Continuous tenor extension of affine LIBOR models with multiple curves and applications to XVA
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Abstract
We consider the class of affine LIBOR models with multiple curves, which is an analytically tractable class of discrete tenor models that easily accommodates positive or negative interest rates and positive spreads. By introducing an interpolating function, we extend the affine LIBOR models to a continuous tenor and derive expressions for the instantaneous forward rate and the short rate. We show that the continuous tenor model is arbitragefree, that the analytical tractability is retained under the spot martingale measure, and that under mild conditions an interpolating function can be found such that the extended model fits any initial forward curve. This allows us to compute value adjustments (i.e. XVAs) consistently, by solving the corresponding ‘predefault’ BSDE. As an application, we compute the price and value adjustments for a basis swap, and study the model risk associated to different interpolating functions.
Keywords
Affine LIBOR models Multiple curves Discrete tenor Continuous tenor Interpolation XVA Model riskMathematics Subject Classification
91G30 91G20 60G44Introduction
In the aftermath of the credit crisis and the European sovereign debt crisis, several of the classical paradigms in finance were no longer able to describe the new reality and needed to be designed afresh. On the one hand, significant spreads have appeared between rates of different tenors, which led to the development of multiple curve interest rate models. On the other hand, counterparty credit risk has emerged as the native form of default risk, along with liquidity risk, funding constraints and the collateralization of trades. Therefore, in postcrisis markets the quoted price of a derivative product (or, better, the cost of its hedging portfolio) is computed as the “clean” price of the product together with several value adjustments that reflect counterparty credit risk, liquidity risk, funding constraints, etc. In the context of interest rate derivatives, the “clean” price is typically computed as the discounted expected payoff under a martingale measure using a (discrete tenor) LIBOR market model, while the value adjustments are provided via the solution of a BSDE, which requires the existence of a short rate to discount the cash flows.
The aim of this work is to compute prices and value adjustments consistently, in the sense that we only calibrate a discrete tenor LIBOR model and then infer the dynamics of the short rate from it, instead of resorting to an additional, external short rate model. In the sequel, we will work with the class of affine LIBOR models with multiple curves. This class of models easily produces positive interest rates and positive spreads, as well as negative interest rates alongside positive spreads. Moreover, the models are analytically tractable in the sense that the driving process remains affine under all forward measures, which allows deriving of explicit expressions for the prices of caplets and semianalytic expressions for swaptions. Thus, these models can be efficiently calibrated to market data; cf. Grbac et al. (2015) for more details.
Once the affine LIBOR model has been set up, we introduce an interpolating function that allows extending the model from a discrete to a continuous tenor, and derive explicit expressions for the dynamics of the instantaneous forward rate and of the short rate. This part follows and extends KellerRessel (2009), while a similar interpolation for affine LIBOR models has been recently introduced by Cuchiero et al. (2016). Moreover, we show that the resulting continuous tenor model is arbitragefree and belongs to the class of affine term structure models. Let us mention that, on the contrary, the arbitragefree interpolation of “classical” LIBOR market models is a challenging task; see e.g., Beveridge and Joshi (2012). In addition, we show that the driving process remains an affine process under the spot martingale measure, hence also the short rate is analytically tractable under this measure. The choice of the interpolating function is not innocuous though, as it may lead to undesirable behavior of the short rate; e.g., it may induce jumps at fixed times. Thus, we investigate what properties the (discrete tenor) affine LIBOR model and the interpolating function should have in order to avoid such situations. In particular, we show that under mild assumptions there exists an interpolating function such that the extended model can fit any initial forward curve.
Then, we can compute value adjustments via solutions of a “predefault” BSDE using the framework of Crépey (2015a,b). As an illustration, we design and calibrate an affine LIBOR model, and consider a simple postcrisis interest rate derivative, namely a basis swap. Using the methodology outlined above, we derive the dynamics of the short rate and of the basis swap using an interpolating function, and compute the value adjustments for different specifications of the contract. As the choice of an interpolating function is still arbitrary, we study the model risk associated to different choices.
This paper is organized as follows: Section “Affine processes on \({\mathbb {R}}_{\geqslant 0}^{d}\)” reviews affine processes and Section “Affine LIBOR models with multiple curves” presents an overview of multiple curve markets and affine LIBOR models. Section “Continuous tenor extension of affine LIBOR models” focuses on the continuous tenor extension of affine LIBOR models and studies the properties of interpolating functions. The final Section “Computation of XVA in affine LIBOR models” outlines the computation of value adjustments in affine LIBOR models, and discusses the model risk associated with the choice of interpolating functions. The Appendix contains a useful result on the time integration of affine processes.
Affine processes on \({\mathbb {R}}_{\geqslant 0}^{d}\)
This section provides a brief overview of the basic notions and properties of affine processes. Proofs and further details can be found in Duffie et al. (2003), in KellerRessel (2008), and in Filipović (2005) for the timeinhomogeneous case.
