What Black-Scholes-Merton means to a speculative materialist

SpecMat Options Tutorials.

Lesson Two

Even those persons paralyzed by anxiety over their lack of knowledge of financial economics will intuitively grasp that uncertainty pervades an attempt to ascertain what will have been the value of an asset in the future, relative to its subsequent past value now in the present. Can this even be done? BSM says it can.

Initially BSM announced itself as a risk-neutral, nonarbitrage model for pricing options. We will later expand on the importance that BSM isn’t quite used like this today. But let us presently observe that BSM is indeed a nonarbitrage model, albeit one whose condition of possibility and goal is arbitrage. However, BSM asserts that when deploying its partial differential equation to determine the value of an option, i.e. in order to know in the future what will have been the value of an option today, an operator must know four things:


(i) the option’s time to maturity (T)

(ii) the riskless interest rate (r)

(iii) the referent price (S0)

(iv) the volatility of referent price (σ)


The first three parameters are easily found. They’re either quoted in the market, or in the case of the first parameter, (T) time to maturity, i.e. the option’s expiration date, is written into the terms of the contract itself. However, the fourth parameter, volatility, is a bit trickier. BSM presumes a normal distribution, or ‘constant’ volatility –which amounts to erecting a thin epistemological wall to artificially insulate its model from jumps, irregularities, or volatile volatility, hoping those hideous animals on the other side won’t breach its perimeter, and stroll right in. Is to know volatility ontologically impossible? What even is volatility?

These queries have no quick redress, but are crucial for grasping the model of economy of deterministic chaos that is dromocracy proposed by D&G, and which in 2014 we have been and will be continuing to elaborate. Let us then move through its logic, or at least of it what we presently know.

If markets make a random walk, so too are plots of trajectories of the price movements of its assets, whose economic properties orbit along their markets’ surfaces. This means volatility is stochastic, unsteady, intractably irregular, a dark beast –and therefore this fourth parameter that an operator ‘must’ know to use BSM exhibits some determinism, yes, but a determinism wholly infused with chaos, or even is chaos.[1] Time spent studying the behavior of any class of financial asset causes quick realization that data on ‘past’ or ‘historical volatility’ (also called ‘actual volatility’)[2] is available but not dispositive for knowing future price movements. So any attempts by a BSM operator to divine ‘future volatility’ amounts to an attempt to solve a differential equation by way of a nondifferentiable function (*it can’t be done). Operators know this, and for this reason elect to retain BSM, but invert its equation to iterate ‘implied volatility’. Doing so, an operator must still know four things:


(i) the option’s time to maturity (T)

(ii) the riskless interest rate (r)

(iii) the referent price (S0)

(iv) the volatility of referent price (σ) (iv) the market price of the option


…but all four of which are now dictated or conveyed by the market, are messages transmitted in and by the market: the new fourth parameter is combined with the previous three parameters, and now used to derive the as-yet-unactualized volatility implied by the current market price of the option. The BSM operator, then, no longer plugs in the parameter of a normally-distributed volatility, which is to say a ‘constant’ volatility presumed to be actual, actualized, or ever actualizable, in order to derive the theoretical value of an option, but now plugs in the market price of the option to derive the theoretical value of volatility –i.e. the virtual value of volatility that an actual option price implies. Does this not render implied volatility a partial relic of the virtual that’s yet now paradoxically actual as well? In a future post we will take up this technological issue by opening up, to briefly peer inside, the peculiar material profundities interpellating implied volatility, which we believe is an intensive economic property: an odd, rare empirical instance of a differentiated aspect of the virtual that’s been refracted through itself and now dumped out into actuality; giving rise to ‘actual volatility’ at the same time such actualization of the latter covers or cancels it out.[3] Moreover, some attention directed to the robust Deleuzian-dynamical systems theoretic sense of the concept ‘intensive’ organically breeds our conviction that implied volatility is readily deployable as a fungible pricing mechanism, far more commensurate with the economic institutions and endemic behaviors of the denizens of a dromocracy, than that base and placid, one-dimensional, extensive medium of exchange we call ‘money’.


Presently, our brief tutorial on options will presume little background on our reader’s part.[4]


Financial derivatives comprise a class of financial assets. Options comprise a class of financial derivatives. In dromocracy, the exchange of options, especially exotic options (or simply ‘exotics’) comprise its principal class of exchange. Contingent local communities of becoming are ‘clusters’, rendering ‘clusters of exotic options’ (CEOs) one of its two economic institutions (the second being a universal synthetic CDO, to be outlined a bit later). In dromocracy, exotics among clusters are traded en masse.

