Tranche Declension.


With the CDO (collateralized debt obligation) market picking up, it is important to build a stronger understanding of pricing and risk management models. The role of the Gaussian copula model, has well-known deficiencies and has been criticized, but it continues to be fundamental as a starter. Here, we draw attention to the applicability of Gaussian inequalities in analyzing tranche loss sensitivity to correlation parameters for the Gaussian copula model.

We work with an RN-valued Gaussian random variable X = (X1, … , XN), where each Xj is normalized to mean 0 and variance 1, and study the equity tranche loss

L[0,a] = ∑m=1Nlm1[xm≤cm] – {∑m=1Nlm1[xm≤cm] – a}

where l1 ,…, lN > 0, a > 0, and c1,…, cN ∈ R are parameters. We thus establish an identity between the sensitivity of E[L[0,a]] to the correlation rjk = E[XjXk] and the parameters cj and ck, from where subsequently we come to the inequality

∂E[L[0,a]]/∂rjk ≤ 0

Applying this inequality to a CDO containing N names whose default behavior is governed by the Gaussian variables Xj shows that an increase in name-to-name correlation decreases expected loss in an equity tranche. This is a generalization of the well-known result for Gaussian copulas with uniform correlation.

Consider a CDO consisting of N names, with τj denoting the (random) default time of the jth name. Let

Xj = φj-1(Fjj))

where Fj is the distribution function of τj (relative to the market pricing measure), assumed to be continuous and strictly increasing, and φj is the standard Gaussian distribution function. Then for any x ∈ R we have

P[Xj ≤ x] = P[τj ≤ Fj-1j(x))] = Fj(Fj-1j(x))) = φj(x)

which means that Xj has standard Gaussian distribution. The Gaussian copula model posits that the joint distribution of the Xj is Gaussian; thus,

X = (X1, …., Xn)

is an RN-valued Gaussian variable whose marginals are all standard Gaussian. The correlation

τj = E[XjXk]

reflects the default correlation between the names j and k. Now let

pj = E[τj ≤ T] = P[Xj ≤ cj]

be the probability that the jth name defaults within a time horizon T, which is held constant, and

cj = φj−1(Fj(T))

is the default threshold of the jth name.

In schematics, when we explore the essential phenomenon, the default of name j, which happens if the default time τis within the time horizon T, results in a loss of amount lj > 0 in the CDO portfolio. Thus, the total loss during the time period [0, T] is

L = ∑m=1Nlm1[xm≤cm]

This is where we are essentially working with a one-period CDO, and ignoring discounting from the random time of actual default. A tranche is simply a range of loss for the portfolio; it is specified by a closed interval [a, b] with 0 ≤ a ≤ b. If the loss x is less than a, then this tranche is unaffected, whereas if x ≥ b then the entire tranche value b − a is eaten up by loss; in between, if a ≤ x ≤ b, the loss to the tranche is x − a. Thus, the tranche loss function t[a, b] is given by

t[a, b](x) = 0 if x < a; = x – a, if x ∈ [a, b]; = b – a; if x > b

or compactly,

t[a, b](x) = (x – a)+ – (x – b)+

From this, it is clear that t[a, b](x) is continuous in (a, b, x), and we see that it is a non-decreasing function of x. Thus, the loss in an equity tranche [0, a] is given by

t[0,a](L) = L − (L − a)+

with a > 0.


Quantum Energy Teleportation. Drunken Risibility.


Time is one of the most difficult concepts in physics. It enters in the equations in a rather artificial way – as an external parameter. Although strictly speaking time is a quantity that we measure, it is not possible in quantum physics to define a time-observable in the same way as for the other quantities that we measure (position, momentum, etc.). The intuition that we have about time is that of a uniform flow, as suggested by the regular ticks of clocks. Time flows undisturbed by the variety of events that may occur in an irregular pattern in the world. Similarly, the quantum vacuum is the most regular state one can think of. For example, a persistent superconducting current flows at a constant speed – essentially forever. Can then one use the quantum vacuum as a clock? This is a fascinating dispute in condensed-matter physics, formulated as the problem of existence of time crystals. A time crystal, by analogy with a crystal in space, is a system that displays a time-regularity under measurement, while being in the ground (vacuum) state.

Then, if there is an energy (the zero-point energy) associated with empty space, it follows via the special theory of relativity that this energy should correspond to an inertial mass. By the principle of equivalence of the general theory of relativity, inertial mass is identical with the gravitational mass. Thus, empty space must gravitate. So, how much does empty space weigh? This question brings us to the frontiers of our knowledge of vacuum – the famous problem of the cosmological constant, a problem that Einstein was wrestling with, and which is still an open issue in modern cosmology.

