commodity and the price of the hedging instrument. Flat Price Risks do not always materialise into distress, for hedging sees to it that an exchange of Flat Price Risk for a Basis Risk transpires, i.e. the risk of changes in the difference of the price between the commodity being hedged and the hedging instrument. Such a price differential is possible because the characteristics of the hedging instrument are seldom identical to the characteristics of the physical commodity being hedged. The differential is, moreover built on a positive feedback mechanism that creates a virtuous cycle, standardising hedging instruments for commodities and inducing market participants to trade these standardised contracts with less of a basis risk, but more of a transaction cost. In short, Basis Risk is commodity-contextual and opportunistic for the firms to accept, and pregnant with what in financial jargon is termed ‘a corner or a squeeze’, by which is meant an exercise of the market power in a derivative market, a tendency to cause distortion in the basis that can possibly inflict harm on hedgers. Certain rogue traders can cause ruptures by either spreading risks across margins between the sale and purchase prices depending on volumes of transactions, or even cause ruptures in operations. Well, it is not very difficult to realise that this is part of a contractual risk, when the other party defaults. This could be quite detrimental for the CTFs for the sellers of commodities to consumers have an incentive to default when prices rise subsequent to their contracting clause, and the CTFs are left to lurk for finding the necessary supplies. To escape the trap of such situations, CTFs devise ways to enter and exit positions to negotiate Market Liquidity Risks, where liquidity as a node of causal chain in market flip-flops can cause huge distress. But, if caught in such scenarios, funding this liquidity risk becomes the imperative. Both, funding liquidity and market liquidity are in a relationship of correlation, of interaction, in that stressed conditions in financial markets result in decline of both market liquidity and funding liquidity, compounded through large price fluctuations and movements leading to greater variation margin payments and thus increasing financing. Lastly, appreciation and depreciation of local currencies tied with political economy of the geographies and vulnerability to legal transgressions often face the CTFs in the face of legal reputation getting hit and imposition of legal sanctions looming large. At present times, when commodity manipulation is subjected to considerably intense political and regulatory attention and scrutiny, CTFs bank on difficulties in legal proceedings on the one hand, and on the other, their expertise regarding the economic frictions in transformation processes that make their activities profitable and their financial size big enough and thus almost lending them a position to do so through a term mentioned in the beginning of this sentence, manipulation.
Turning to financing now. As is well known that debt and equity issued by CTFs link them to a broad financial ecology, and therefore any capital structure envisaged by CTFs opens them up to vulnerabilities of market swings. CTFs traverse from gearing/leverage, forms of leverage these employ and rights/ownership of equities. Pure trading firms that own relatively few fixed assets tend to be more highly leveraged than firms that also engage in processing and refining transformations that require investments in fixed assets. At the same time, firms engaged in more fixed asset intensive transformations have a greater proportion of long-term liabilities. CTFs do not always engage in maturity transformations as do the banks, and when they do, it is the reverse of the borrow short-lend long transformation that makes bank balance sheets fragile, and exposes banks and financial intermediaries (FIs) to runover risks. Additionally, since CTFs’ primarily hedged inventories and trade receivables tend to be highly liquid and of high credit quality, these firms run less liquidity risks than FIs and banks. CTFs rely on bank borrowings to finance these transformation activities, by either through short-term borrowings that could also be routed through unsecured credit lines via an arrangement that is syndicated. A typical case involves bilateral credit lines, and are secured by saleable commodities in liquid markets that are marked to market and hedged, and thereby benefiting these exposures to short maturities, which in turn present less credit risks as compared to a credit secured by less liquid collateral. Other than these, non-bank financial vehicles like shadow-bank transactions are often used to securitise inventories and receivables. Glencore specialises in this format, the format that dals with FIs through the issuance of debt outside the insured banking system. The financing mechanisms get complex when tied with ownership rights, where if inefficient risk bearing is the major cost incurred in private ownership, it is the idiosyncratic risks of commodity that get diversified, thanks to shareholders when the firm is publicly listed. But, private firms have a way out inked in financial contracts whereby risks outside the gamut of management control can be transferred to others. This structure built into the contract not only incentivises benefits to the private CTFs, but also score mighty in comparison to public-listed firms, where risk bearing capability is modest at best. But, there is a catch here that swings the pendulum in favour of publicly-listed firms and was possibly one of the chief reasons for Glencore going public. In large-scale investments where equity investments can shoot the budget of private players and expose them to humungous risks, a transference of risks by means of non-equity financial contracts to others is not feasible, and thus a recourse to businesses that can be hedged in derivatives, credit and insurance markets is undertaken. In short, with increasing asset intensity and accumulation of sorts, a movement away from private ownership to going public is indispensable. The obvious question in many minds at the moment would naturally be: what about disclosure then? Who scores and who wins here? The private firms are obligated to keep accounts and records to be kept in accordance with accepted accounting principles and standards, but the laws regarding what information must be disclosed is discretionary upon jurisdiction. In the case of US, private firms have to provide information to their lenders and derivatives counterparts, and at any time with their discretion can provide their financial information in ways similar to those employed by their public counterparts. Importantly, with respect to disclosures to government regulators, CTF positions in listed derivatives are available to exchange staff and government regulators.
