Finance and Economics Discussion Series: 2013-14 Screen Reader version ^{♣}

with application to credit risk modeling*

February 26, 2013

Keywords: Time change, default intensity, credit risk, CDS options

Abstract:

We develop two novel approaches to solving for the Laplace transform of a time-changed stochastic process. We discard the standard assumption that the background process () is Lévy.
Maintaining the assumption that the business clock () and the background process are independent, we develop two different series solutions for the Laplace transform of the time-changed
process
. In fact, our methods apply not only to Laplace transforms, but more generically to expectations of smooth functions of random time. We apply the methods to introduce
stochastic time change to the standard class of default intensity models of credit risk, and show that stochastic time-change has a very large effect on the pricing of deep out-of-the-money options on credit default swaps.
*JEL Classification:* G12, G13

1 Introduction

Stochastic time-change offers a parsimonious and economically well-grounded device for introducing stochastic volatility to simpler constant volatility models. The constant volatility model is assumed to apply in a latent "business time." The speed of business time with respect to calendar time is stochastic, and reflects the varying rate of arrival of news to the markets. Most applications of stochastic time-change in the finance literature have focused on the pricing of stock options. Log stock prices are naturally modeled as Lévy processes, and it is well known that any Lévy process subordinated by a Lévy time-change is also a Lévy process. The variance gamma (Madan and Seneta, 1990; Madan et al., 1998) and normal inverse Gaussian (Barndorff-Nielsen, 1998) models are well-known early examples. To allow for volatility clustering, Carr, Geman, Madan, and Yor (2003) introduce a class of models in which the background Lévy process is subordinated by the time-integral of a mean-reverting CIR activity-rate process, and solve for the Laplace transform of the time-changed process. Carr and Wu (2004) extend this framework to accommodate dependence of a general form between the activity rate and background processes, as well as a wider class of activity rate processes.

In this paper, we generalize the basic model in complementary directions. We discard the assumption that the background process is Lévy, and assume instead that the background process () has a known Laplace transform, . Maintaining the requirement that the business clock () and the background process are independent, we develop two different series solutions for the Laplace transform of the time-changed process given by . In fact, our methods apply generically to a very wide class of smooth functions of time, and in no way require to be the Laplace transform of a stochastic process. Henceforth, for notational parsimony, we drop the auxilliary parameter from .

Our two series solution are complementary to one another in the sense that the restrictions imposed by the two methods on and on differ substantively. The first method requires that be a Lévy process, but imposes fairly mild restrictions on . The second method imposes fairly stringent restrictions on , but very weak restrictions on . In particular, the second method allows for volatility clustering through serial dependence in the activity rate. Thus, the two methods may be useful in different sorts of applications.

Our application is to modeling credit risk. Despite the extensive literature on stochastic volatility in stock returns, the theoretical and empirical literature on stochastic volatility in credit risk models is sparse. Empirical evidence of stochastic volatility in models of corporate bond and
credit default swap spreads is provided by Jacobs and Li (2008), Alexander and Kaeck (2008), Zhang et al. (2009) and Gordy and Willemann (2012). To introduce stochastic
volatility to the class of default intensity models pioneered by Jarrow and Turnbull (1995) and Duffie and Singleton (1999), Jacobs and Li (2008) replace the widely-used single-factor CIR specification
for the intensity with a two-factor specification in which a second CIR process controls the volatility of the intensity process. The model is formally equivalent to the Fong and Vasicek (1991) model of stochastic volatility in interest rates. An important
limitation of this two-factor model is that there is no region of the parameter space for which the default intensity is bounded nonnegative (unless the volatility of volatility is zero).^{4}

In this paper, we introduce stochastic volatility to the default intensity framework by time-changing the firm's default time. Let denote the calendar default time, and let be the corresponding time under the business clock. Define the background process as the time-integral (or "compensator") of the default intensity and as the business-time survival probability function . If we impose independence between and , as we do throughout this paper, then time-changing the default time is equivalent to time-changing , and the calendar-time survival probability function is

The idea of time-changing default times appears to have first been used by Joshi and Stacey (2006). Their model is intended for pricing collateralized debt obligations, so makes the simplifying assumption that firm default intensities are deterministic.^{6} Mendoza-Arriaga et al. (2010) apply time-change to a credit-equity hybrid model. If we strip out the equity component
of their model, the credit component is essentially a time-changed default intensity model. Unlike our model, however, their model does not nest the CIR specification of the default intensity, which is by far the most widely used specification in the literature and in practice. Most closely related
to our paper is the time-changed intensity model of Mendoza-Arriaga and Linetsky (2012).^{7} They obtain a spectral
decomposition of the subordinate semigroups, and from this obtain a series solution to the survival probability function. As in our paper, the primary application in their paper is to the evolution of survival probabilities in a model with a CIR intensity in business time and a tempered stable
subordinator. When that CIR process is stationary, their solution coincides with that of our second solution method. However, our method can be applied in the non-stationary case as well and generalizes easily when the CIR process is replaced by a basic affine process. Empirically, the default
intensity process is indeed non-stationary under the risk-neutral measure for the typical firm (Jacobs and Li, 2008; Duffee, 1999).

