Conditional Law of the Hitting Time for a Lévy Process in Incomplete Observation ()
Received 8 October 2015; accepted 27 November 2015; published 30 November 2015
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1. Introduction
Here we consider a jump-diffusion process X which models the value of a firm. This is a Lévy process. Details on this class of processes can be found in [1] and [2] . Their use in financial modeling is well developed in [3] . We study the first passage time of process X at level
modeling the default time. We investigate the behavior of the default time under incomplete observation of assets. In the literature, there exists some papers in relation to this topic. Duffie and Lando [4] suppose that bond investors cannot observe the issuer’s assets directly; instead, they only receive periodic and imperfect reports. For a setting in which the assets of the firm are geometric Brownian motion until informed equity holders optimally liquidate, they derive the conditional distribution of the assets, and give the available information. In a similar model, but with complete information, Kou and Wang [5] study the first passage time of a jump-diffusion process whose jump sizes follow a double exponential distribution. They obtain explicit solutions of the Laplace transform of the distribution of the first passage time. Laplace transform of the joint distribution of jump-diffusion and its running maximum,
, is too obtained. To finish, they give numerical examples. Bernyk et al. [6] , for their part, consider stable Lévy process X of index
with non negative jumps and its running maximum. They characterize the density function of
as the unique solution of a weakly singular Volterra integral equation of the first kind. This leads to an explicit representation of the density of the first passage time. To unify the noisy information in Duffie and Lando [4] , X. Guo, R. A. Jarrow and Y. Zang [7] define a filtration which models incomplete information. By simple examples, they give the importance of this notion. Similarly to Kou and Wang, without specifying the jumps size law, Dorobantu [8] provides the intensity function of the default time. That is very important for investors, but the information brought by this intensity is low. Furthermore, Roynette et al. [9] prove that the Laplace transform of the random triplet (first passage time, overshoot, undershoot) satisfies an integral equation. After normalization of the first passage time, they show under some convenient assumptions that the random triplet converges in distribution as level x goes to
. Gapeev and Jeanblanc [10] study a model of a financial market in which the dividend rates of two risky asset’s initial values change when certain unobservable external events occur. The asset price dynamics are described by a geometric Brownian motion, with random drift rates switching at independent exponential random times. These random times are independent of the constantly correlated driving Brownian motion. They obtain closed expressions for rational values of European contingent claims given the available information. Moreover, estimates of the switching times and their conditional probability density are provided. Coutin and Dorobantu [11] prove that the default time law has a density (defective when
) with respect to the Lebesgue measure in case of a stationary independent increment process built on a pair (compound Poisson process, Brownian motion).
We extend this approach studying the conditional law of the first passage time of Lévy process at level x given a partial information. We solve this problem using filtering theory inspired by Zakai [12] , Pardoux [13] , Coutin [14] , Bain and Crisan [15] , based on the so called “reference probability measure” method. The paper is organized as follows: Section 2 sets the model; Section 3 gives the results on the existence of the conditional density given the observed filtration and on the integro-differential equation satisfied by this conditional density; Section 4 gives the proofs of the results. To finish, we conclude and give some auxiliary results in Appendix.
2. Model and Motivations
This section defines the basic space in which we work and announces what we will do. Subsection 2.1 gives the model of the firm value and defines the default time. Subsection 2.2 recalls some important results in the complete information case. Subsection 2.3 defines the signal and observation process and the model for available information. Basically, it introduces the notion of filtering theory. Subsection 2.4 gives our motivation.
2.1. Construction of the Model
Let
be a filtered probability space satisfying the usual conditions on which we define a
standard Brownian motion W, a sequence of independent and identically distributed random variables ![](//html.scirp.org/file/6-1490372x13.png)
with distribution function
, a Poisson process N with intensity
and a stochastic process Q. We assume that all these elements are independent,
is a Brownian motion and
is a compound
Poisson process with intensity ν under
defined for any Borel set A by
. On this
probability space, we define a process X as follows:
(1)
X models a firm value and the default is modeled by the first passage time of X at a level
. Hence the default time is defined as
. (2)
We suppose that X is not perfectly observable and that observation is modeled by process Q.
2.2. Some Results When X Is Perfectly Observed
Let
be a Brownian motion with drift mÎR (
). For
, we let ![]()
By (5.12) page 197 of [16] ,
has the following law on
:
(3)
where
![]()
The function
is
on
, and all its derivatives admit 0 as right limit at 0 and therefore belongs
to
. For
, Roynette et al. [9] consider as a firm value the process
and
as a default time the random variable
They let
namely overshoot and
namely undershoot. They prove that the Laplace transform of
satisfies an integral equation. After a suitable renormalization of
that we can note here
, they show that
converges in distribution as x goes to
. Overall they have obtained an asymptotic behavior of the defaut time, the overshoot and the undershoot.