The standard, if only sometimes correct definition of a financial derivative is an asset whose value derives from some other asset, often called a referent or underlier.[5] We’re supposed to tell you this; but it need not overly concern us, and at any rate, like the principles of Euclidean geometry, is not so much always wrong as it is only sometimes true. Our real concern is that an option is a nonlinear financial derivative producing a contingent claim; and that holding an option gives one the right to do something by a certain date, it gives the option holder choice, or optionality. Taleb tells us that ‘optionality is a broad term used by traders to describe a nonlinearity in the payoff of an instrument’[6], which will be particularly compelling to a reader who is now beginning to cognitively synthesize that rhizomes-nonlinearity-chaos-financial derivatives are the constitutive components of dromocracy, and that such novel model of economy is available to us if we so choose.

There are two kinds of options. There are ‘call options’, the holding of which gives one the right to acquire something at a certain price (called the ‘strike price’), on or by a future date (called ‘maturity’ or ‘expiration’). And there are ‘put options’, the holding of which gives one the right to part with something at a strike price on or by a future date. The terms ‘European’ and ‘American’ have nothing to do with where the options are written, read, or otherwise exchanged. Rather, European options can only be exercised on the day of their expiration, while American options can be exercised any time between their inception and expiration.

‘Operators’ are those persons exchanging options. Operators trade optionality. There are two types of operators: ‘writers’ and ‘readers’. To write optionality is to compose and sell an option for a fee to a reader, who now holds the right to choose to acquire some pre-agreed-to asset, whether an object or service, at a predetermined price, if mutually-pre-agreed-to conditions are met either at maturity or during the life of the option. To read optionality is to buy an option for a fee from a writer, who now accepts liability to deliver some pre-agreed-to asset, whether an object or service, at a predetermined price to the reader, if mutually pre-agreed-to conditions are met either at maturity or during the life of the option. Writers, then, issue optionality for a price, and accept liability if conditions written into the option are met. Readers accept optionality with a price, and exercise their choice if conditions written into the option are met.

What we have just said equally applies to exotics or vanillas, which are the two classes of options. Vanillas are standardized, and conventionally-structured. Exotics are bespoke, and have non-conventional structures. However, we will principally concern ourselves with exotics. It worth noting here, to begin, that pricing exotics can quickly become quite complicated in ways not conquerable by however-sophisticated modeling techniques, therefore generating available arbitrage opportunities for its operators. Importantly, this is due to exotics’ high-degree of nonlinearity: vanillas, yes, are already nonlinear, since all options are nonlinear assets; albeit exotics exhibit a higher degree of nonlinearity, as we will show. Exotics are to be the principal class of options exchanged in a dromocracy.[7]


BSM’s original assertion is that in theory it’s possible to construct a riskless portfolio, comprised of a position in options and some referent, such as stocks (though it could be any generic asset). We henceforth call this portfolio a ‘package’.[8]

Scholes says, ‘Black’s and my discovery was how to price options and to provide a way to manage risk.’[9] Derman and Taleb remind us this doesn’t mean that options are rendered riskless assets, or that an option’s actual price movements are in any way predictable, periodic, or nonstochastic.[10] Rather, the success of BSM’s pricing model pivots on hedging. And not just any hedging, but delta hedging –whose wager is that if an operator can get the delta of their package ‘right’, and then hedge accordingly and continuously, any price movements in an option position will always be offset by price movements in the referent position, and vice versa: and that these price movements offset one another means that the delta of the package at any given point in time, while not strictly zero, is nonetheless always striving towards it, tending towards it, asymptotically ever attempting to move yet closer to zero. The delta of the package is perpetually a becoming-zero.


[1] We’ll see that the issue is more involved than this. The initial model of price behavior used by BSM assumed that price changes are stochastic and normally distributed. To simply assert that a process is ‘stochastic’, or random, only further begs the question of the order and degree of its randomness –there are, after all qualitatively different classes of stochastization, so that any identification of a process as random must clarify to what class of randomness the process belongs (e.g. a Markov, Wiener, Itô, or Deleuzian process)? The answer given by BSM is that volatility exhibits a randomness that is normally-distributed, which makes it a Weiner process, but which turns out to be problematic. Today the standard financial economic definition of its class of stochastization is an Itô process, which we will show is also problematic.