Our two expansion methods are developed for a general function and wide classes of time-change processes in Sections 2 and 3. An application to credit risk modeling is presented in Section 4. The properties of the resulting model are explored with numerical examples in Section 5. In Section 6, we show that stochastic time-change has a very large effect on the pricing of deep out-of-the-money options on credit default swaps. In Section 7, we demonstrate that our expansion methods can be extended to a much wider class of multi-factor affine jump-diffusion business time models.

2 Expansion in derivatives

The method of this section imposes weak regularity conditions on , but places somewhat strong restrictions on . Throughout this section, we assume

The cumulant of is . Carr and Wu (2004) normalize so that the business clock is an unbiased distortion of the calendar, i.e., . We assume but otherwise leave it unconstrained. The moments of can be obtained from the cumulants:

where is the incomplete Bell polynomial. For notational compactness, we may write to mean . In the analysis below, we will manipulate Bell polynomials in various ways. Unless otherwise noted, the transformations can easily be verified using the identities collected in Appendix A.

We assume that is a smooth function of time. Imposing Assumption 1, we expand as a formal series and integrate:

From equation (A.1) and the recurrence rule (A.3), it follows immediately that where denotes the falling factorial . To handle the special case of , we have for .

Defining the constants

for , we can write Observe that depends on , , and , but not on . We substitute into equation (2.3) to getObserving that we have

To obtain a generating function for the constants , we substitute for and then divide each side by .

By Assumption 1(ii), is analytic in the neighborhood of the origin, and is linear in . Therefore, is analytic in and locally analytic in . The exponential of a convergent series gives rise to a convergent series, so the series in (2.6) is convergent for any and for near zero.

To guarantee that the series expansion in equation (2.5) is convergent, we would require rather strong conditions. The function must be entire, the
coefficients
in equation (2.3) must decay faster than geometrically, and the coefficients
must vanish at a geometric rate in . In application, it may be that
none of these assumptions hold. If is analytic but non-entire, then
, so geometric behavior in the
would not be sufficient for convergence. Furthermore, we will provide a practically relevant specification below in which the
are *increasing* in for fixed . Even if the series expansion in equation (2.5) is, in general, divergent, we will see that it may nonetheless be computationally effective.

We now provide an alternative justification for equation (2.5) to clarify the convergence behavior of our expansion. We introduce a regularity condition on :

Assumption 2 implies

so Assumption 2 guarantees that this integral is convergent for all , which implies that is smooth. Thus we have for all

Let be the remainder function from the generating equation (2.6), that is,

and let be the approximation to up to term in the expansion (2.5), i.e.,

The following proposition formalizes our approximation:

where Assumption 2 guarantees the change of the order of integration and the last equality follows from the fact that is the cumulant generating function of . We obtain from (2.8), (2.9) and (2.11) that

which implies the conclusion.

Since can be arbitrarily large, Proposition 1 provides a rigorous meaning for equation (2.5). However, it does not by itself
explain why we should expect
to provide a good approximation to
. Equation (2.8) shows that the divergent sum (2.5) comes from the Laplace transform of the locally convergent sum in (2.6) (with replaced by
). It has been known for a long time that a divergent power series obtained by Laplace transforming a locally convergent sum is computationally very effective when
truncated close to the numerically least term. In recent years, this classical method of "summation to the least term" has been justified rigorously in quite some generality for various classes of problems.^{10} The analysis of Costin and Kruskal (1999) is in the setting of differential equations, but their method of proof extends to much more general problems. Although the series in our analysis is not a
usual power series, the procedure is conceptually similar and therefore expected to yield comparably good results.

For an interesting class of processes for , the sequence
takes a convenient form. Let
be the *scaling parameter* of the process, and let
be the *precision parameter*. We introduce the assumption

(2.12) |

Define a new set of constants so that Under Assumptions 1, 2 and 3, Proposition 1 holds with

(2.13) | |||

(2.14) |

This solution is especially convenient for two reasons. First, when the precision parameter is large, the expansion will yield accurate results in few terms. The variance is inversely proportional to , so converges in probability to as . This implies that for large . Since the expansion constructs as plus successive correction terms, it is well-structured for the case in which is not too volatile. The same remark applies to the more general case of Proposition 1, but the logic is more transparent when a single parameter controls the scaled higher cumulants. Second, in the special case of Assumption 3, the coefficients depend only on the chosen family of processes for and not on its parameters . In econometric applications, there can be millions of calls to the function , so the ability to pre-calculate the can result in significant efficiencies.