For a general Lévy process, Doney and Kiprianou [17] give the law of the quintuplet
where
and
.
Coutin and Dorobantu [11] consider (1) and (2) and show that
admits a density with respect to the Lebesgue measure. They give the following closed expression of this density
(4)
where
is the sequence of the jump times of the process N.
2.3. The Incomplete Information
Our work is inspired and is in the same spirit as D. Dorobantu [8] . In her thesis, Dorobantu assumes that investors wishing to detain a part of the firm do not have complete information. They don’t observe perfectly the process value X of the firm but a noisy value. She defined a process Q independent of
and satisfying the following evolution equation
![]()
with h a Borel and bounded function and B a standard Brownian motion.
Definition 1. The process X is called the signal. The process Q is called the observation and is perfectly observed by investors.
This leads us to a filtering model and we introduce the filtering framework inspired of Zakai [12] , Coutin [14] or Pardoux [13] .
Since the function h is bounded, the Novikov condition,
is satisfied and we
define the following exponential martingale for the filtration
by
![]()
For a fixed maturity
, the process
is a uniformly integrable
-martingale.
Definition 2. For fixed
, let us define a probability measure
on
by
![]()
We also note that the law of X, so the one of
, under
is the same as under
. Note that investors have additional information on the firm which is modeled at time t by
![]()
Then all the available information is represented by the filtration
![]()
where the s-algebra
is generated by the observation of the process Q up to time t.
2.4. Motivations
D. Dorobantu [8] obtains the
-intensity of the default, namely the
-predictable process
, such that
![]()
is a
-martingale. With this result, using their available information, the investors can predict the default time. More precisely, given that default did not occur at time t, the probability that it occurs at time
is
approximated by
. But the information brought by the knowledge of
is low. This motivates us to
show that the conditional law of default time
given
admits a density with respect to Lebesgue measure and to give its dynamic evolution.
This section presents our basic model of a firm with incomplete information about its assets. More generally, we treat a continuous time setting, staying with the work of D. Dorobantu [8] in her thesis second part. Next section gives our main results.
3. The Results
3.1. Existence of the Conditional Density
We recall that
is the default time of a firm and
is the available information of investors at time t. In this subsection, we prove that conditionally on the s-algebra
,
admits a density with respect to the Lebesgue measure.
Proposition 1. For all
, on the set
, the
conditional law of
has the following form
(5)
where
![]()
And
![]()
Remark 1 Referring to [9] , for all
, the passage time
is finite almost surely if and only if
.
3.2. Mixed Filtering-Integro-Differential Equation for Conditional Density
In this subsection, we give our main results. Indeed, we first show that the conditional law of the hitting time ![]()
given the filtration
satisfies a stochastic integro-differential equation. Afterwards, we give a uniqueness
result. This type of equation is the same as the one studied in [18] with the only difference that here, we have more general Voltera random coefficients.
Theorem 1. Let
be a real number. For any
, on the set
, the conditional density of
given
satisfies the stochastic integro-differential equation:
(6)
where
![]()
![]()
and G is defined in Proposition 1.
Proposition 2. If Equation (6) admits a solution, this one is unique.
3.3. Some Technical Results
Here, we give some technical and auxiliary results which are useful to prove Theorem 1 and Proposition 2.
Proposition 3. For any bounded function
such that
is
-measurable, ![]()
(7)
By this proposition, we establish two corollaries which give a representation more accessible of the processes
and
: we apply Proposition 3 respectively to the functions
and
the second expressions being consequence of the fact that on the event
τx = u +
(q is the shift operator) and ![]()
Corollary 1. For all
we have ![]()
1)
(8)
and equivalently
2)
(9)
Corollary 2. For ![]()
1)
(10)
and equivalently
2)
(11)
Proposition 4. For any
we have on the set ![]()
(12)
Remark 2. Equation (12) of Proposition 4 can be rewriten as:
![]()
Where
![]()
![]()
![]()
This equation is similar to the non normalized conditional distribution Equation (3.43) in A. Bain and D. Crisan [15] , called Zakai equation.
In the same way, Equation (6) which is derived from (12) is similar to the normalized conditional distribution Equation (3.57) in A. Bain and D. Crisan [15] , called Kushner-Stratonovich equation.
3.4. Numerical Examples
We simulate the density of the first passage time respectively in complete information and in incomplete information. We suppose that the jump size follows a double exponential distribution, i.e, the common density of Y
is given by
where
are constants,
and
.
Here,
and
. The difference between the figures is on one hand due to the
information and on another hand to the values taken by the parameters m and
.