Finally, although we cannot locally extract the zero-point energy of the vacuum fluctuations, the vacuum state of a field can be used to transfer energy from one place to another by using only information. This protocol has been called quantum energy teleportation and uses the fact that different spatial regions of a quantum field in the ground state are entangled. It then becomes possible to extract locally energy from the vacuum by making a measurement in one place, then communicating the result to an experimentalist in a spatially remote region, who would be able then to extract energy by making an appropriate (depending on the result communicated) measurement on her or his local vacuum. This suggests that the vacuum is the primordial essence, the ousia from which everything came into existence.

Of Magnitudes, Metrization and Materiality of Abstracto-Concrete Objects.


The possibility of introducing magnitudes in a certain domain of concrete material objects is by no means immediate, granted or elementary. First of all, it is necessary to find a property of such objects that permits to compare them, so that a quasi-serial ordering be introduced in their set, that is a total linear ordering not excluding that more than one object may occupy the same position in the series. Such an ordering must then undergo a metrization, which depends on finding a fundamental measuring procedure permitting the determination of a standard sample to which the unit of measure can be bound. This also depends on the existence of an operation of physical composition, which behaves additively with respect to the quantity which we intend to measure. Only if all these conditions are satisfied will it be possible to introduce a magnitude in a proper sense, that is a function which assigns to each object of the material domain a real number. This real number represents the measure of the object with respect to the intended magnitude. This condition, by introducing an homomorphism between the domain of the material objects and that of the positive real numbers, transforms the language of analysis (that is of the concrete theory of real numbers) into a language capable of speaking faithfully and truly about those physical objects to which it is said that such a magnitude belongs.

Does the success of applying mathematics in the study of the physical world mean that this world has a mathematical structure in an ontological sense, or does it simply mean that we find in mathematics nothing but a convenient practical tool for putting order in our representations of the world? Neither of the answers to this question is right, and this is because the question itself is not correctly raised. Indeed it tacitly presupposes that the endeavour of our scientific investigations consists in facing the reality of “things” as it is, so to speak, in itself. But we know that any science is uniquely concerned with a limited “cut” operated in reality by adopting a particular point of view, that is concretely manifested by adopting a restricted number of predicates in the discourse on reality. Several skilful operational manipulations are needed in order to bring about a homomorphism with the structure of the positive real numbers. It is therefore clear that the objects that are studied by an empirical theory are by no means the rough things of everyday experience, but bundles of “attributes” (that is of properties, relations and functions), introduced through suitable operational procedures having often the explicit and declared goal of determining a concrete structure as isomorphic, or at least homomorphic, to the structure of real numbers or to some other mathematical structure. But now, if the objects of an empirical theory are entities of this kind, we are fully entitled to maintain that they are actually endowed with a mathematical structure: this is simply that structure which we have introduced through our operational procedures. However, this structure is objective and real and, with respect to it, the mathematized discourse is far from having a purely conventional and pragmatic function, with the goal of keeping our ideas in order: it is a faithful description of this structure. Of course, we could never pretend that such a discourse determines the structure of reality in a full and exhaustive way, and this for two distinct reasons: In the first place, reality (both in the sense of the totality of existing things, and of the ”whole” of any single thing), is much richer than the particular “slide” that it is possible to cut out by means of our operational manipulations. In the second place, we must be aware that a scientific object, defined as a structured set of attributes, is an abstract object, is a conceptual construction that is perfectly defined just because it is totally determined by a finite list of predicates. But concrete objects are by no means so: they are endowed with a great deal of attributes of an indefinite variety, so that they can at best exemplify with an acceptable approximation certain abstract objects that are totally encoding a given set of attributes through their corresponding predicates. The reason why such an exemplification can only be partial is that the different attributes that are simultaneously present in a concrete object are, in a way, mutually limiting themselves, so that this object does never fully exemplify anyone of them. This explains the correct sense of such common and obvious remarks as: “a rigid body, a perfect gas, an adiabatic transformation, a perfect elastic recoil, etc, do not exist in reality (or in Nature)”. Sometimes this remark is intended to vehiculate the thesis that these are nothing but intellectual fictions devoid of any correspondence with reality, but instrumentally used by scientists in order to organize their ideas. This interpretation is totally wrong, and is simply due to a confusion between encoding and exemplifying: no concrete thing encodes any finite and explicit number of characteristics that, on the contrary, can be appropriately encoded in a concept. Things can exemplify several concepts, while concepts (or abstract objects) do not exemplify the attributes they encode. Going back to the distinction between sense on the one hand, and reference or denotation on the other hand, we could also say that abstract objects belong to the level of sense, while their exemplifications belong to the level of reference, and constitute what is denoted by them. It is obvious that in the case of empirical sciences we try to construct conceptual structures (abstract objects) having empirical denotations (exemplified by concrete objects). If one has well understood this elementary but important distinction, one is in the position of correctly seeing how mathematics can concern physical objects. These objects are abstract objects, are structured sets of predicates, and there is absolutely nothing surprising in the fact that they could receive a mathematical structure (for example, a structure isomorphic to that of the positive real numbers, or to that of a given group, or of an abstract mathematical space, etc.). If it happens that these abstract objects are exemplified by concrete objects within a certain degree of approximation, we are entitled to say that the corresponding mathematical structure also holds true (with the same degree of approximation) for this domain of concrete objects. Now, in the case of physics, the abstract objects are constructed by isolating certain ontological attributes of things by means of concrete operations, so that they actually refer to things, and are exemplified by the concrete objects singled out by means of such operations up to a given degree of approximation or accuracy. In conclusion, one can maintain that mathematics constitutes at the same time the most exact language for speaking of the objects of the domain under consideration, and faithfully mirrors the concrete structure (in an ontological sense) of this domain of objects. Of course, it is very reasonable to recognize that other aspects of these things (or other attributes of them) might not be treatable by means of the particular mathematical language adopted, and this may imply either that these attributes could perhaps be handled through a different available mathematical language, or even that no mathematical language found as yet could be used for handling them.