What about their relation with FIs? CTFs supply financial intermediation servies to their customers through trade credits, structured transactions that bundle financing, risk management and marketing services. A CTF selling a commodity to a customer has better information about the buyer than would a bank, thus giving the option to the CTF to have a better preparedness on evaluating creditworthiness as compared to a bank. As is a well known fact that cash is more fungible than a commodity, any diversion with cash input is more likely than with a commodity, and thereby more risky. One way to reduce this risk susceptibility is through an off-take agreement, where a CTF agrees to purchase a contractually specified quantity of a commodity from a producer usually at a floating price. the process starts off with refinancing involving three parties: the borrower, CTF and the bank. Borrower and the CTF enter into a prepay arrangement with the bank providing the necessary funds to the borrower. When the commodity is delivered to the CTF, the CTF pays the amount it owes under the off-take agreement to the bank to repay the loan. Wow!, the bank has no recourse to the CTF, and bears all the credit risk associated with the loan to the borrower. What, then is prepay? Two variants emanate in this regard: in the 1st, the bank provides limited recourse financing to the CTF, and the trader assigns the rights under the off-take arrangement to the bank as a security; the CTF provides funds to the producer, but the bank absorbs the credit risk on the loan (there could be instances when the CTF may keep a risk participation), in the 2nd, the bank provides full recourse financing to the CTF, which then makes a loan to the borrower. Thus in the 2nd variation, the CTF bears the risk that the borrower will not repay the prepaid amount. The CTF, in turn, can offload all or some of this credit risk by entering into an insurance policy, and depending on the terms of the financing provided by the bank to the CTF, the bank may be the loss payee on this insurance policy. A CTF can also engage in a Tolling arrangement, where the CTF supplies a commodity processor with an input and takes ownership of the processed commodity. The CTF pays a fixed fee to the processor, pays the market price to acquire the input, and receives the market price for the refined products. This type of an arrangement is common with oil as the main input. Thee structures bundle together multiple goods and services. For e.g. in a simple off-take agreement, the CTF provides marketing services and hedging. A prepay incorporates these elements and a financing component as well. The seller receives cash upfront, in exchange for a lower stream of payments in the future with the discount on the sales price being effectively the interest on the prep amount. A Tolling arrangement bundles input sourcing, output marketing, price risk management, and working capital financing. The working capital element exists because the CTF has to finance the input from the time it is purchased until it can realise revenue from the sale of the refined goods after processing is complete. The benefits of Tolling entail a need for working capital to finance the timing gap between cash outflows and inflows. But, there is an ethical dilemma here: providing financing for working capital is a traditional activity banks have hitherto engaged in. When the lender lends to an entity, it leaves the entity to acquire input and market outputs, and bear and perhaps manage the price and operational risks associated with those activities. This leaves the lender exposed to risks where any adverse movements in prices could leave the entity into a financial distress and cause default. The lender could require the borrower to hedge, and if it does not, or does not do it effectively, the lender bears the risk. This undermines the incentive of the borrower to hedge, and hedge well. The lender can monitor, but this is costly and often times imperfect. The ethical dilemma is addressed by passing the risk to the lender. A prepay or Tolling does this well. These implicitly provide the funding to bridge the outflow-inflow gap, and pass on the price risks back to the lender. The lender can manage these risks and the agency costs in this arrangement is on the lower side, and since the lender bears the price risk, there is no ethical dilemma anymore. Most crucially, it takes on the incentive to manage the risks, thus quashing any need for monitoring it. the implication is that bundling price risk management and financing can reduce the cost of funding working capital needs. Furthermore, the lender may have a comparative advantage in managing risks due to specialisation and expertise in this function: CTFs and banks have a comparative advantage in risk management. CTFs with their specialisation in logistics and marketing smoothly navigate scale and scope economies. For instance, it may be cheaper for a CTF to provide marketing and logistical services thereby eliminating any associated overheads with these activities. Less sophisticated firms, on the other hand benefit hugely by delegating marketing, logistics and risk management services to specialist firms that can exploit the scale and scope economies. Thus, bundling financing and FIs make for a complementarity.
In conclusion, CTFs are here to stay, but need serious attention of regulators, for there is a scare that traders’ ownership of infrastructure allows these firms to manipulate local prices, even if they do not have the heft to rig global markets. Mochas Kituyi, secretary-general of the United Nations Conference on Trade and Development accuses the industry of corruption and illicit flows and large-scale trade mispricing in the developing world. And this is where their potential hazardous nature surfaces. Importantly, activists need the necessary instruments to dig deep in transactions that more than likely result in CTFs ride out with profit when cornered and/or investigated. In this era of black swans, the sharpening of teeth eating into the flesh of CTFs should generally commence from the knowledge economy/ecology with no truck for the dichotomy.