Let Open image in new window denote a complete stochastic basis in the sense of Jacod and Shiryaev (2003, Def. I.1.3), where \(\mathbb F=(\mathcal {F}_{t})_{t\in [0,T]}\) and T∈[0,∞) denotes the time horizon. In the sequel, we will consider a process X that satisfies the following:
Assumption (\(\mathbb {A}\))
 (i)
\(0\in \mathcal {I}_{T}^{\circ }\), where \(\mathcal I_{T}^{\circ }\) denotes the interior of \(\mathcal I_{T}\);
 (ii)The conditional moment generating function of X_{ t } under Open image in new window has exponentiallyaffine dependence on x; that is, there exist functions \(\phi \colon [0,T]\times \mathcal {I}_{T}\rightarrow \mathbb {R}\) and \(\psi \colon [0,T]\times \mathcal {I}_{T}\rightarrow {\mathbb {R}}^{d}\) such that
for all \((t,u,x)\in [0,T]\times \mathcal {I}_{T}\times D\).
for all \(f\in C_{0}^{2}(D)\) and x∈D.
Additional results are summarized in the following lemma. In the sequel, inequalities have to be understood componentwise, in the sense that (a_{1},a_{2})≤(b_{1},b_{2}) if and only if a_{1}≤b_{1} and a_{2}≤b_{2}.
Lemma 1
 1.
ϕ_{ t }(0)=ψ_{ t }(0)=0 for all t∈[0,T].
 2.
\(\mathcal {I}_{T}\) is a convex set; moreover, for each t∈[0,T], the functions u↦ϕ_{ t }(u) and u↦ψ_{ t }(u), for \(u\in \mathcal {I}_{T}\), are (componentwise) convex.
 3.ϕ_{ t }(·) and ψ_{ t }(·) are order preserving: let \((t,u),\,(t,v)\in [0,T]\times \mathcal {I}_{T}\), with u≤v. Then$$ \phi_{t}(u)\leq\phi_{t}(v) \quad \text{and} \quad \psi_{t}(u)\leq\psi_{t}(v). $$(6)
 4.
ψ_{ t }(·) is strictly orderpreserving: let \((t,u),\,(t,v)\in [0,T]\times \mathcal {I}_{T}^{\circ }\), with u<v. Then ψ_{ t }(u)<ψ_{ t }(v).
 5.
ϕ and ψ are jointly continuous on \([0,T]\times \mathcal {I}_{T}^{\circ }\).
 6.The partial derivativesexist and are continuous for \(\left (t,u\right)\in [0,T]\times \mathcal {I}_{T}^{\circ }\).$$ \frac{\partial}{\partial u_{i}}\phi_{t}(u) \quad \text{and} \quad \frac{\partial}{\partial u_{i}}\psi_{t}(u),\quad i=1,\dots,d $$
Proof
See KellerRessel et al. (2013, Lem. 4.2) for statements (1)–(4) and KellerRessel (2008, Prop. 3.16 and Lem. 3.17) for the last two. □
Affine LIBOR models with multiple curves
A multiple curve setting
We start by introducing some basic notation and the main concepts used in multiple curve LIBOR models, following the approach introduced by Mercurio (2010); see also Grbac et al. (2015) for an overview and more details.
The emergence of significant spreads between the OIS and LIBOR rates which depend on the investment horizon, also called tenor, means that we cannot work with a single tenor structure any longer. Let \(\mathcal {T}=\{0=T_{0}<T_{1}<\cdots <T_{N} = T\}\) denote a discrete, equidistant time structure where T_{ k }, for \(k\in \mathcal {K}=\{1,\dots,N\}\), denote the relevant market dates, e.g., payment dates and maturities of traded instruments. The set of tenors is denoted by \(\mathcal {X}=\{x_{1},\dots,x_{n}\}\), where we typically have \(\mathcal {X}=\{1,3,6,12\}\) months. Then, for every \(x\in \mathcal {X}\) we consider the corresponding tenor structure \(\mathcal {T}^{x}=\{0=T_{0}^{x}<T_{1}^{x}<\cdots <T_{N^{x}}^{x}=T_{N}\}\) with constant tenor length \(\delta _{x}=T_{k}^{x}T_{k1}^{x}\). We denote by \(\mathcal {K}^{x}=\{1,\dots,N^{x}\}\) the collection of all subscripts related to the tenor structure \(\mathcal {T}^{x}\), and assume that \(\mathcal {T}^{x}\subseteq \mathcal {T}\) for all \(x\in \mathcal {X}\).
The Overnight Indexed Swap (OIS) rate is regarded as the best market proxy for the riskfree interest rate. Moreover, the majority of traded interest rate derivatives are nowadays collateralized and the remuneration of the collateral is based on the overnight rate. Therefore, the discount factors B(0,T) are assumed to be stripped from OIS rates and defined for every possible maturity \(T\in \mathcal {T}\); see also Grbac and Runggaldier (2015, §1.3.1). B(t,T) denotes the discount factor, i.e., the timet price of a zero coupon bond with maturity T, which is assumed to coincide with the corresponding OISbased zero coupon bond.