Stationarity or Homogeneity of Random Fields


Let (Ω, F, P) be a probability space on which all random objects will be defined. A filtration {Ft : t ≥ 0} of σ-algebras, is fixed and defines the information available at each time t.

Random field: A real-valued random field is a family of random variables Z(x) indexed by x ∈ Rd together with a collection of distribution functions of the form Fx1,…,xn which satisfy

Fx1,…,xn(b1,…,bn) = P[Z(x1) ≤ b1,…,Z(xn) ≤ bn], b1,…,bn ∈ R

The mean function of Z is m(x) = E[Z(x)] whereas the covariance function and the correlation function are respectively defined as

R(x, y) = E[Z(x)Z(y)] − m(x)m(y)

c(x, y) = R(x, x)/√(R(x, x)R(y, y))

Notice that the covariance function of a random field Z is a non-negative definite function on Rd × Rd, that is if x1, . . . , xk is any collection of points in Rd, and ξ1, . . . , ξk are arbitrary real constants, then

l=1kj=1k ξlξj R(xl, xj) = ∑l=1kj=1k ξlξj E(Z(xl) Z(xj)) = E (∑j=1k ξj Z(xj))2 ≥ 0

Without loss of generality, we assumed m = 0. The property of non-negative definiteness characterizes covariance functions. Hence, given any function m : Rd → R and a non-negative definite function R : Rd × Rd → R, it is always possible to construct a random field for which m and R are the mean and covariance function, respectively.

Bochner’s Theorem: A continuous function R from Rd to the complex plane is non-negative definite if and only if it is the Fourier-Stieltjes transform of a measure F on Rd, that is the representation

R(x) = ∫Rd eix.λ dF(λ)

holds for x ∈ Rd. Here, x.λ denotes the scalar product ∑k=1d xkλk and F is a bounded,  real-valued function satisfying ∫A dF(λ) ≥ 0 ∀ measurable A ⊂ Rd

The cross covariance function is defined as R12(x, y) = E[Z1(x)Z2(y)] − m1(x)m2(y)

, where m1 and m2 are the respective mean functions. Obviously, R12(x, y) = R21(y, x). A family of processes Zι with ι belonging to some index set I can be considered as a process in the product space (Rd, I).

A central concept in the study of random fields is that of homogeneity or stationarity. A random field is homogeneous or (second-order) stationary if E[Z(x)2] is finite ∀ x and

• m(x) ≡ m is independent of x ∈ Rd

• R(x, y) solely depends on the difference x − y

Thus we may consider R(h) = Cov(Z(x), Z(x+h)) = E[Z(x) Z(x+h)] − m2, h ∈ Rd,

and denote R the covariance function of Z. In this case, the following correspondence exists between the covariance and correlation function, respectively:

c(h) = R(h)/R(o)

i.e. c(h) ∝ R(h). For this reason, the attention is confined to either c or R. Two stationary random fields Z1, Z2 are stationarily correlated if their cross covariance function R12(x, y) depends on the difference x − y only. The two random fields are uncorrelated if R12 vanishes identically.