[2] Our reader will be reminded that the three registers of reality in Deleuze’s ontology are actual-potential-virtual. ‘The actual’ is simply that which ‘is’ differentiated (what is sometimes mistakenly labeled ‘reality’). ‘The potential’ also is that which ‘is’, albeit only ‘is’ as a possibility (Deleuze identifies the potential as that which is subject to a probability distribution, but whose possible outcomes are therefore predetermined by the interlocutions of the actual and virtual). ‘The virtual’ is neither actual nor potential, and yet it exists ‘in reality’ nonetheless. A good deal of Deleuze’s project is to make technical recourse to mathematics and sciences to illustrate that while neither actual nor potential, the virtual comprises another register of reality altogether –a register structuring the space of what is possible to become actual.

[3] It is far from evident this notion is wholly comprehensible in Deleuze’s ontology. Its presentation, however, is far from a foreign element in his house. On the one hand, Deleuze toes the standard dynamical systems theoretic line that intensive properties often or always are canceled in those systems in which their spatiotemporal dynamisms generate the actualization of the extensive properties, whose very generation cancels them out. For example (‘There is an illusion tied to intensive quantities. This illusion, however, is not intensity itself, but rather the movement by which difference in intensity is cancelled. Nor is it apparently canceled. It is really canceled, but outside itself, in extensity and underneath quality.’) Difference & Repetition pg. 240; and (‘Intensity creates the extensities and qualities in which it is explicated….It is nevertheless true that intensity is explicated only in being cancelled in this differentiated system.’) Ibid pg. 255 Also see Ibid pg. 228. However, on the other hand, Deleuze’s (and D&G’s) special interest in complex, high-order, nonlinear chaotic systems (i.e. ‘systems of difference’) is their explication of relics of the virtual, e.g. intensive properties, whose logic can then be traced back up through the actual, and tinkered with. After all, why map, e.g. in phase space –if not to then tinker with matter’s evolutionary capacities? For example (‘it is in [systems of] difference that…phenomena flash their meaning like signs. The intense world of differences…is precisely the object of a superior empiricism. This empiricism teaches us strange “reason” [read: strange attractors], that of the multiple, chaos, and difference.’)

[4]The best book on options for the nonspecialist is John C. Hull Options, Futures, and Other Derivatives, Prentice-Hall 2009. For this reason, on our reader’s behalf we draw on Hull throughout Part IV.

[5] For example (‘A derivative can be defined as a financial instrument whose value depends on (or derives from) the values of other, more basic, underlying variables. Very often the variables underlying derivatives are the prices of traded assets. A stock option, for example, is a derivative whose value is dependent on the price of a stock. However, derivatives can be dependent on almost any variable, form the price of hogs to the amount of snow falling as a certain ski resort.’) Hull pg. 1; and (‘A derivative is a security whose price ultimately depends on that of another asset (called underlying). There are different categories of derivatives, ranging from something as simple as a future to something as complex as an exotic option, with all shades in between.’)Taleb pg. 9

[6] Ibid pg. 20

[7] In dromocracy there are two types of markets, ontologically-speaking, whose materiality is bound as one: there are commoditized products, which have standardized agreements in place to eliminate non-template inconveniences, and range from simple ‘spot-priced’ classic objects (e.g. things to eat and wear), to low-order forms of exotics (e.g. single barrier knock-outs); there are also nonstandard products, which are wholly exotic, and whose payoffs are specific to the instrument –these comprise the majority of contracts for work relations for the denizens of dromocracy. With such exotics, everyone is constantly tracking their Greeks. We thus agree with Taleb’s itemization of the basic difference between commoditized and nonstandard products, when he observes that ‘the real difference between [the two] is that one type is tailor made, with [higher risk and volatility, and] smaller traffic, while the other has features of a discount store with standard sizes and prices, but a higher volume.’ Taleb pg. 51

[8] Hull (2009) defines a conventional package (‘A package is a portfolio consisting of standard European calls, standard European puts, forward contracts, cash, and the underlying asset itself.’) pg. 555. We will ultimately wish to tailor this general concept to include an individual’s total portfolio of assets –generic and synthetic, comprised of exotic options and CLNs, as well as the synthetic assets whose total notional value comprises an individual’s universal synthetic portfolio, which is why we have neologized the term herein.

