Description of Incomplete Financial Markets for Time Evolution of Risk Assets ()
1. Introduction
In the paper, the notion of the regular super-martingale relative to the set of equivalent measures is introduced. The necessary and sufficient conditions of the regular super-martingale relative to the set of equivalent measures are found. The notion of the family of equivalent measures consistent with filtration is introduced. Theorem giving the sufficient conditions of the existence of super-martingale and martingale relative to the set of equivalent measures consistent with the filtration is proved. The sufficient conditions of the existence of the set of equivalent measures consistent with the filtration, satisfying the conditions: the mean value of the nonnegative random value relative to these set of measures equal one, are given. Further, we construct the set of equivalent measures consistent with the filtration satisfying the above conditions. First, we give the complete description of the set of equivalent measures satisfying the conditions: the mean value of the nonnegative random value relative to this set of measures equals one. Using the above result we construct the example of the set of equivalent measures consistent with the filtration satisfying the condition: the mean value of the nonnegative random value relative to every measure of this set of measures equals one. The above method we use for the construction of evolution
of risk assets and we describe completely the set of equivalent martingale measures for this evolution. We prove that every martingale measure is an integral on the set of extreme points of the convex set of martingale measures relative to some measure on it. To give a new proof of the optional decomposition for super-martingale we describe the contraction of every martingale measure on the filtration and find the closure of integrals from the integrable random values over all martingale measures. To do this we introduce the notion of the exhaustive decomposition and prove that every separable metric space with the Borel σ-algebra has an exhaustive decomposition. For the integral from the nonnegative random value relative to all martingale measures which is dominated by one, the inequalities for this random value are obtained. This fact gives us the possibility to find a new proof of the optional decomposition for the nonnegative super-martingale. This proof does not use the no-arbitrage arguments and the measurable choice [1] [2] [3] [4] . This paper is a generalization of the results of the paper [5] .
First, the optional decomposition for diffusion processes super-martingale was opened by El Karoui N. and Quenez M. C. [6] . After that, Kramkov D. O. and Follmer H. [1] [2] proved the optional decomposition for the nonnegative bounded super-martingales. Folmer H. and Kabanov Yu. M. [3] [4] proved analogous result for an arbitrary super-martingale. Recently, Bouchard B. and Nutz M. [7] considered a class of discrete models and proved the necessary and sufficient conditions for the validity of the optional decomposition.
The optional decomposition for super-martingales plays the fundamental role for the risk assessment in incomplete markets [1] [2] [6] [8] [9] [10] [11] .
At last, we consider an application of the results obtained to find the new formula for the fair price of super-hedge in the case, as the risk asset evolves by the discrete geometric Brownian motion.
2. Local Regular Super-Martingales Relative to a Set of Equivalent Measures
We assume that on a measurable space
a filtration
, and a set of equivalent measures M on
are given. Further, we
assume that
and the σ-algebra
is a minimal σ-algebra generated by the algebra
. A random process
is
said to be adapted one relative to the filtration
, if
is a
measurable random value,
.
Definition 1. An adapted random process
is said to be a super-martingale relative to the filtration
, and the family of equivalent measures M, if
, and the inequalities
(1)
are valid.
Further, for an adapted process f we use both the denotation
and the denotation
.
Definition 2. A super-martingale
relative to a set of equivalent
measures M is a local regular one, if
, and there exists
an adapted nonnegative increasing random process
,
, such that
is a martingale relative to
every measure from M.
The next elementary Theorem 1 will be very useful later.
Theorem 1. Let a super-martingale
, relative to a set of equivalent measures M be such that
. The necessary and sufficient
condition for it to be a local regular one is the existence of an adapted nonnegative
random process
,
, such that
(2)
Proof. The necessity. If
is a local regular super-martingale, then there exist a martingale
and a non-decreasing nonnegative random process
,
, such that
(3)
From here, we obtain the equalities
(4)
where we introduced the denotation
. It is evident that
.