An interesting special class of homogeneous random fields that often arise in practice is the class of isotropic fields. These are characterized by the property that the covariance function R depends only on the length ∥h∥ of the vector h:

R(h) = R(∥h∥) .

In many applications, random fields are considered as functions of “time” and “space”. In this case, the parameter set is most conveniently written as (t,x) with t ∈ R+ and x ∈ Rd. Such processes are often homogeneous in (t, x) and isotropic in x in the sense that

E[Z(t, x)Z(t + h, x + y)] = R(h, ∥y∥) ,

where R is a function from R2 into R. In such a situation, the covariance function can be written as

R(t, ∥x∥) = ∫Rλ=0 eitu Hd (λ ∥x∥) dG(u, λ),


Hd(r) = (2/r)(d – 2)/2 Γ(d/2) J(d – 2)/2 (r)

and Jm is the Bessel function of the first kind of order m and G is a multiple of a distribution function on the half plane {(λ,u)|λ ≥ 0,u ∈ R}.

Music Territorializes Time: Deleuze. Thought of the Day 19.0


Deleuze speaks of music expressed through pulsed and non-pulsed time. Pulsed time, or Chronos, is “territorialized time,” the “marking” of time through measure and repetition/return (“ritornello”) that is one expression of musically performative “time because it’s fundamentally the way in which a sonorous form, however simple it may be, marks a territory.” He continues, “Each time that there is a marking of a territoriality, there will be a pulsation of time.” If pulsed time is also musical time, and if both pulsed and musical time is territorial, what can be inferred of proximity?

Territorialization is the assemblage of proximities, in Deleuze and Guattari’s formation, bodies formed through not merely the association but the complicity of parts in their nearness to one another that make up bodies/territories distinct from other bodies/territories. Musical performance territorializes time because the performance of music takes (“appropriates”) embedded times in proximity (the curvature of the rate of change) and creates a body of aesthetic sound and practice distinct from non-territorialized or deterritorialized sound (the noises of traffic and machines or of digestive processes may have musically performative possibilities but are not themselves music or part of a musical territory without coming together through proximities to shared and changing time, to shared and changing space).

Deleuze reminds his listeners that territorialization (and thus pulsed time) may be embedded in measurement but is also contained in “development.” “[A]s soon as you can fix a sonorous form, determinable by its internal coordinates, for example melody-harmony, as soon as you can fix a sonorous form endowed with intrinsic properties, this form is subject to developments, by which it is transformed into other forms or enters into relation or again is connected to other forms, and here, following these transformations and these connections, you can fix pulsations of time.” Pulsations of time, or Chronos, then become even more subject to musical proximities that change in response to one another, an echo of borderswerving’s relationship to borderlinking.

Non-pulsed time, Aion, is defined rather by deterritorialization and the taking apart of “sonorous form.” Deleuze relates non-pulsed time to velocity, recalling the rate of change described by DeLanda and the dromoscopy of Paul Virilio. Aionic time is part of what Deleuze terms the “mixture” of time; Aion and Chronos blend together in a musical territorializing that is also a deterritorializing, and in these opposite yet proximal movements are proximities of becoming and unmaking. The experience of participating in a musical performance as a whole (instruments, performers, audience, composition, context, etc.) and as its component parts (notes, phrases, measures, dynamics, individual characteristics of performers and instruments) is the experience of this mixed time.

And yet, not all mixed time is musical, and not all sounds in proximity are musical. For music to occur, aesthetic proximities must become aware of their possibilities for becoming and unmaking, must perceive the friction and soothing of their near surfaces. As Deleuze questions, “¿Cuando [sic] deviene musical una voz? Yo diría, desde el punto de vista de la expresión, que la voz musical es esencialmente una voz desterrito- rializada. ¿Qué quiere decir eso? Pienso que hay cosas que aún no son música y que, sin embargo, están muy próximas a la música.” (When does a voice become musical? I would say, from the point of view of expression, that the musical voice is essentially a deterritorialized voice. Why does one want to say that? I think that there are things that are not music, and that nevertheless are very close to music.)