[9] Myron Scholes, “Derivatives in a Dynamic Environment”, The American Economic Review, Vol. 88, No.3, June 1988 pg. 351

[10] Emanuel Derman and Nassim Nicholas Taleb, “The Illusions of Dynamic Replication”, first draft Apr. 1995

What the Greeks mean to a speculative materialist

SpecMat Options Tutorials.

Lesson One

Let’s introduce the logic, then back up and unpack it.


Person: How does the package of the BSM operator chase delta-zero?

Speculative Materialist: By becoming delta-neutral, by always desiring to move yet faster towards zero.


P: But what about stochastization and nonlinearity? Won’t prices move, move randomly, and won’t volatility jump around?

SM: Well, yes, an operator needs to continuously recalibrate the delta of the package back towards its becoming-zero.


P: What’s recalibration? How does an operator continuously recalibrate?

SM: By dynamically replicating.


P: What’s dynamic replication? How does an operator dynamically replicate?

SM: With the Greeks: with delta (∆), gamma (Γ), vega (V), theta (Θ), and so on.


P: But what are the Greeks?


The Greeks

A standard financial economic definition of the Greeks is a map of the relation of the price of an option with respect to a variety of parameters;[1] by the term ‘relation’ we should understand ‘speed’, or ‘rate of change’. A speculative materialist’s brain often wishes to explode when ruminating on the ontological status of the Greeks. For us, they explicate the intensive differential relations between an option price and any number of parameters which would remain quite operative, but inaccessible to examination, strictly virtual, and thus unactualized were we lacking analytic recourse to their concepts.

We’ve conveyed our intention to tweak the Greeks, and before that to probe their peculiar ontological status for dromocratic purposes. However, let us first observe that risk has strong consequences for valuation and cash flow; but that risk is multidimensional, and dimension is not an economic invariant. In financial economics, each Greek letter (e.g. delta (Δ), gamma (Γ), theta (Θ), vega (V), etc.) purports to explicate a different dimension of risk –and its associated cash flow; so that an operator managing her Greeks will always be seeking to hedge her exposure to the multidimensional risks whose differential relations and amounts of relations perpetually supervene on her portfolio, affecting its actual value, and therefore it’s return.[2]


First, then, delta (Δ). Delta is first among Greeks, literally. It’s the first mathematical derivative of the product with respect to the referent, and the most important parameter to begin to understand dynamic replication by manner of continuous recalibration.[3] BSM’s first analytic virtue is to provide a formula for knowing delta.

Hull defines delta as ‘the rate of change of the option price with respect to the price of the underlying.’[4] Taleb defines delta as ‘the sensitivity of the option price to the change in the underlying price.’[5] More generally, this means that delta maps the speed of change between the option price and referent price. Like all Greeks, delta is an intensive economic property –which importantly, more generally means its concept denotes the speed of spread between valuations of the derivative expressed, represented, metricized, and territorialized as price, and valuations of its referent expressed, represented, metricized, territorialized as price. It is common to depict such expressions of delta in either a % or in total amounts. So for example, as Taleb explains, ‘a 50% delta is supposed to mean that the derivative is half as sensitive as the [referent] asset’ –this is delta in % terms; or ‘that one needs two dollars in face value of the derivative to replicate the behavior of one dollar of [referent] asset.’[6]

Standard industry definitions of delta will typically observe, as we have above, that it’s the first mathematical derivative of the product with respect to the referent asset. This means delta provides the primary numerics of the hedge ratio for any price moves between the referent and its derivative, when the two are combined to comprise some amount in a package. For this reason ‘delta’ and ‘hedging’ are terms which, while the former is somehow both more than but also only a subclass of the latter (for there is more than one way to hedge), are often combined so that ‘delta-hedging’, ‘hedging’, or ‘hedging with delta’ intends to denote that any infinitely small price moves in an option are offset by price moves in its referent, and vice versa.

And in a simple, linear, Euclidean world –which is to say in a world without drift, a world wherein the distribution of variations are normal –and not that there’s no volatility, but rather volatility is steady, oscillating, constant, its variations invariable, and thus globally predictable; in such a linear world, delta would obtain a value, that value would remain unchanged, and in turn would cause the efficient, effective, and sufficient achievement of a riskless package with delta-hedging: an operator would use BSM to tell her the number of units of stock (or other referent) relative to options she should hold to deterritorialize her package from a continual barrage of inundating, multidimensional risks; and constructing her riskless package by way of BSM would merely be a matter of static, linear, delta hedging, in a fixed, Euclidean, linear world.