The sufficiency. Suppose that there exists an adapted nonnegative random process
,
, such that the equalities (2) hold. Let us consider the random process
, where
(5)
It is evident that
and
(6)
Theorem 1 is proved. □
Lemma 1. Any super-martingale
relative to a family of measures M for which there hold equalities
, is a martingale with respect to this family of measures and the filtration
.
Proof. The proof of Lemma 1 see [12] . □
In the next Lemma, we present the formula for calculation of the conditional expectation relative to another measure from M.
Lemma 2. On the measurable space
with the filtration
on it, let M be a set of equivalent measures and let
be an integrable random value. Then, the following formulas
(7)
are valid, where
(8)
Proof. The proof of Lemma 2 is evident. □
3. Local Regular Super-Martingales Relative to a Set of Equivalent Measures Consistent with the Filtration
Definition 3. On a measurable space
with a filtration
on it, a set of equivalent measures M we call consistent with the filtration
, if for every pair of measures
the set of measures
(9)
belongs to the set M, where
is a direct product of the set M by itself.
Lemma 3. On the measurable space
with the filtration
on it, the set of measures
(10)
is a consistent one with the filtration
, if P is a measure on
and a random value
runs over all nonnegative random values, satisfying the condition
.
Proof. Suppose that
belongs to
. Then,
and
, since the equalities
,
are true. It is evident that
(11)
It is easy to see that
(12)
since
(13)
(14)
The last equality follows from the equivalence of the measures
and P. Altogether, it means that the set of measures
, belongs to the set M. The same is true for the pair
. Lemma 3 is proved. □
Theorem 2. On the measurable space
with the filtration
on it, let the set of equivalent measures M be consistent with the filtration
. Then,
for every nonnegative random value
such that
, the random process
is a super-martingale relative to the set of measures M, where
.
Proof. Let
, then, due to Lemma 2, for every
(15)
If to put instead of the measure P the measure
, for the pair of measures
we obtain
(16)
where we took into account the equality
(17)
From the formula (16), it follows the equality
(18)
where
is a set of martingales
relative to the measure Q such
that
,
. The definition of
for the uncountable set of random values see [13] . It is evident that
. Let us consider
(19)
where
(20)
(21)
Lemma 2 is proved. □
Theorem 3. On the measurable space
,
, let M be a set
of equivalent measures being consistent with the filtration
. If there exists a nonnegative random value
such that
, then
, is a local regular martingale.
Proof. Due to Lemma 2, the random process
, where
,
, is a super-martingale relative to the set of
measures M, that is,
(22)
From the inequality (22), it follows the inequality
(23)
Since
, we have
(24)
The inequalities (22) and the equalities (24) give the equalities
(25)
which are true with the probability 1. The last means that
is a martingale relative to the set of measures M, where
. With the probability 1,
, where the random value
is F measurable one. From the inequality (23) and Fatou Lemma [13] [14] , we obtain
(26)
Prove that
. Going to the limit in the inequality
(27)
as
, we obtain the inequality
(28)
From the inequality (26) and the inequality (28), we obtain the inequalities
. Or,
. The equalities
and the inequality (28) give the equality
with the probability 1. Lemma 3 is proved. □
Lemma 4. On the measurable space
with the filtration
on it, let there exist k equivalent measures
, and a nonnegative random value
be such that
(29)
Then, there exists the set of equivalent measures M consistent with the filtration
, satisfying the condition
.
Proof. Let us consider the set of equivalent measures M, satisfying the condition
(30)
Such a set of measures is a nonempty one. Suppose that
, then
(31)
Let us prove that the formula
(32)
is valid. Let
. Then, from the equalities (31), we have
Let
. Then,
This proves the formula (32). To finish the proof of Lemma 4, it needs to prove that the set of measures
(33)
belongs to the set M. Really,
(34)
where we took into account the equality
(35)
From this, it follows that the set of measures
. This proves the consistence with the filtration of the set of measures M. Lemma 4 is proved. □
The next Lemma 5 is a key statement in the construction of the set of measures satisfying the conditions of Lemma 4.