These four first figures (Figue 1 and Figure 2) represent the densities of the first passage time for a jump
diffusion process (case of complete information). The variable
and Monte Carlo results are based on 5000 simulation runs.
Figure 3, Figure 4 and Figure 6 are those of the conditional density
(case of incomplete information), for fixed
and the variable r is such that
. Part II of A. Bain and D. Crisan [15] , namely Numerical Algorithms, where the authors give some tools to solve the filtering problem is really useful. The class of the numerical method used is the particle method for continuous time framework.Here, the Monte Carlo results are based on 120 simulation runs.
We observe that the maximum reached is greater if the drift m is positive, meaning the positive level x is more probably reached in a shorter time.
In incomplete information, the distance between the curve and axis is greater than in complete information case, this would mean that in case of incomplete information, the level x is more difficult to be reached in a short time.
The choice of the small value of
serves to compare the results with the limiting Brownian motion case (
). In complete information case, the formulae for the first passage times of Brownian motion can be found in [16] .
A large value of
implies a lot of jumps, a large computing time and less regular curve.
In these last four figures (Figure 5 and Figure 6), the maximum reached is greater if the drift m is negative, meaning the positive level x is more probably reached in a shorter time. This is due to the very small value of
.
4. Proofs
Proposition 1
Proof. First note that, since X is a
-Markov process and
, we have
![]()
The fact that
is a
-stopping time justifies the last equality.
Secondly, for any
the
Markov property of the process X and the fact that on the set
,
ensure
![]()
The
-conditional law of
has the density (possibly defective)
, thus
![]()
By hypothesis, we have
It follows from Lemma 3 of Appendix that
![]()
Then, we have for any ![]()
![]()
Now, we show the equality almost surely for all
Let
and
be the processes defined by
![]()
These processes are increasing, then they are sub-martingales with respect to the filtration
Note that
and
are too continuous. Using Revuz-Yor Theorem 2.9 p. 61 [19] , they have same càd-làg modification for all b, meaning that
![]()
We conclude that, almost surely, for all ![]()
![]()
Taking
, letting n going to infinity and using monotone Lebesgue Theorem yield that, ![]()
![]()
□
Proposition 2
Proof. Let
and
be two solutions of Equation (6) and
. It follows that
(13)
where
(14)
We recall the expression
![]()
and remark that
. Then
![]()
Markov property implies
![]()
We use Lemma 4 with
and
and it follows that
![]()
and Lemma 7 (22) with the pair
gets
.
All computations are done on the set
. We observe too
is a positive
submartingale. Then for all
, we obtain by Lemma 7 (22) with the pair
, Doob’s inequality and ![]()
.
Thanks to Jensen inequality and Lemma 8 with
and
, it follows that
![]()
Concerning the numerator,
Since Novikov condition ![]()
is satisfied then
is a locally square integrable
-martingale. Once again Doob’s inequality gets
![]()
So finally
(15)
Let
and
. On the set
,![]()
. Moreover (15) proves that
so
.
It follows using (13) that
![]()
Taking
, we obtain
(16)
Then
.
By Gronwall’s lemma, we deduce that
is the unique solution of (16) on the set
, so ![]()
Uniqueness of solution of (6) is a consequence of
. □
Proposition 3
Proof. Let be a process
where the set of processes
is defined in Lemma 5 and a time t. Lemma 7 applied to
which belongs to
implies
![]()
Conditioning by
under the time integral, it follows that
![]()
Conversely compute the expectation of the product of
by right hand of (7):
.
Since
is dense in ![]()
![]()
Finally we could replace
by its
conditional expectation since
□
Proposition 4
Proof. Applying Lemma 4, it follows that
(17)
But, since the condition
is not necessarily satisfied, we are not able to prove
that
is a semi martingale (e.g. see Protter’s Theorem 65 Chapter 4 [20] ). This leads us to
consider for
the expression
instead of
at denominator of
(17). But Lemma 7 of Appendix ensures that
![]()
We apply Ito formula to the ratio of processes
. For this end, we let two processes
satisfying the stochastic equations respectively (9) and (11):
![]()
The Itô’s formula applied to
from 0 to t gives us
![]()
We achieve the proof letting
using the monotonous Lebesgue theorem since
increases to
when
. □
Theorem 1
Proof. Let us now find a mixed filtering-integro-differential equation satisfied by the conditional probability density process defined from the representation
(18)
We fix a and t such that
. Let be
, recalling the
-Markov property of X at point u and the fact that
justify
![]()
By definition of G, we have
![]()
Then
![]()
By Tonelli Theorem,
![]()
Similarly
![]()
In Equation (12) of Proposition 4,
![]()
are respectively replaced by
![]()
By hypothesis, we have
.