The three-parameter tempered stable subordinator is a flexible and widely-used family of subordinators. We can reparameterize the standard form of the Laplace exponent given by Cont and Tankov (2004, 4.2.2) in terms of our precision and scale parameters ( and ) and a stability parameter with . We obtain

It can easily be verified that

Two well-known examples of the tempered stable subordinator are the gamma subordinator ( ) and the inverse Gaussian subordinator ( ). For the gamma subordinator, the constants simplify to , so

We can calculate the efficiently via recurrence. Rzdkowski (2012) shows thatfor . The recurrence bottoms out at and for .

In the inverse Gaussian case ( ), the parameters are

so Thus, for , we have(2.17) |

where the last equality follows from identity (A.2).

3 Expansion in exponential functions

The method of this section relaxes the assumption that is a Lévy process, but is more restrictive on . In the simplest case, we require that

The convergence of implies uniform convergence for the expansion of . Note here that we are redeploying symbols , and , which were defined differently in Section 2. When this assumption is satisfied, Assumption 2 is satisfied withwhere is the point measure of mass one at . Since is convergent, is a finite measure.

Let denote the moment generating function for . We assume

Assumption 5 can accommodate non-Lévy specifications as well. Since volatility spikes are often clustered in time, it may be desirable to allow for serial dependence in the rate of time change. Following Carr and Wu (2004) and Mendoza-Arriaga et al. (2010), we let a positive process be the instantaneous activity rate of business time, so that

If we specify the activity rate as an affine process, the moment generating function for will have the tractable form for known functions and . A widely-used special case is thewhere is a compound Poisson process, independent of the diffusion . The arrival intensity of jumps is and jump sizes are exponential with mean . In Appendix B, we review the solution of functions and under this specification. Carr and Wu (2004, Table 2) list alternative specifications of the activity rate with known .

Under Assumptions 4 and 5, we have

When is a Laplace transform of the time-integral of a nonnegative diffusion and when is a Lévy subordinator, Proposition 3 is equivalent to the eigenfunction expansion of Mendoza-Arriaga and Linetsky (2012).^{11} However, because our approach is agnostic with respect to the interpretation of , it can be applied in situations
when spectral decomposition is unavailable, e.g., when the background process is a time-integral of a process containing jumps. All that is needed is that has a convergent Taylor series
expansion as specified in Assumption 4. Moreover, our approach makes clear that need not be a Lévy subordinator.

As we will see in the next section, there are situations in which Assumption 4 does not hold, so neither Proposition 3 nor the corresponding eigenfunction expansion of Mendoza-Arriaga and Linetsky (2012) pertains. However, our method can be adapted so long as has a suitable expansion in powers of an affine function of . We will make use of this alternative in particular:

Under Assumptions 4' and 5, we haveThis leads to

Although Assumption 4' is not a sufficient condition for Assumption 2, it is sufficient for purposes of approximating by the expansion in derivatives of Section 2. Let denote the approximation to given by the finite expansion

for . This function by construction satisfies Assumption 4', and furthermore satisfies Assumption 2 with where is the point measure of mass one at . Therefore, expansion in derivatives can be applied to . Let be the approximation to up to term in the expansion in derivatives, and let be the corresponding remainder term in Proposition 1. By Proposition 3', for any there exists such that for all , . Thus, we can bound the residual in expansion in derivatives by for .4 Application to credit risk modeling

We now apply the two expansion methods to the widely-used default intensity class of models for pricing credit-sensitive corporate bonds and credit default swaps. In these models, a firm's default occurs at the first event of a non-explosive counting process. Under the business-time clock, the intensity of the counting process is . The intuition driving the model is that is the probability of default before business time , conditional on survival to business time . We define as the time-integral of , which is also known as the compensator.

Let denote the calendar default time, and let be the corresponding time under the business clock. Current time is normalized to zero under both clocks. The probability of survival to future business time is

It is easily seen that time-changing the default time is equivalent to time-changing the compensator, i.e., that the survival probability in calendar time is equal to , where . In application, we are often interested in the calendar time default intensity. When is the time-integral of an absolutely continuous activity rate process , we can apply a change of variable as in Mendoza-Arriaga et al. (2010, 4.2) from which it is clear that the default intensity in calendar time is simply . Observe that if and are both bounded nonnegative, then is bounded nonnegative as well.

When is a Lévy subordinator, the process is not differentiable, so the change of variable cannot be applied. We have so far fixed the current time to zero to minimize notation. To accommodate analysis of time-series behavior, let us define

The default intensity under calendar time can then be obtained as .Since this holds for any nonnegative , we must have which implies . Thus, the bound on is preserved under time-change.

We acknowledge that the assumption of independence between and may be strong. In the empirical literature on stochastic volatility in stock returns, there is strong evidence for dependence between the volatility factor and stock returns (e.g., Jones, 2003; Andersen et al., 2002; Jacquier et al., 2004). In the credit risk literature, however, the evidence is less compelling. Across the firms in their sample, Jacobs and Li (2008) find a median correlation of around 1% between the default intensity diffusion and the volatility factor. Nonetheless, for a significant share of the firms, the correlation appears to be material. We hope to relax the independence assumption in future work.