At this time we remind our reader of D&G’s principle of asignifying rupture –one of six principles endemic to the rhizome model of economy outlined in Chapter 1. Asignifying rupture marks nonlinearity, nonlinearity has deep ontological-economic significance, and options are thoroughly nonlinear. The first good observation an options pricing manual will make is that options are nonlinear assets, and therefore the trajectories of an option’s constitutive parameters orbit amidst a nonlinear world. The second thing your manual will tell you is that this requires dynamic hedging, which in turn requires consideration of some second-order derivatives. We can and will be observing the epistemological significance of this deep ontological fact as well –albeit, as we will see, the issue obtains a qualitatively different profundity in dromocracy. For rather than seeking to mitigate, negotiate, or in some way tame nonlinearity for sedentary distributive purposes, the denizens of a dromocracy embrace its dynamics, urge its vortical propulsion, stoke its nomadic distributive thrust.

So we will demonstrate that dynamic delta-hedging consists of hedging on an ongoing and interminable, rather than static or one-time basis. There is neither true nor final ‘being’ to delta, but only ever its becoming. To dynamically-hedge with second-order derivatives is to continuously recalibrate, which renders the latter a financial economic method for interminably deterritorializing one’s package always back towards a zero that it will only ever asymptotically approach. Dynamic replication by method of continuous recalibration is then, for us, understood as a matter of interminable deterritorialization, a pure activity of intermittency isomorphic to the becoming that is the Cantor set –for it is infinitely-becoming zero, yet now comprises a substance unto itself. And insofar as dynamic replication requires second-order derivatives, such as for instance gamma, theta, vega, and rho; and that these Greeks lend us analytic purview into the multidimensionality of risk; this means that continuous recalibration provides us with a practical method for interminable deterritorialization, yes, but moreover is one whose technology provides for an operator, a set of operators, or indeed clusters of exotic operators to comingle with deterministic chaos, to drink from its open wealth –this is our interest in and wager on technology of continuous recalibration for the institutional and behavioral purposes of dromocracy.




[1] Taleb’s analysis of the Greeks are generally good (see Chapters 7-11), but his definition is characteristically careless. He invokes the term ‘sensitivity’ to denote the speed, or rate of change (‘“The Greeks”…denote the sensitivity of the option price with respect to several parameters’ pg. 10). Hull is less analytically-expansive, (see Chapter 17), but his definition is perhaps more robust (‘Each Greek letter measures a different dimension to risk in an option position and the aim of the trader is to manage the Greeks so that all the risks are acceptable.’ pg. 357)

[2] It should be observed that in any economy an operator is always virtually reading and writing their options to, among, and with others, and is therefore always faced with the actual problem of managing her multidimensional risk –the hedging of which may be easy or hard to come by, had cheaply or at great expense. Bob Meister has illustrated that the virtual exchange of options were already occurring between Adam Smith’s yeoman farmer and his financier (See his “Liquidity”, unpublished draft, Sept. 2013). The difference, for dromocracy, is that operators are actually buying and selling and swapping options, creating and destroying them, and all of their multidimensional and virtual but very real risks. In dromocracy, operators trade standardized options, yes, but significantly more so exotic and hyperexotic options as well. Moreover, because a war machine economy is equitable (but not equal), clusters provide operators a permanent set of impermanent venues for hedging their exposures to risk easily and cheaply. And because, as we will also see, by manner of universal synthetic CDO everyone is leveraged on top of everyone else –a leverage, natural and infinite– every operator is simultaneously an entire financial institution unto herself, together with her incessantly-changing cluster, and universally as an One.

[3] (‘A delta is expressed as the first mathematical derivative of the product with respect to the underlying asset. [This] means that it is the hedge ratio of the asset for an infinitely small move. Somehow, when the portfolio includes more than one option, with a combination of shorts and longs, delta and hedge ration start parting ways.’) Taleb pg. 115

[4] Hull pg. 360

[5] Taleb pg. 10

[6] Taleb pg. 115

The Lure of, and Luring Shadow Banking

The post-2008 rise of shadow banking continues to generate a dustup between those who view its nebulous activity as a bulwark against both illiquidity and inefficiencies in the distribution of capital, versus those who hold it as a perennial threat to global stability. The most recent Economist includes a special report on shadow banking, in which declamations of the system’s potential to vitiate regulated, on-balance sheet activity is cast aside in favor of a more fecund discussion: the increasingly acknowledged potential for use as a viable form of risk securitization and lending.