On a probability space
, let
be a random value, satisfying the conditions
(36)
Denote
and let
be the restrictions of the σ-algebra
on the sets
and
, correspondingly. Suppose that
and
are the contractions of the measure P on the σ-algebras
, correspondingly. Consider the measurable space with measure
, which is a direct product of the measurable spaces with measures
and
, where
. Introduce the denotations
(37)
(38)
Then,
.
On the measurable space
, we assume that the set of nonnegative measurable functions
, satisfying the conditions
(39)
(40)
(41)
is a nonempty set. Such assumptions are true for the nonempty set of bounded random values
, for example, if the random value
is an integrable one relative to the measure P.
Lemma 5. On the probability space
, let a random value
satisfy the conditions (36) and let a measure Q be equivalent to the measure P and such that
. Then, for the measure Q the following representation
(42)
is valid for those random value
that satisfy the conditions (39)-(41).
Every measure Q, given by the formula (42), with the random value
, satisfying the conditions (39)-(41) is equivalent to the measure P and is such that
. For the measure Q, the canonical representation
(43)
is valid, where
(44)
(45)
(46)
(47)
Proof. From the Lemma 5 conditions,
(48)
(49)
The condition (49) means
(50)
where
(51)
(52)
Let us put
(53)
Then, for such
the equality (39) is true. Moreover,
(54)
(55)
(56)
since
.
Let us prove the last statement of Lemma 5. Suppose that the representation (42) for the measure Q, satisfying the conditions (39)-(41), is valid. Taking into account the denotations (45)-(47), we obtain
(57)
(58)
If to introduce the denotation
(59)
then we obtain the representation
(60)
where
.
The last formula proves the equivalence of the measures Q and P. At last, to prove the canonical representation (43) it is sufficient to substitute the expression (44) for
into the expression (43) for
. We obtain the expression (57) for
. Then, if to substitute the expressions (45), (46) for
into the expression (57) for
, we obtain that the canonical representation for
is true. This proves Lemma 5. □
For further investigations, the next Theorem 4 is very important [5] .
Theorem 4. The necessary and sufficient conditions of the local regularity of the nonnegative super-martingale
relative to a set of equivalent measures M are the existence of
-measurable random values
, such that
(61)
Proof. The necessity. Without loss of generality, we assume that
for a certain real number
. Really, if it is not so, then we can come to the consideration of the super-martingale
. Thus, let
be a nonnegative local regular super-martingale. Then, there exists a nonnegative
adapted random process
, such that
,
(62)
Let us put
. Then,
and from the equalities
(62) we obtain
. It is evident that the inequalities (61) are valid.
The sufficiency. Suppose that the conditions of Theorem 4 are valid. Then,
. Introduce the denotation
. Then,
,
. The last equality and
the inequalities give
(63)
Let us consider the random process
, where
. Then,
. Theorem 4 is proved. □
4. Construction of the Regular Set of Measures
In the next two Lemmas, we investigate the closure of a convex set of equivalent measures presented in Lemma 5 by the formula (42). First, we consider the countable case.
Suppose that
contains the countable set of elementary events and let
be a σ-algebra of all subsets of the set
. Let
be a measure on the σ-algebra
. We assume that
. On the probability space
, let us consider a nonnegative random value
, satisfying the conditions
(64)
where we introduced the denotation
. On the measurable space
, let us consider the set of measures
, which are equivalent to the measure
and are given by the formula
(65)
where
,
,
. Introduce the denotations
,
. Let
be a contraction of the measure
on the σ-algebra
and let
be a contraction of the measure
on the σ-algebra
. On the probability space
, the set of random value
satisfies the conditions
(66)
(67)
(68)
On the probability space
, all the bounded strictly positive random values
the above conditions satisfy. Introduce into the set of all measures on
the metrics
(69)
Lemma 6. The closure of the set of measures
in metrics (69) contains the set of measures
(70)
for
,
,
. For every bounded random value
, the closure of the set of points
, in metrics
, contains the points
.