We re-introduce the *basic affine process*, which we earlier defined in Section 3. Under the business clock,
follows the stochastic differential equation

where is a compound Poisson process, independent of the diffusion . The arrival intensity of jumps is and jump sizes are exponential with mean . We assume to ensure that the default intensity is nonnegative. The generalized Laplace transform for the basic affine process is

for functions with explicit solution given in Appendix B. Defining the functions and , we arrive at the survival probability function

We digress briefly to consider whether the method of Carr and Wu (2004) can be applied in this setting. The compensator is not Lévy, but can be
expressed as a time-changed time-integral of a constant intensity, where the time change in this case is the time-integral of the basic affine process in (4.2). Thus, we can write
as
where is trivially a Lévy process and is a *compound* time change. However, this approach leads nowhere, because
is equivalent to
. Put another way, we are still left with the problem of solving the Laplace transform for
.

To apply our expansion in exponential functions, we show that Assumption 4 is satisfied when and Assumption 4' is always satisfied. In Appendix C, we prove

When
and in the absence of jumps (
), the expansion in (ii) is equivalent to the eigenfunction expansion in Davydov and Linetsky (2003, 4.3).^{13}Therefore, the associated solution to
under a Lévy subordinator is identical to the solution in Mendoza-Arriaga and Linetsky (2012). Our result is more general in
that it permits non-stationarity (i.e.,
) in expansion (i) and accommodates the presence of jumps in the intensity process in expansions (i) and
(ii). Furthermore, it is clear in our analysis that our expansions can be applied to non-Lévy specifications of time-change as well, such as the mean-reverting activity rate model in (3.2).

Subject to the technical caveat at the end of Section 3, Assumptions 1 and 4' are together sufficient for application of expansion in derivatives without restrictions on . To implement, we need an efficient algorithm to obtain derivatives of . Let be the family of functions defined by

These functions have closed-form expressions, which we provide in Appendix D. Using Itô's Lemma, we prove in Appendix E: Proposition 6 points to a general strategy for iterative computation of the derivatives of . We began with . We then apply Proposition 6 to obtain We differentiate again to get and so on. In general, can be expressed as a weighted sum of . While the higher derivatives of would be tedious to write out, the recurrence algorithm is easily implemented. The incremental cost of computing is dominated by the cost of computing , assuming that the lower order have been retained from computation of lower order derivatives of .5 Numerical examples

We explore the effect of time-change on the behavior of the model, as well as the efficacy of our two series solutions. To fix a benchmark model, we assume that follows a CIR process in business time with parameters , and . This calibration is consistent in a stylized fashion with median parameter values under the physical measure as reported by Duffee (1999). Our benchmark specification adopts inverse Gaussian time change. In all the examples discussed below, the behavior under gamma time-change is quite similar.

The survival probability function is falling monotonically and almost linearly, so is not scaled well for our exercises. Instead, following the presentation in Duffie and Singleton (2003, 3), we work with the forward default rate,
.^{14} In our benchmark calibration, we set starting condition
well below its long-run mean in order to
give reasonable variation across the term structure in the forward default rate. Both and are scale-invariant processes, so we fix the scale parameter
with no loss of generality.

Figure 1 shows how the term structure of the forward default rate changes with the precision parameter . We see that lower values of flatten the term-structure, which accords with the intuition that the time-changed term-structure is a mixture across time of the business-time term-structure. Above , it becomes difficult to distinguish from the term structure for the CIR model without time-change.

CIR model under business time with parameters , starting condition , and inverse Gaussian time-change with . When , the model is equivalent to the CIR model without time-change. Term-structures calculated with the series expansion in exponential functions of Proposition 3 with 12 terms.Finding that time-change has negligible effect on the term structure
for moderate values of
does *not* imply that time-change has a small effect on the time-series behavior of the default intensity. For a given time-increment , we obtain by simulation the kurtosis of calendar time increments of the default intensity (that is,
) under the stationary law. For the limiting CIR model without time-change, moments for the increments
have simple closed-form solutions provided by Gordy (2012). The kurtosis is equal to
, which is invariant to the time-increment .

In Figure 2, we plot kurtosis as a function of on a log-log scale. Using the same baseline model specification as before, we plot separate curves for a one day horizon ( , assuming 250 trading days per year), a one month horizon ( ), and an annual horizon (). As we expect, kurtosis at all horizons tends to its asymptotic CIR limit (dotted line) as . For fixed , kurtosis also tends to its CIR limit as . This is because an unbiased trend stationary time-change has no effect on the distribution of a stationary process far into the future. For intermediate values of (say, between 1 and 10), we see that time-change has a modest impact on kurtosis beyond one year, but a material impact at a one month horizon, and a very large impact at a daily horizon.