Before one enters into discussion regarding the nebulous system, a proper definition is seemingly necessary. Shadow banking, however, is difficult to define, and for obvious semantic reasons (i.e. the signifier “shadow’). The following is taken from The Economist report:

“The definition of shadow banking is itself shadowy. The term was coined in 2007 by Paul McCulley, a senior executive at PIMCO, a big asset manager, to describe the legal structures used by big Western banks before the financial crisis to keep opaque and complicated securitised loans off their balance-sheets, but it is now generally used much more broadly. The Financial Stability Board, an international watchdog set up to guard against financial crises, defines shadow banking as ‘credit intermediation involving entities and activities outside the regular banking system’—in other words, lending by anything other than a bank […] Some of these competitors are simply banks by another name, trying to boost profits by cutting regulatory corners, which is a worry. But most are genuinely different creatures, able to absorb losses more easily than banks. They are a buttress rather than a threat to financial stability.”

Thus, shadow banking isn’t really just a system of recalcitrant competitors belying financial stability. Rather, as the report states, it has become a non-entrained system with all the plenum of traditional finance, now comprising a quarter of the global financial system, with assets reportedly reaching $71 trillion in 2013 –signaling a growth of $26 trillion in the last decade alone.

As I’ve argued before, it’s quite likely we can more readily account for the growth of shadow banking than the system itself, by beginning to think, or rather examine, its attractors as (Deleuzian) singularities. Why? Simply put, unlike the system itself, its basins of attraction are in fact not new at all. Just take, for instance, The Economist’s anecdote of Hall & Woodhouse, an English brewery founded in 1777. Until this year, H&W’s financial activity was a tableau of a “traditional” business that borrowed money in the form of bank loans, and then proceeded to gradually pay back the interest and principal, thus generating revenue, a steady income, a low level of debt, and a “pristine credit record”. Hall & Woodhouse, however, encountered some trouble in 2010 following the financial crisis, when their traditional lender, the Royal Bank of Scotland, informed the business it would only renew their line of credit for three years, instead of the usual five –and notably, now at a higher interest rate. Rather than conceding to this new, more expensive line of traditional credit, Hall & Woodhouse turned to shadow banking.

The Economist reports:

“They decided they needed more reliable long-term creditors, so they reduced their bank borrowing and turned instead to a shadow bank—a financial firm that is not regulated as a bank but performs many of the same functions (see article). The one they picked was M&G (the asset-management arm of Prudential, a big insurance firm), which offered them £20m over ten years.”

The Economist’s narrative here is that shadow banking offered H&W what traditional banks no longer could, or would, thus filling the void. However, this new trajectory, at least in Hall & Woodhouse’s case, is driven by much older, perennial business practices: securitization against risk via cost-effective loans. The attractors for businesses such as Hall & Woodhouse, then, haven’t changed. Shadow banking has been increasingly replacing traditional banks, which are now bogged down by post-crisis regulations and putative risk-minimizing measures. As The Economist puts it, “[t]his retreat of the banks has allowed the shadow banking system to fill the ensuing void.” But are we truly witnessing a meaningful exodus from tradition? The structure of shadow banking paradoxically mimes that of traditional lending, albeit in a more elliptical and unregulated dimension. So increasingly we see the discourse shifting away from oversimplified declamations against shadow banking, towards arguments that borrowers are drawn towards shadow banking because it offers what traditional banks can’t, as these banks are now “beset by heavier regulation, higher capital requirements, endless legal troubles and swingeing fines.” If we examine such activities in finance as Deleuze, and after him Manuel Delanda, conceive dynamical systems –namely, as comprised of trajectories whose basin of attraction are singularities, and wherein shocks or various critical stimuli help account for the creation of a bifurcated systems like shadow banking– this “draw” to the shadows, which accounts for the system’s subsequent growth, increasingly seems obvious.

Today regulators are apparently seeking ways to promulgate the upside of the shadow banking system, their intention being the attenuation of activities potentially leading to future crises, while simultaneously utilizing the system “for good”. Much of their focus, The Economist reports, is on leverage.