The expectations with respect to the forward measures Open image in new window and the terminal measure Open image in new window are denoted by Open image in new window and Open image in new window , respectively.
Next, we define the main modeling objects in the multiple curve LIBOR setting: the OIS forward rate, the forward LIBOR rate and the corresponding spread.
Definition 1
Definition 2
where \(L\left (T_{k1}^{x},T_{k}^{x}\right)\) denotes the spot LIBOR rate at time \(T_{k1}^{x}\) for the time interval \(\left [T_{k1}^{x},T_{k}^{x}\right ]\).
Definition 3
Remark 1
In a single curve setup, the forward LIBOR rate is defined via (11) and the spread equals zero for all times. However, in a multiple curve model these rates are not equal any more and we typically have that \(L_{k}^{x} \geq F_{k}^{x}\). \(F_{k}^{x}\) and \(L_{k}^{x}\) can also be interpreted as forward rates corresponding to a riskless and a risky bond, respectively; see e.g., Crépey et al. (2012).
Affine LIBOR models with multiple curves
is a martingale. Moreover, if \(u\in \mathcal {I}_{T}\cap \mathbb {R}_{\geqslant 0}^{d}\) the mapping \(u \mapsto M_{t}^{u}\) is increasing and \(M^{u}_{t}\ge 1\) for every t∈[0,T]; see KellerRessel et al. (2013, Thm. 5.1) and Papapantoleon (2010).
The multiple curve affine LIBOR models are defined as follows:
Definition 4

An affine process X under Open image in new window satisfying Assumption (\(\mathbb {A}\)) and starting at the canonical value 1.

A set of tenors \(\mathcal {X}\).

A terminal maturity T_{ N }.
 A sequence of vectors u=(u_{1},…,u_{ N }) with \(u_{l}=:u_{k}^{x}\in \mathcal {I}_{T}\cap \mathbb {R}_{\geqslant 0}^{d}\), for all \(l=kT_{1}^{x}/T_{1}\) and \(x\in \mathcal {X}\), such that$$ u_{1} \ge u_{2} \ge \cdots \ge u_{N}=0. $$(14)
 A collection of sequences of vectors \(v={\left \{(v_{1}^{x},\dots,v^{x}_{N^{x}})\right \}}_{x\in \mathcal {X}}\) with \(v_{k}^{x}\in \mathcal {I}_{T}\cap \mathbb {R}_{\geqslant 0}^{d}\), such that$$ \begin{aligned} v_{k}^{x} \ge u_{k}^{x} \quad \text{for all}~ k\in\mathcal{K}^{x},x\in\mathcal{X}. \end{aligned} $$(15)
for all \(t\in [0,T_{k}^{x}]\), \(k\in \mathcal {K}^{x}\) and \(x\in \mathcal {X}\).
The definition of multiple curve affine LIBOR models implies that the dynamics of OIS forward rates and forward LIBOR rates, more precisely of \(1+\delta _{x}F_{k}^{x}\) and \(1+\delta _{x}L_{k}^{x}\), exhibit an exponentialaffine dependence in the driving process X; see (13) and (16). Glau et al. (2016) recently showed that models that exhibit this exponentialaffine dependence are the only ones that produce structure preserving LIBOR models; cf. Proposition 3.11 therein. The denominators in (16) are the same in both cases, since both rates have to be Open image in new window martingales by definition. On the other hand, different sequences \((u_{l})_{l\in \mathcal {K}}\) and \(\left (v_{k}^{x}\right)_{k\in \mathcal {K}^{x}}\) are used in the numerators in (16) producing different dynamics for OIS and LIBOR rates. These sequences are used to fit the multiple curve affine LIBOR model to a given initial term structure of OIS and LIBOR rates. In particular, the subsequent propositions show that by fitting the model to the initial term structure we automatically obtain sequences \((u_{l})_{l\in \mathcal {K}}\) and \((v_{k}^{x})_{k\in \mathcal {K}^{x}}\) that satisfy (14) and (15), respectively; see also Grbac et al. (2015, Rem. 4.4 and 4.5) for further comments on these sequences.
In several models commonly used in mathematical finance, such as the Cox–Ingersoll–Ross model and Ornstein–Uhlenbeck processes driven by subordinators, this quantity equals infinity. The following propositions show that the affine LIBOR models are welldefined and can fit any initial term structure under mild conditions.
Proposition 1
 1.If γ_{ X }>B(0,T_{1})/B(0,T_{ N }), there exists a sequence \((u_{l})_{l\in \mathcal {K}}\) in \(\mathcal {I}_{T}\cap \mathbb {R}_{\geqslant 0}^{d}\) satisfying (14) such thatIn particular, if γ_{ X }=∞, then the multiple curve affine LIBOR model can fit any initial term structure of OIS rates.$$ M_{0}^{u_{l}} = \frac{B(0,T_{l})}{B(0,T_{N})}\quad\text{for all }l\in\mathcal{K}. $$
 2.