Stationary CIR model under business time with parameters , and inverse Gaussian time-change with . Dotted line plots the limiting CIR kurtosis. Both axes on log-scale. Moments of the calendar time increments are obtained by simulation with 5 million trials.Next, we explore the convergence of the series expansion in exponentials. Let denote the estimated forward default rate using the first terms of the series for and the corresponding expansion for . Figure 3 shows that the convergence of to is quite rapid. We proxy the series solution with terms as the true forward default rate, and plot the error in basis points (bp). The error is decreasing in , as the series in Proposition 5 is an asymptotic expansion. With only terms, the error is 0.25bp at , which corresponds to a relative error under 0.25%. With terms, relative error is negligible (under 0.0005%) at .

CIR model under business time with parameters , starting condition , and inverse Gaussian time-change with and .We turn now to the convergence of the expansion in derivatives. In Figure 4, we plot the error against the benchmark for terms in expansion (2.10). The benchmark curve is calculated, as before, using the series expansion in exponential functions with 12 terms. The magnitude of the relative error is generally largest at small values of . For , the forward default rate is off by nearly 0.5bp at . Observed bid-ask spreads in the credit default swap market are an order of magnitude larger, so this degree of accuracy is already likely to be sufficient for empirical application. For , the gap is never over 0.025bp at any .

CIR model under business time with parameters , starting condition , and inverse Gaussian time-change with .In Figure 5, we hold fixed and explore how error varies with . As the expansion is in powers of , it is not surprising that error vanishes as grows, and is negligible (under 0.005bp in absolute magnitude) at . Experiments with other model parameters suggest that absolute relative error increases with and and decreases with .

CIR model under business time with parameters , starting condition , and inverse Gaussian time-change with . Number of terms in expansion is fixed to .6 Options on credit default swaps

In the previous section, we observe that stochastic time-change has a negligible effect on the term structure of default probability for moderate values of
, which implies that introducing time-change should have little impact on the term-structure of credit spreads on corporate bonds and credit default swaps (CDS).
Nonetheless, time-change has a large effect on the forecast density of the default intensity at short horizon. Consequently, introducing time-change should have material impact on the pricing of short-dated options on credit instruments.^{15} In this section, we develop a pricing methodology for European options on single-name CDS in the time-changed CIR model.

At present, the CDS option market is dominated by index options. The market for single-name CDS options is less liquid, but trades do occur. A *payer option* gives the right to buy protection of maturity
at a fixed spread (the "strike" or "pay premium") at a fixed
expiry date
. A payer option is in-the-money if the par CDS spread at date
is greater than . A *receiver option* gives the right
to sell protection. We focus here on the pricing of payer options, but all results extend in an obvious fashion to the pricing of receiver options. An important difference between the index and single-name option markets is that single-name options are sold with knock-out, i.e., the option expires
worthless if the reference entity defaults before
. As we will see, this complicates the analysis. Willemann and Bicer (2010) provide an overview of CDS option trading and its
conventions.

To simplify the analysis and to keep the focus on default risk, we assume a constant risk free interest rate and a constant recovery rate . In the next section, we generalize our methods to accommodate a multi-factor model governing both the short rate and default intensity. The assumption of constant recovery can be relaxed by adopting the stochastic recovery model of Chen and Joslin (2012) in business time. We assume that follows a mean-reverting ( ) CIR process in business time, and that the clock is a Lévy process satisfying Assumption 1 with Laplace exponent . All probabilities and expectations are under the risk-neutral measure.

In the event of default at date , the receiver of CDS protection receives a single payment of at . Therefore, the value at date of the protection leg of a CDS of maturity is

where is the density of the remaining time to default (relative to date ) conditional on . From Proposition 3, we have for and . We differentiate, insert into the expression for the protection leg, apply Fubini's theorem, and integrate term-by-term to getTo price the premium leg, we make the simplifying assumption that the spread is paid continuously until default or maturity. The value at date of the premium leg of a CDS of maturity is then

We again substitute the expansion for and integrate to getThe par spread equates the protection leg value (6.1) to the premium leg value (6.2).

To simplify exposition, we assume that CDS are traded on a running spread basis.^{16} Let
be the net value of the CDS for the buyer of protection at time given
and the spread
, i.e., is the difference in value between the protection
leg and the premium leg. Note that does not depend directly on time . This
simplifies to

The value at time 0 of the payer option is the expectation of expression (6.3) over the joint distribution of
under the risk-neutral measure:

Let be the Laplace transform of conditional on . This is given by Broadie and Kaya (2006, Eq. 40) for the CIR process in business time. Since we have

(6.4) |

This expectation is most easily obtained by Monte Carlo simulation. In each trial , we draw a single value of the business clock expiry date from the distribution of . Next, we draw from the noncentral chi-squared transition distribution for given and . The transition law for the CIR process is given by Broadie and Kaya (2006, Eq. 8). The option value is estimated by

Observe that we can efficiently calculate option values across a range of strike spreads with the same sample of .