“One focus is leverage, the amount an institution has borrowed relative to the amount of loss-absorbing equity its owners have put into it. Most investment funds (with the notable but small exception of hedge funds) have minimal leverage or none at all, so if they run into trouble there is little risk that other lenders will suffer as a result. Alas, such contamination was a much bigger problem for the shadowy vehicles that issued asset-backed securities before the crisis.”

Much of the post-2008 discourse initially painted shadow banking as a principal contributor to the financial meltdown, partly due to the difficulty of evaluating off-balance sheet transactions, which proved so insidious, albeit only after the fact. That being said, we now better understand the cognitive response to post-crisis risk aversion (e.g., Hall & Woodhouse); consequently, we better understand the draw to shadow banking, and we better understand, to some extent, the risks associated with it. As The Economist report observes, “[t]he sooner the regime spells out which assets are protected, the sooner investors will take more care about risk. Shadow banking can make finance safer, but only if it is clear whose money is on the line.” So perhaps the important question here is a Deleuzian inquiry into the potential utilization of this new, bifurcated system of finance, which in turn will require that we move beyond traditional approaches to the topic, and now towards an understanding of the more fungible, even topological form of trading, lending, and securitization that is shadow banking. Indeed, shadow banking has already seemingly proved itself as a viable lending and borrowing tool par excellence in a post-crisis world, comprised of comparatively fewer, if any, nascent, state-violenced regulatory structures. Therefore, the question here is, how do we really wish to utilize such a system that holds the potential for disaster, but, conversely, the potential “for good” –and of course, as always, by what do we mean “good”? Mark Carney of the Financial Stability Board recently described shadow banking as the greatest danger to the world economy. The Economist report, however, is obviously more optimistic (The report celebrates shadow banking, while still betraying some anxiety: “Shadow banking certainly has the credentials to be a global bogeyman. It is huge, fast-growing in certain forms and little understood—a powerful tool for good but, if carelessly managed, potentially explosive.”)

We know shadow banking is inherently elliptical and difficult to map. But viewed through the analytical prism of the Deleuzian ontology, perhaps we will become more capable of mapping its growth, but subsequently must also be willing to then seek out concrete ways for its positive, alternative, even radical utilizations. Most of the discourse surrounding shadow banking articulates a deep and no doubt warranted concern over inadvertently instigating another liquidity crisis-turned-solvency crisis-turned-systemic-crisis. But is it enough for political economy to always concern itself with preempting a repeat of the same mistakes of the past? Is this to be our only vocation? Shadow Banking is indeed, in actuality, relatively new. So far as we seek to draw upon a metricized balance sheet to account for, understand, and regulate the correlative risks represented therein, there does seem to be a common, perhaps warranted, but at any rate apprehension that this system is too vague, nebulous, and potentially threating to system-stability. More proactive, however, may be to continue to prepare our imagination for our financial system’s alternative potential uses in ways that eschew habit and tradition (à laNietzsche). Only then can we thus elude the perpetual, surreptitious introjection of crippling anxiety over ongoing psychological, emotional, and cognitive attachments to traditional practices that vitiate our ability to actualize the virtual potential of finance, which is so necessary for rejecting the trope of insulating security against risk: for perhaps today our true task is to favor expanding universal risk against its perpetual threats by individuated insecurity.

            — by Alex Montero



Cited web versions of The Economist report:









A division that changes in kind is thawing

Here at SpecMat we’re interested in studying the peculiar materiality of structured finance, or securitization, which is the division of a generic or synthetic asset that always changes the asset in kind. We’ve also observed (Of Synthetic Finance ch.3) that it’s a technology, if used nomadically, for the abolition of private property (albeit not quite in the way the leather-cap communists meant). That’s why we were interested to see in yesterday’s WSJ three articles illustrating the veritable resurgence of structured finance.

It is true that we’re usually most interested in the many deep ontological features of finance, which constantly just sort of leap-out at the philosopher who sets out to rigorously study the technical elements of finance. And this is because, in part, the wagers of speculative materialism require us to think the monstrous power of the synthetic -and we can only do this by first grasping our images of objects by thought, as images, and then setting out to reimagine, refashion, or otherwise engineering their alternative, radical use. But its also true that there are days upon days when we read nothing in the news but what Heidegger called “chatter” (e.g. the Dow rose 22.19 points one day, then fell 22.18 the next, the VIX is at 12, then 13, etc. etc.), and therefore there will be times where we wish to alert our readers to interesting news in finance. For after all, are we not also  trying to think the slow and incremental change by degrees that eventually if suddenly produces a change in kind?