If X is onedimensional, the sequence \((u_{l})_{l\in \mathcal {K}}\) is unique.
 3.
If all initial OIS rates are positive, the sequence \((u_{l})_{l\in \mathcal {K}}\) is strictly decreasing.
Proof
See Proposition 6.1 in KellerRessel et al. (2013). □
Proposition 2
 1.If \(\gamma _{X}>(1+\delta _{x}L_{k}^{x}(0))B(0,T_{k}^{x})/B(0,T_{N})\) for all \(k\in \mathcal {K}^{x}\), then there exists a sequence \((v_{k}^{x})_{k\in \mathcal {K}^{x}}\) in \({\mathcal {I}}_{T}\cap {\mathbb {R}_{\geqslant 0}}^{d}\) satisfying (15) such thatIn particular, if γ_{ X }=∞, then the multiple curve affine LIBOR model can fit any initial term structure of forward LIBOR rates.$$ M_{0}^{v_{k}^{x}} = \left(1+\delta_{x}L_{k+1}^{x}(0)\right) M_{0}^{u_{k+1}^{x}}, \quad \text{for all}~ k\in\mathcal{K}^{x}\setminus\{N^{x}\}. $$
 2.
If X is onedimensional, the sequence \((v_{k}^{x})_{k\in \mathcal {K}^{x}}\) is unique.
 3.
If all initial LIBOROIS spreads are positive (i.e., (19) becomes strict), then \(v_{k}^{x}>u_{k}^{x}\), for all \(k\in \mathcal {K}^{x}\setminus \{N^{x}\}\).
Proof
See Proposition 4.2 in Grbac et al. (2015). □
Remark 2
However, any other continuous path from another u^{′} to 0, that satisfies (20) and is componentwise decreasing, would have worked as well.
The next proposition shows that multiple curve affine LIBOR models are analytically tractable, in the sense that the affine structure is preserved under any forward measure.
Proposition 3
Proof
See Proposition 4.6 in Grbac et al. (2015). □
The multiple curve affine LIBOR models defined above and satisfying the prerequisites of Propositions 1 and 2 are arbitragefree discrete tenor models, in the sense that \(F_{k}^{x}\) and \(L_{k}^{x}\) are Open image in new window martingales for every \(k\in \mathcal {K}^{x}\), \(x\in \mathcal {X}\), while the interest rates and the spread are positive, i.e., \(F_{k}^{x}(t)\ge 0\) and \(S_{k}^{x}(t)=L_{k}^{x}(t)F_{k}^{x}(t)\ge 0\) for every \(t\in [0,T_{k1}^{x}], k\in \mathcal {K}^{x}\), \(x\in \mathcal {X}\); cf. Proposition 4.3 in Grbac et al. (2015).
Remark 3
The class of affine LIBOR models with multiple curves can be extended to accommodate negative interest rates alongside positive spreads; see Grbac et al. (2015, §4.1) for the details.
Remark 4
We could use timedependent parameters, i.e., timeinhomogeneous affine processes, in the construction of affine LIBOR models, in particular since the dynamics of X are timedependent under forward measures; see Proposition 3. We use affine processes instead, in order to ease the presentation of the model and its properties, and to be consistent with the relevant literature (cf. KellerRessel et al. 2013 and Grbac et al. 2015).
Continuous tenor extension of affine LIBOR models
Discrete to continuous tenor
This section is devoted to the extension of the affine LIBOR models from a discrete to a continuous tenor structure, and the derivation of the dynamics of the corresponding instantaneous forward rate and short rate. The main tool is an interpolating function Open image in new window , which is a function defined on [0,T_{ N }] that matches u_{ l } at each tenor date T_{ l }. This subsection follows and extends KellerRessel (2009).
Definition 5
An interpolating function for the multiple curve affine LIBOR model \((X,\mathcal {X},T_{N},u,v)\) is a continuous, componentwise decreasing function Open image in new window with Open image in new window for all t∈[0,T_{ N }] and bounded righthand derivatives, such that Open image in new window for all \(T_{l}\in \mathcal {T}\).
Remark 5
Since Open image in new window is a mapping from [0,T_{ N }], it makes sense to define a Open image in new window element. This can be chosen such that Open image in new window which is consistent with Proposition 1.
The interpolating function allows deriving of an explicit expression for the dynamics of zero coupon bond prices in the multiple curve affine LIBOR model. In particular, they belong to the class of affine term structure models; see e.g., Björk (2009, §24.3).