Figure 6 depicts the effect of on the value of a one month payer option on a five year CDS. Model parameters are taken from the baseline values of Section 5. The riskfree rate is fixed at 3% and the recovery rate at 40%. Depending on the choice of , the par spread is in the range of 115-120bp (marked with circles). For deep out-of-the-money options, i.e., for , we see that option value is decreasing in . Stochastic time-change opens the possibility that the short horizon to option expiry will be greatly expanded in business time, and so increases the likelihood of extreme changes in the intensity. The effect is important even at large values of for which the term-structure of forward default rate would be visually indistinguishable from the CIR case in Figure 1. For example, at a strike spread of 200bp, the value of the option is nearly 700 times greater for the time-changed model with than for the CIR model without time-change.

Perhaps counterintuitively, the value of the option is increasing in for near-the-money options. Because the transition variance of is concave in , introducing stochastic time-change actually reduces the variance of the default intensity at option expiry even as it increases the higher moments. Relative to out-of-the-money options, near-the-money options are more sensitive to the variance and less sensitive to higher moments.

Value of one-month ( ) payer option on a 5 year CDS as function of strike spread. CIR model under business time with parameters , starting condition , and inverse Gaussian time-change with . Riskfree rate . Recovery . Circles mark par spread at date 0. When , the model is equivalent to the CIR model without time-change.The effect of time to expiry on option value is depicted in Figure 7. The solid lines are for the model with stochastic time-change (), and the dashed lines are for the CIR model without time-change. Relative to the case of the short-dated (one month) option, stochastic time-change has a small effect on the value of the long-dated (one year) option. This is consistent with our observation in Figure 2 that the kurtosis of converges to that of as grows large. Because the Lévy subordinator lacks persistence, stochastic time-change simply washes out at long horizon.

Value of CDS payer option. CIR model under business time with parameters , starting condition . Solid lines for model with inverse Gaussian time-change with and , and dashed line for the CIR model without time-change. Riskfree rate . Recovery . Circles mark par spread at date 0.7 Multi-factor affine models

We have so far taken the business-time default intensity to be a single-factor basic affine process. In this section, we show that our methods of Sections 2 and 3 can be applied to a much wider class of multi-factor affine jump-diffusion models. For the sake of brevity, we limit our analysis here to stationary models.

Let be a -dimensional affine jump-diffusion, and let the default intensity at business time be given by an affine function . We now obtain a convergent series expansion of

As in Duffie et al. (2000, 2), we assume that the jump component of is a Poisson process with time-varying intensity that is affine in , and that jump sizes are independent of .By Proposition 1 of Duffie et al. (2000, 2), has exponential-affine solution , where denotes the inner product. Functions and satisfy complex-valued ODEs which we represent simply as

where . In typical application, tends to zero as , which indicates the existence of an attracting critical point. Less restrictively, we assume there is an attracting critical point such that , and analyze the system in a neighborhood of such a point. The functions and are assumed to be analytic in a neighborhood of , which is a mild restriction of the setting in Duffie et al. (2000).

which we study under assumptions guaranteeing stability of the equilibrium.

- (i)
- is analytic in a polydisk centered at zero.
- (ii)
- is a diagonalizable matrix of constants. Its eigenvalues are nonresonant, i.e., for nonnegative integers with , we have for .
- (iii)
- are in the left half plane, .

The following is a classical theorem due to Poincaré (see e.g., Ilyashenko and Yakovenko, 2008).

satisfies (7.3). Tangent to the identity simply means the fact that the linear part of the conjugation map is the identity, as seen in (7.4).

Let denote the common polydisk of analyticity of and .

where are arbitrary. The rest follows from Theorem 1, (7.4), the fact that , and the analyticity of which implies that its Taylor series at zero converges.

Let be a ball in which is analytic and

The existence of is guaranteed by Assumption 7(i) and by the property as in (7.2). Condition (7.8) implies that is an invariant domain under the flow. This is an immediate consequence of the much stronger Proposition 8 below, but it has an elementary proof: By Cauchy-Schwartz and the assumption on , Since there exists some for which We can also write the inner product as which implies . Thus, if the initial condition is in , then the solution remains in for all .

We turn now to the second equation in the ODE system (7.1). Assume, without loss of generality, that is analytic in the same polydisk as , i.e., in , which implies that the expansion converges uniformly and absolutely inside . Imposing Assumption 7, we substitute (7.6) to obtain a uniformly and absolutely convergent expansion in , and integrate term-by-term to get

where are the Taylor coefficients of .

The absolute and uniform convergence of the expansions of and extends to the expansion of , which is the multi-factor extension of Proposition 5(ii). Thus, subject to Assumption 7 and , expansion in exponentials can be applied to the multi-factor model. Furthermore, the construction in (3.1) of a finite signed measure satisfying Assumption 2 extends naturally, so expansion in derivatives also applies.