For
, Lemma 8 of Appendix ensures that
![]()
The numerators being bounded by
, we can apply stochastic Fubini’s theorem to Equation (12) Pro-
position 4, which can be written again as
![]()
To express this result with
conditional expectation instead of
conditional expectation, each fraction
under the integral is multiplied and divided by the same term
To manage the indicator func-
tion, we use the filtration
since
is a
-stopping time.
Therefore, using (20) in Lemma 4, on the set
we obtain
![]()
which finishes the proof. □
5. Conclusion
This paper extends the study of the first passage time for a Lévy process in [5] from complete to incomplete information and D. Dorobantu’s work in [8] from intensity to conditional density. Here, we are proving the existence of the density of
law given an information set, giving a stochastic differential integral equation satisfied by it and some numerical examples. All this gives us a behavior of the default time. In future works, we will be interested by the same studies in discrete time, in another kind of information set or under another process modeling the firm value.
Acknowledgements
We thank my PhD advisor Laure Coutin for her help and pointing out error. We thank too Monique Pontier for her careful reading. We thank the Editor and the referee for their comments. This work is supported by A.N.R. Masterie. This support is greatly appreciated.
Appendix
Lemma 1. Let be
and
real numbers and G a Gaussian random variable with mean zero and variance one, then
![]()
Proof. Indeed using the law of G, we have
![]()
Since
then
![]()
By change of variable
, it follows that
![]()
□
Lemma 2. If
is the sequence of jump time of the process N, then
![]()
Proof. We have
![]()
where
is an exponential random variable with parameter
and independent of
which follows a Gamma law with parameters n and
. Therefore
![]()
□
Lemma 3. There exists some constants
and C such that ![]()
(19)
Proof. The function f defined in (4) satisfies
![]()
Using the fact that if
then
, we have
![]()
Replacing
by its expression, we obtain
![]()
Let
. We apply this bound to
:
![]()
Remark that conditionally to process N and the
, the law of the random variable
is a
Gaussian law with mean
and variance ![]()
Applying Lemma 1 we get the conditional expectation
![]()
Using the fact that
we obtain since ![]()
![]()
The proof is completed with Lemma 2. □
The next lemma is inspired of Jeanblanc and Rutkovski [21] and Dorobantu [8] .
Lemma 4. For all
for all a and b such that
for all ![]()
(20)
For instance with
we get
![]()
Proof. Assume that there exists
such that
Then for all
It follows that the density function of
f, defined in (4), is the zero function on
. This means that
,
![]()
Then,
implies that ![]()
Thus
But we have
and on the set
Therefore,
for all t ≥ t0 Hence, we obtain ![]()
what is not possible. Indeed,
![]()
That means for all
In particular, for
,
![]()
Thus for any t, t,
and ![]()
On the set
, any
-measurable random variable coincides with some
-measurable random variable (cf. Jeanblanc and Rutkovski [21] p. 18). Then for all
, there exists a
-measurable random variable Z such that
![]()
Taking the conditional expectation with respect to
, we get
![]()
This implies that
![]()
Using Kallianpur-Striebel formula (see Pardoux [13] ) and
we obtain
![]()
□
The following is in [14] .
Lemma 5. The family of
adapted processes
![]()
is total in the set of processes taking their values in ![]()
Let us denote by
(resp.
and
) the completed, right continuous filtration generated by W, (resp. N or X)
Lemma 6. Let
be an
-progressively measurable process such that for all
, we have
![]()
Then
(21)
Proof. As in Lemma 5, the family of processes
![]()
is total in the set of processes taking their values in
where
is the compensated Poisson random measure on
and
is a Borel set.
Therefore, since
by Itô’s formula, we have
![]()
The equality is obtained from the fact that under
,
by independence. □
Lemma 7. Let be a process
such that for any t
. Let
and
, then
![]()
and
(22)
For instance
![]()
Proof. Let be
and let us define the process K
![]()
The integration by parts Itô formula applied to the product
between 0 and T yields
![]()
and remark that ![]()
Since X and Q are independent under
, we use Lemma 6 and it follows
(23)
Similarly, using first
Itô’s formula on product of processes ![]()
and the independence between X and Q under
yields
. (24)
Equations (23) and (24) imply that
![]()
Now let be
and apply the above equality to
:
![]()
so
![]()
which concludes the proof. □
Lemma 8. For all
,
,
almost surely and
![]()
Proof. The process
is a positive
(upper ) martingale, which converges to the non null random variable
(see Lemma 4) then it never vanishes.
From Corollary 2 (i), the process
is a
martingale with decom-
position
![]()
Let
using Itô’s formula for
between 0 and
and
taking the expectation we derive
![]()
Using Gronwall’s Lemma
![]()
The proof of Lemma 8 is achieved by letting n going to infinity. □