Lemma 2
Proof
where ⌊t⌋ is such that T_{⌊t⌋} is the largest element in the time structure \(\mathcal {T}\) less than or equal to t. Hence, since Open image in new window depends exponentiallyaffine on X_{ t }, we arrive at (22)–(23). □
Next, we will show that the extension of an affine LIBOR model from a discrete to a continuous tenor is an arbitragefree term structure model. Following Musiela and Rutkowski (1997, Def. 2.3), we say that a family of bond prices Open image in new window satisfies a noarbitrage condition if there exists a measure Open image in new window such that Open image in new window is a Open image in new window local martingale and Open image in new window for any Open image in new window
Theorem 1
Let Open image in new window be an interpolating function for the multiple curve affine LIBOR model \((X,\mathcal {X},T_{N},u,v)\). Then, Open image in new window is a continuous tenor extension of the affine LIBOR model, i.e., an arbitragefree model for all maturities Open image in new window , such that for all maturities \(T\in \mathcal {T}\) the bond prices coincide with those of the (discrete tenor) affine LIBOR model.
Proof
The definition of the interpolating function yields immediately that bond prices in the continuous tenor extension coincide with those of the discrete tenor affine LIBOR model for all maturities.
 (i)
Open image in new window is a strictly positive special semimartingale and the lefthand limit process Open image in new window is also strictly positive, for every Open image in new window
 (ii)
The bond price quotients Open image in new window are Open image in new window martingales.
 (iii)
B(t,S)≤B(t,U) for all 0≤t≤S≤U≤T_{ N }.
The second condition follows immediately from (21) and the construction of M^{ u } as a Open image in new window martingale. In order to check the first and the third conditions, we shall use the representation for the bond prices from Lemma 2. Indeed, the last condition follows directly from representation (22)–(23), using the monotonicity of the function Open image in new window and the order preserving property of ϕ and ψ; cf. Lemma 1. Moreover, the continuity of ϕ and ψ together with (22) imply that Open image in new window which ensures the positivity of Open image in new window
Lemma 3
for all Open image in new window Here, a∘b denotes the componentwise multiplication of two vectors a and b having the same dimension.
Proof
We know from Lemma 2 that bond prices are logaffine functions of X, in particular they are provided by (22)–(23). The result now follows by taking the righthand derivative of Open image in new window w.r.t. Open image in new window , which exists by Definition 5 and Lemma 1(6). Note also that Open image in new window and Open image in new window are positive, for all Open image in new window □
Lemma 4
Let Open image in new window be an interpolating function and consider the continuous tenor extension of the affine LIBOR model \({(X,\mathcal {X},T_{N},u,v)}\). Then, the spot measure Open image in new window is determined by the density process
where Open image in new window and Open image in new window .
Proof
The spot measure and the terminal forward measure are related via
cf. Musiela and Rutkowski (2005, §13.2.2). Then, the representation above follows easily from (28), (22)–(23), Lemma 1(1), and Remark 5, using the fact that Open image in new window hence Open image in new window □
The next result resembles Proposition 3 and shows that the driving process X remains an affine process under the spot measure Open image in new window . In other words, the multiple curve affine LIBOR model remains analytically tractable under the spot measure as well.
Theorem 2
Proof
compare with (7). The semigroup of the affine process X under Open image in new window is weakly regular in the sense of Filipović (2005, Def. 2.3), since the process X is stochastically continuous under Open image in new window and the generator exists and is continuous at w=0 for all (t,x)∈[0,T]×D. Moreover, X is strongly regular affine under Open image in new window since the weakly admissible parameters (α^{⋆}(t),b^{⋆}(t),β^{⋆}(t),m^{⋆}(t),μ^{⋆}(t)) implied by (30) are continuous transformations of (α,b,β,m,μ). □
On the choice of the interpolating function
The next corollary is an immediate consequence of Lemma 3 and the fact that X is stochastically continuous.
Corollary 1
Let Open image in new window be a continuously differentiable interpolating function, i.e., Open image in new window , and consider the continuous tenor extension of the affine LIBOR model \({(X,\mathcal {X},T_{N},u,v)}\). Then the short rate process r is stochastically continuous.
However, even when the interpolating function is continuously differentiable there can be sources of undesirable behavior of the short rate inherited from the sequence (u_{ k }) itself (which is not unique unless d=1; cf. Remark 2). Consider, for example, the following “diagonal” structure for (u_{ k }), which is similar to the one employed by Grbac et al. (2015) to model independence between rates of different maturities:
We would like in the sequel to provide conditions such that the short rate resulting from a continuous tenor extension of an affine LIBOR model exhibits “reasonable” behavior, in the sense that it neither jumps at fixed times, nor drops to zero at each maturity date. Moreover, we would like to identify a method for choosing an interpolating function that removes the arbitrariness from this choice. A condition for the former is that the sequence \((u_{k})_{k\in \mathcal {K}}\) lies on a smooth manifold. Regarding the latter, we could require that a continuum of bond prices are fitted as well. These together lead to a uniquely defined, continuously differentiable interpolating function.