The multi-factor extension can be applied to a joint affine model of the riskfree rate and default intensity. Let be the short rate in business time and let be the recovery rate as a fraction of market value at . We assume that is an affine function of the affine jump-diffusion , and solve for the business-time default-adjusted discount function . Subject to the regularity conditions in Assumption 7, we can thereby introduce stochastic time-change to the class of models studied by Duffie and Singleton (1999) and estimated by Duffee (1999). The possibility of handling stochastic interest rates in our framework is also recognized by Mendoza-Arriaga and Linetsky (2012, Remark 4.1).

We have derived and demonstrated two new methods for obtaining the Laplace transform of a stochastic process subjected to a stochastic time change. Each method provides a simple way to extend a wide variety of constant volatility models to allow for stochastic volatility. More generally, we can abstract from the background process, and view our methods simply as ways to calculate the expectation of a function of stochastic time. The two methods are complements in their domains of application. Expansion in derivatives imposes strictly weaker conditions on the function, whereas expansion in exponentials imposes strictly weaker conditions on the stochastic clock. We have found both methods to be straightforward to implement and computationally efficient.

Relative to the earlier literature, the primary advantage of our approach is that the background process need not be Lévy or even Markov. Thus, our methods are especially well-suited to application to default intensity models of credit risk. Both of our methods apply to the survival probability function under the ubiquitous basic affine specification of the default intensity. The forward default rate is easily calculated as well. Therefore, we can easily price both corporate bonds and credit default swaps in the time-changed model. In a separate paper, a time-changed default intensity model is estimated on panels of CDS spreads (across maturity and observation time) using Bayesian MCMC methods.

In contrast to the direct approach of modeling time-varying volatility as a second factor, stochastic time-change naturally preserves important properties of the background model. In particular, so long as the default intensity is bounded nonnegative in the background model, it will be bounded nonnegative in the time-changed model. In numerical examples in which the business-time default intensity is a CIR process, we find that introducing a moderate volatility in the stochastic clock has hardly any impact on the term-structure of credit spreads, yet a very large impact on the intertemporal variation of spreads. Consequently, the model preserves the cross-sectional behavior of the standard CIR model in pricing bonds and CDS at a fixed point in time, but allows for much greater flexibility in capturing kurtosis in the distribution of changes in spreads across time. The model also has a first-order effect on the pricing of deep out-of-the-money CDS options.

A. Identities for the Bell polynomials

Bell polynomial identities arise frequently in our analysis, so we gather the important results together here for reference. In this appendix, and are scalar constants, and and are infinite sequences and . Unless otherwise noted, results are drawn from Comtet (1974, 3.3), in some cases with slight rearrangement.

We begin with the incomplete Bell polynomials, . The homogeneity rule is

From Mihoubi (2008, Example 2), we can obtain the identity

for any . Recall here that denotes the falling factorial . We also make use of the recurrence rules

For , the complete Bell polynomials, , are obtained from the incomplete Bell polynomials as

Note that and that for . Riordan (1968, 5.2) provides the recurrence rule

B. Generalized transform for the basic affine process

In this appendix, we set forth the closed-form solution to functions in the generalized transform

The process is assumed to follow a basic affine process as in equation (4.2). If we replace by , then the results here apply to equation (3.2) as well with and . In this appendix we drop the subscripts in the BAP parameters .

We follow the presentation in Duffie (2005, Appendix D.4), but with slightly modified notation. All functions and parameters associated with the generalized transform are written in Fraktur script. Let

We next define for

(B.2) |

and the functions

(B.3) |

Then the functions and are

(B.4a) | |||

(B.4b) |

The discount function is
where
and
. To obtain these, let
be the value of
when and for , and similarly define
,
, etc. These simplify to

For the special case of and , the simplify to

(B.5a) | |||

(B.5b) |

The do not simplify dramatically. We obtain

C. Expansion of the BAP survival probability function

We draw on the notation and results of Appendix B, and begin with the expansion in (i) of Proposition 5. Let , and introduce the change of variable . Then for , we can expand

where For , we find . Since , we have , which implies . For , we find Since , we have . Since , and since is analytic for , the expansion in (C.1) is absolutely convergent. Finally, since and for all and , we have

Using the same change of variable, the function has expansion

Again, since is analytic for , this expansion is absolutely convergent.

We combine these results to obtain

where and

Since the composition of two analytic functions is analytic, a series expansion of in powers of is absolutely convergent for (equivalently, ). Thus, Proposition 5 holds with

(C.4) |

These coefficients are most conveniently calculated via a recurrence rule easily derived from (A.6):

We now assume and derive the expansion in (ii) of Proposition 5. Here we introduce the change of variable . Following the same steps as above, we find that for can be expanded as

Since , we see that . Since we have . Thus, for . Since as well, the expansion in (C.6) is absolutely convergent.