And so?

First, Timaraos and Zibel report in “Easing of Mortgage Curb Weighed”  that US securitization originators’ previous 5% risk-retention requirement is considered being lifted by US regulators, insofar as fear and evidence suggests that it is crimping the housing recovery; notably, both consumer advocate groups and industry practitioners are on the same side on this, of wanting the requirement to be lifted because its bad for both sellers (i.e. bad for mortgage lenders and securitization originators) and results in higher mortgage interest rates (which is bad for borrowers/buyers). This article nicely illustrates once more how regulation, which attempts to curb bad actor behavior in order to preempt its obvious and undeniable detrimental effects on the market, tends to have obvious and undeniable detrimental effects on the market. The point: regulation is bad for the market.

Secondly, Yoon and Timiraos tell us in “Freddie Shifts Housing Risk” that Freddie Mac just issued $500 million in derivatives tied to the mortgages they guarantee. The notes are called “Structured Agency Credit Risk”, which means they are synthetic notes whose notional value is tied to the values of billions of dollars ($20 billion, according to the report) of residential mortgage loans. The Journal always likes to quote people from the industry, as examples of Lacan’s subjects-supposed-to-know: to this effect, apparently Steve Abrahams, a mortgage analyst for Deutsche Bank, said of the deal, “Its the beginning of an experiment [for Fannie and Freddie]’, and that the transactions could mark ‘if not an end to their existence, then a serious change to their role.’  But this statement, as well as the title to this piece, as well as the officially-asserted reason for issuing these securities, is suspicious to a SpecMater: given that synthetic assets replicate a new risk and cash flow which did not exist before, how does synthetic securitization of residential mortgages securities move towards “shifting” the housing risk away from Fannie and Freddie, who together, lets face it, keep the secondary mortgage market afloat; the more accurate term here is probably “multiplying” risk ….We can’t help but think that something more is going on here than this?! (and of course, there is also the matter that if ‘everyone’ held the notes whose values corresponded to the values of ‘everyone’s’ mortgages, then ‘everyone’ would be materially invested in everyone else’s solvency -but now we’re talking about socialism…). The point: regulation is ambivalent for the market.

But thirdly, and clearly the most interesting of these three is Bisserbe’s report, “French Banks Get their Wings Back”. French Banks, who have long been involved in the aircraft finance market, but have slowly been losing market share of this industry, are now regaining that lost market share by electing to securitize their loans to airlines. The reason why is interesting. European regulators have been increasingly forcing their banks to match long-term funding with long-term debt, which squeezes their long practiced carry trade (viz. arbitrage) of borrowing a nickel for a penny every day after day and lending that same nickel they just borrowed to someone else (like an airline) for three pennies for two days. The yield curve, in other words, has arbitrage built into its time-horizon; and French banks, like all banks, have long made good money that way. The problem is, today, what happens in money markets never stays in money markets -as we learned in 2008, when suddenly a day arrives where no one will lend the bank that penny anymore; and even if this happens for just a day, the debt stops circulating, and given that the circulation of debt is finance capitalism, which either is in motion or is nothing at all, so too it stops. Regulators get this, and because these days they’re on perpetual market suicide-watch, they have eliminated, or at least sought to greatly curb this short term, money market method of funding.

But as this article illustrates: What happens when regulators regulate? The market adapts, new technologies are born, and/or in this case it leads to the resurgence of securitization. Its really fucking brilliant. Do you want to see some financial innovation, are you looking for a repetition that produces a new difference, just regulate, and voila! The point: regulation is good for the market.

However, if you’re new to drinking you don’t start out with Oban, but rather a small glass of chilled Brut Rosé. For this reason, if you’re new to securitization, you don’t want to read a technical manual on it (even though you could also read ch.2 of Deleuze’s Bergsonism  at the same time and immediately sit down to  write a book on the qualitative multiplicities of structured financial assets), or even rely on Journal articles on it. So maybe check out Vinod Kothari’s book on securitization, which  is big and expensive, but luckily we have some chapters scanned in, and would gladly send it out to those who ask.