Example 1
for all Open image in new window This equation follows directly from the requirement that (34) holds for all Open image in new window together with (22)–(23) and Remark 5. Moreover, the dynamics of the instantaneous forward rate are provided by (26)–(27) for all T∈[0,T_{ N }], and satisfy the initial condition Open image in new window
The curve fitting problem in (35) can be solved analogously to the problem of fitting the sequence (u_{ k }) to an initial term structure of bond prices; compare with Proposition 6.1 in KellerRessel et al. (2013) and the corresponding proof. The resulting interpolating function is then differentiable with respect to time, as the following result shows.
Proposition 4
Let \({(X,\mathcal {X},T_{N},u,v)}\) be a multiple curve affine LIBOR model, assume that \(\tilde f(0,\cdot):[0,T_{N}]\to \mathbb {R}_{\geqslant 0}\) is continuous and that \((u_{k})_{k\in \mathcal {K}}\) allows for an interpolating function that maps onto a C^{1}manifold Open image in new window Then, there exists a unique continuously differentiable interpolating function Open image in new window satisfying (35).
Proof
Remark 6
Figure 1 reveals another interesting behavior of the short rate implied by an affine LIBOR model. In particular, there exists a lower bound for the short rate that is greater than zero. Indeed, since the state space of our driving affine process is \(\mathbb {R}_{\geqslant 0}^{d}\), we have that r_{ t }≥p_{ t }, which is greater than zero as ϕ is strictly order preserving and u is decreasing. A similar phenomenon was already observed in the discrete tenor model for the LIBOR rate, compare with KellerRessel et al. (2013, Rem. 6.4).
Computation of XVA in affine LIBOR models
The quoted price of a derivative product in precrisis markets was equal to its discounted expected payoff (under a martingale measure), since counterparties were considered defaultfree, there was abundance of liquidity in the markets, and other frictions were also negligible. In postcrisis markets, however, these assumptions have been challenged; in particular, counterparty credit risk has emerged as the natural form of default risk, there is shortage of liquidity in financial markets, while other frictions have also gained importance. These facts have thus to be factored into the quoted price. One way to do that, is to compute first the socalled “clean” price of the derivative, which equals its discounted expected payoff (under a martingale measure), and then add to it several value adjustments, collectively abbreviated as XVA, that reflect counterparty credit risk, liquidity costs, etc. We refer to Brigo et al. (2013), Crépey (2015a,b), Crépey and Bielecki (2014), and Bichuch et al. (2016) among others for more details on XVA.
Clean valuation
This section reviews basis swaps and provides formulas for their clean price in affine LIBOR models with multiple curves. The clean valuation of caps, swaptions, and basis swaptions in these models is extensively studied in Grbac et al. (2015).
The typical example of an interest rate swap is where a floating rate is exchanged for a fixed rate; see, e.g., Musiela and Rutkowski (2005, §9.4). The appearance of significant spreads between rates of different tenors has given rise to a new kind of interest rate swap, called basis swap, where two streams of floating payments linked to underlying rates of different tenors are exchanged. As an example, in a 3M6M basis swap linked to the LIBOR, the 3month LIBOR is paid quarterly and the 6month LIBOR is received semiannually.
where \(S_{r}=S_{r}\left (\mathcal {T}_{p_{1}q_{1}}^{x_{1}},\mathcal {T}_{p_{2}q_{2}}^{x_{2}}\right)\), for \(r\in \left [0,T_{p_{1}}^{x_{1}}\right ]\) being the date of inception, while \(\lceil t \rceil _{i} = \min \left \{ k\in \mathcal {K}^{x_{i}}: t<T_{k}^{x_{i}} \right \}\).
Remark 7
Basis swaps are postcrisis financial products, which can only be priced in models accounting for the multiple curve nature of interest rates. In a single curve model, the price of a basis swap is zero; cf. Crépey et al. (2012, p. 181)
XVA equations
Our approach to the computation of VA closely follows the work of Crépey (2015a,b), while our exposition and numerical examples are based on Crépey et al. (2013), and Crépey et al. (2015, §5.1).
We consider two counterparties, called a bank and an investor in the sequel, that are both defaultable, and denote by τ_{ b } the default time of the bank, by τ_{ i } the default time of the investor, while we set τ=τ_{ b }∧τ_{ i }∧T. The default intensities of τ_{ b },τ_{ i }, and τ are denoted γ_{ b },γ_{ i }, and γ, respectively. We also consider the “full model” filtration \(\mathbb {G}\), which is given by \(\mathbb {F}\) enlarged by the natural filtrations of the default times τ_{ b } and τ_{ i }, and assume that the immersion hypothesis holds, that is, every \(\mathbb {F}\)martingale stopped at τ is a \(\mathbb {G}\)martingale.

\(\gamma _{t}^{i},\,\gamma _{t}^{b}\), and γ_{ t } are the default intensities of the investor, the bank, and the first to default intensity, respectively.