Using the same change of variable, the function has expansion

Again, since is analytic for , this expansion is absolutely convergent.

We combine these results to obtain

where is defined as before and where

(C.9) |

Recurrence rule (C.5) applies in this case as well.

D. Derivatives of the generalized transform

Here we provide analytical expressions for . As in the previous appendix, the process is assumed to follow a basic affine process with parameters . Recall that

Let and denote the functionsThen by Faà di Bruno's formula,

where denotes the complete Bell polynomial. Given solutions to the functions , it is straightforward and efficient to calculate the sequentially via recurrence rule (A.6).

The functions and appear to be quite tedious (and the higher order and presumably even more so), as they depend on partial derivatives of , , and so on. Fortunately, these derivatives simplify dramatically when evaluated at . Define

and similarly define , , etc. We findAn especially useful result is Last, we can show

We arrive at

Perhaps surprisingly, there are no further complications for and for . Proceeding along the same lines, we find

These expressions imply that the cost of computing does not vary with .

E. Differentiation of the functions

As in the previous appendix, the process is assumed to follow a basic affine process with parameters . Let us define

so that . The extended Itô's Lemma (Protter, 1992, Theorem II.32) impliesTaking expectations,

where we define Note that the term vanishes because is independent of .

We interpret as the expected jump in conditional on a jump in at time . Let be the jump at time . Noting that is distributed exponential with parameter , we have

For , we have . Assuming , conditioning on and expanding , the integral is

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* We have benefitted from discussion with Peter Carr, Darrell Duffie, Kay Giesecke, Canlin Li and Richard Sowers, and from the excellent research assistance of Jim Marrone, Danny Marts and Bobak Moallemi. The opinions expressed here are our own, and do not
reflect the views of the Board of Governors or its staff. Email: [email protected]. Return to Text

1. Ohio State University, Columbus, OH. Return to Text

2. Federal Reserve Board, Washington, DC. Return to Text

3. University of Chicago, Chicago, IL Return to Text

4. The structural models of Merton (1974) and Black and Cox (1976) have also been extended to allow for stochastic volatility. See Fouque et al. (2006), Hurd (2009), and Gouriéroux and Sufana (2010). Return to Text

5. As we will discuss in Section 4, the compensator can be expressed as a time-changed Lévy process, but not in a way
that allows the Laplace transform of
to be obtained as in Carr and Wu (2004). Return to Text

6. Ding et al. (2009) solve a more sophisticated model in which the default intensity is self-exciting, but is constant in between default arrival times. Return to Text

7. We became aware of this paper only upon release of the working paper on SSRN in September 2012. Our research was conducted independently and contemporaneously. Return to Text

8. By Hartog's theorem, a function which is analytic in a number of variables separately, for each of them in some disk, is jointly analytic in the product of the disks (Narasimhan, 1971). Return to Text

9. Our analysis indicates that Assumption 2 could be replaced altogether with the much weaker condition that is
analyzable, i.e., that the function admits a Borel summable transseries at infinity (see Écalle, 1993). Return to Text

10. The earliest use of optimal truncation of divergent series was a proof by Cauchy (1843) that the least term truncation of the Gamma function series is optimal, giving rise to errors of the same order of magnitude as
the least term. Stokes (1864) took the method further, less rigorously, but applied it to many problems and used it to discover what we now call the Stokes phenomenon in asymptotics. Return to Text

11. The tractability of time-changing an expansion in exponential functions of time was earlier exploited by Mendoza-Arriaga et al. (2010, Theorem 8.3) for the special case of the JDCEV model. Return to Text

12. The negative derivative
is the density of the distribution of business-clock default time at business time given information at , so
is the instantaneous hazard rate in business time, i.e.,
. By the same logic, the instantaneous hazard rate at calendar time is
. Return to Text

13. Specifically, our
is equal to
in the notation of Davydov and Linetsky (2003, Proposition 9). Return to
Text

14. In a deterministic intensity model, the forward default rate would equal the intensity. Return to Text

15. We abstract here from the distinction between the risk-neutral measure that governs pricing and the physical measure that governs the empirical time-series of returns. Our statement continues to hold if one adopts the change of measure for the CIR
process that is most commonly seen in the literature (called "drift change in the intensity" by Jarrow et al., 2005). Return to Text

16. That is, the quoted spread specifies the coupon paid by buyer of protection to seller. Since the so-called Big Bang of April 2009, CDS have been traded at standard coupons of 100 and 500 basis points with compensating upfront payments between buyer and
seller. See Leeming et al. (2010) on the recent evolution of the CDS market. Return to Text

17. Comparing our system to the corresponding ODE system (2.5) and (2.6) in Duffie et al. (2000), our restriction merely imposes that the "jump transform"
is analytic in a neighborhood of
. Note here that we have reversed the direction of time. Duffie et al. (2000) fix the horizon and vary current time , whereas we fix current time at 0 and vary the horizon . Return to Text