ρ^{ i }, ρ^{ b } are the recovery rates of the investor and the bank to each other, and \(\mathfrak {r}\) is the recovery rate of the bank to its unsecured funder (which is a third party that jumps in when the banks’ internal sources of funding have been depleted; this funder is assumed to be risk free).

Q_{ t } is the value of the contract according to some valuation scheme specified in the credit support annex (CSA), which is a common part in an overthecounter contract.

\(\Gamma _{t}=\Gamma _{t}^{+}\Gamma _{t}^{}\) is the value of the collateral posted by the bank to the investor.

\(b_{t},\,\bar {b}_{t}\) and \(\lambda _{t},\,\bar {\lambda }_{t}\) are the spreads over the risk free rate r_{ t } corresponding to the remuneration of collateral and external lending and borrowing (from the unsecured funder).
The value of the contract Q and of the collateral Γ, as well as the funding coefficients b and \(\bar {b}\) are specified in the CSA of the contract, which is used to mitigate counterparty risk. Different CSA specifications will result in different behavior of the VA; see Crépey et al. (2013, Sec. 3) and the next subsection for more details.
Remark 8
The immersion hypothesis implies weak or indirect dependence between the contract and the default times of the involved parties. Therefore, not every contract can be priced within the predefault VA framework. As interest rate contracts exhibit weak dependence on the default times, this approach is appropriate for our setting; see also Crépey (2015b, Rem. 2.3).
XVA computation in affine LIBOR models
We are now interested in computing the value adjustments for interest rate derivatives, and focus on basis swaps as a prime example of a postcrisis product. The OIS forward rate and the forward LIBOR rate for each tenor are modeled according to the affine LIBOR models with multiple curves, and the model is calibrated to caplet data; see Grbac et al. (2015, §8) for details on the calibration of affine LIBOR models. An interpolating function is subsequently chosen and the dynamics of the short rate process are derived. Afterwards, the computation of the value adjustments is a straightforward application of the VA BSDE in (37).
This methodology allows us to compute option prices and value adjustments consistently since we only have to calibrate the discretetenor affine LIBOR model, while the dynamics of the short rate process, which is essential in the computation of the VA, follows from the interpolation. In particular, we do not need to introduce and calibrate (or estimate) an “exogenous” model for the short rate, as is done in other approaches. The interpolating function thus plays a crucial role in our methodology, since this is the only “free‘” ingredient once the affine LIBOR model has been calibrated. At the same time, it introduces an element of model risk, through the different possible choices of interpolating functions. In the sequel, we are going to examine the impact of different interpolating functions on the value adjustments.

(IF_{1}): Interpolation by fitting an entire forward curve (see Example 1);

(IF_{2}): Linear interpolation between the u_{ k }’s;

(IF_{3}): Spline interpolation on sectors where all but one component of the vector u_{ k } are constant in k, and linear interpolation in between these sectors (i.e., when the u_{ k }’s lie on curved segments of the manifold).
The first three CSA specifications correspond to a “clean” recovery scheme without collateralization, since the value of the contract Q equals the clean price and there is no collateral posted. The fourth specification corresponds to a “predefault” recovery scheme without collateralization, while the last one corresponds to a fully collateralized contract. Moreover, the first specification yields a linear BSDE in the VA Θ, which allows to use (forward) Monte Carlo simulations for the computation of the VA.
Discussion
Figure 6 depicts the sample path of the VA process using the first interpolating function (left panels) for the five different CSA specifications (top to bottom), while the other figures show the differences in the VA due to the different interpolating functions. The differences in the VA are one order of magnitude smaller than the differences in prices, however, the VA itself is an order of magnitude smaller than the basis swap price. Reflecting the situation for the prices, the largest discrepancies between VAs using the first and the other two interpolating functions occur around the curved section of the manifold. However, the discrepancies in the VA in the flat sections of the manifold are more pronounced than the corresponding discrepancies in prices. The reason is that the interpolation affects value adjustments both via the basis swap price and via the short rate used for discounting, and its effect is propagated in different segments through the backward regression. This becomes clear when one looks at the differences between prices and value adjustments stemming from using the second and third interpolating functions; although the difference in prices is flat zero, the difference in value adjustments is far from zero.
The numerical examples presented above show that the choice of the interpolating function entails significant model risk. The functions we chose are not especially far apart, in terms of their supremum norm, thus the differences above could become even higher. In fact, the coefficients of the short rate can become arbitrarily large due to the interpolating function. Therefore, both the manifolds on which the sequences u and v lie and the interpolating function have to be selected with caution, as they can fundamentally change the behavior of the model.
Appendix A
Timeintegration of Affine processes
The following result is an extension of Theorem 4.10 in KellerRessel (2008).
Theorem 3
Here, ∘ denotes the componentwise multiplication between vectors.
Proof
Notes
Acknowledgements
RW acknowledges funding from the Excellence Initiative of the German Research Foundation (DFG) under grant ZUK 64. Financial support from the Europlace Institute of Finance project “Postcrisis models for interest rate markets” is gratefully acknowledged.
Authors’ contributions
Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
References
 Beveridge, C, Joshi, M: Interpolation schemes in the displaceddiffusion LIBOR market model. SIAM. J. Finan. Math. 3, 593–604 (2012).MathSciNetCrossRefMATHGoogle Scholar
 Bichuch, M, Capponi, A, Sturm, S: Arbitragefree XVA. Math.Finan. (2016). https://arxiv.org/abs/1608.02690.Google Scholar
 Björk, T: Arbitrage Theory in Continuous Time. 3rd edition. Oxford University Press, Chichester (2009).MATHGoogle Scholar
 Brigo, D, Morini, M, Pallavicini, A: Counterparty Credit Risk, Collateral and Funding: with Pricing Cases for all Asset Classes. Wiley (2013).Google Scholar
 Crépey, S: Bilateral Counterparty risk under funding constraints — Part I: Pricing. Math. Finan. 25, 1–22 (2015a).MathSciNetCrossRefMATHGoogle Scholar
 Crépey, S: Bilateral Counterparty risk under funding constraints — Part II: CVA. Math. Finan. 25, 23–50 (2015b).MathSciNetCrossRefMATHGoogle Scholar
 Crépey, S, Bielecki, TR: Counterparty Risk and Funding: A Tale of two Puzzles. Chapman & Hall/CRC Financial Mathematics Series. CRC Press, Boca Raton (2014). With an introductory dialogue by Damiano Brigo.MATHGoogle Scholar
 Crépey, S, Grbac, Z, Nguyen, HN: A multiplecurve HJM model of interbank risk. Math. Financ. Econ. 6, 155–190 (2012).MathSciNetCrossRefMATHGoogle Scholar
 Crépey, S, Gerboud, R, Grbac, Z, Ngor, N: Counterparty risk and funding: The four wings of the TVA. Int. J. Theor. Appl. Financ. 16(1350006) (2013).Google Scholar
 Crépey, S, Grbac, Z, Ngor, N, Skovmand, D: A Lévy HJM multiplecurve model with application to CVA computation. Quant. Financ. 15, 401–419 (2015).CrossRefGoogle Scholar
 Cuchiero, C, Fontana, C, Gnoatto, A: Affine multiple yield curve models. Preprint. arXiv:1603.00527 (2016).Google Scholar
 Duffie, D, Filipović, D, Schachermayer, W: Affine processes and applications in finance. Ann. Appl. Probab. 13, 984–1053 (2003).MathSciNetCrossRefMATHGoogle Scholar
 Filipović, D: Timeinhomogeneous affine processes. Stoch. Process. Appl. 115, 639–659 (2005).MathSciNetCrossRefMATHGoogle Scholar
 Glau, K, Grbac, Z, Papapantoleon, A: A unified view of LIBOR models. In: Kallsen, J, Papapantoleon, A (eds.)Advanced Modelling in Mathematical Finance – In Honour of Ernst Eberlein, pp. 423–452. Springer, Cham (2016).Google Scholar
 Grbac, Z, Runggaldier, WJ: Interest Rate Modeling: PostCrisis Challenges and Approaches. Springer, Cham (2015).CrossRefMATHGoogle Scholar
 Grbac, Z, Papapantoleon, A, Schoenmakers, J, Skovmand, D: Affine LIBOR models with multiple curves: theory, examples and calibration. SIAM. J. Financ. Math. 6, 984–1025 (2015).MathSciNetCrossRefMATHGoogle Scholar
 Jacod, J, Shiryaev, AN: Limit Theorems for Stochastic Processes. 2nd edition. Springer, Berlin Heidelberg (2003).CrossRefMATHGoogle Scholar
 KellerRessel, M: Affine Processes: Theory and Applications to Finance. PhD thesis, TU Vienna (2008).Google Scholar
 KellerRessel, M: Affine LIBOR models with continuous tenor (2009). Unpublished manuscript.Google Scholar
 KellerRessel, M, Papapantoleon, A, Teichmann, J: The affine LIBOR models. Math. Financ. 23, 627–658 (2013).MathSciNetCrossRefMATHGoogle Scholar
 Mercurio, F: Interest rates and the credit crunch: New formulas and market models. Preprint. SSRN/1332205 (2009).Google Scholar
 Mercurio, F: A LIBOR market model with a stochastic basis. Risk.84–89 (2010).Google Scholar
 Musiela, M, Rutkowski, M: Continuoustime term structure models: forward measure approach. Financ. Stoch. 1, 261–291 (1997).CrossRefMATHGoogle Scholar
 Musiela, M, Rutkowski, M:Martingale Methods in Financial Modelling. 2nd edition. Springer, Berlin Heidelberg (2005).MATHGoogle Scholar
 Papapantoleon, A: Old and new approaches to LIBOR modeling. Stat. Neerlandica. 64, 257–275 (2010).MathSciNetCrossRefGoogle Scholar
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