Proof. Let us choose the set of equivalent measures
defined by
, and given by the law:
It is evident that
, for every
, and satisfy the equality
(71)
Then,
(72)
(73)
(74)
(75)
If
, then
(76)
(77)
The distance between the measures
and
is given by the formula
(78)
Since
we obtain
Let us prove the second part of Lemma 6. It is evident that the inequality
(79)
is true. Due to arbitrariness of the small
, Lemma 6 is proved. □
Definition 4. Let
be a measurable space. The decomposition
, of the space
we call exhaustive one if the following conditions are valid:
1)
;
2) the
-th decomposition is a sub-decomposition of the n-th one, that is, for every j,
for a certain
;
3) the minimal σ-algebra containing all
, coincides with
.
The next Remark 1 is important for the construction of the filtration having the exhaustive decomposition.
Remark 1. Suppose that the measurable spaces
and
have the exhaustive decompositions
and
, respectively, then the measurable space
also have the exhaustive decomposition
,
. Really,
1)
,
;
2) the
-th decomposition is a sub-decomposition of the n-th one, that is, for every
for a certain
;
3) the minimal σ-algebra containing all
, coincides with
.
In the next Lemma we give the sufficient condition of the existence of exhaustive decomposition.
Lemma 7. Let
be a measurable space with a complete separable metric space
and Borel σ-algebra
on it. Then
has an exhaustive decomposition.
Proof. If
is a countable dense set in
, then we denote
(80)
the countable set of open balls as
runs all positive rational numbers, where
is a metric in
. Prove that
, where
is a minimal σ-algebra generated by the sets (80). For this purpose let us prove that for every open set
the representation
(81)
is true, where
is a subset of positive integers, and
is a subset of positive rational numbers. Let us denote
. Suppose that
, then
, where
is a closure of the set A.
Let the point
belong to the ball
and
let us consider the ball
. The point
belongs to this ball and for every
the inequality
(82)
is true. Therefore
. Let the rational number
satisfies the inequalities
(83)
then
, since for every
,
. So, for
we found
and the rational number
such that
. The last prove the needed statement. To complete the proof of Lemma 7 let us construct the exhaustive decomposition. Let us renumber the sets
putting by
,
,
, and so on. We put that
consists of two sets
and
. If the set
is constructed, then the set
we construct from the various set of the kind
. By construction the minimal σ-algebra
. Taking into account the previous part of the proof we have
. Lemma 7 is proved. □
Lemma 8. Let a measurable space
have an exhaustive decomposition and let
be an integrable random value relative to the measure P, satisfying the conditions (36). Then, the closure of the set of measure Q, given by the formula (42), relative to the pointwise convergence of measures contains the set of measures
(84)
for those
which have the full measure
For every integrable finite valued random value
relative to all measures Q, the closure in metrics
,
, of the set of real numbers
(85)
when
runs over all random values satisfying the conditions (39), (41), contains the set of numbers
(86)
Proof. On a probability space
, let
be an integrable random value, satisfying the conditions (36). As before, let
,
and let
be the restrictions of the σ-algebra
on the sets
and
, correspondingly. Suppose that
and
are the contractions of the measure P on the σ-algebras
, correspondingly. Consider the probability space
which is a direct product of the probability spaces
and
. Due to Lemma 8 conditions and Remark 1, the measurable space
has the exhaustive decomposition
. Denote
the minimal σ-algebra generated by decomposition
. It is evident
that
. Moreover,
. On the probability space
, for every integrable finite valued random value
the sequence
converges to
with probability one, as
, since it is a regular martingale. It is evident that for those
for which
(87)
Denote
. It is evident that
. For every
, the formula (87) is well defined and is finite. Let
be the subset of the set
, where the limit of the left hand side of the formula (87) does not exist. Then,
. For every
, the right hand side of the formula (87) converges to
. For
, denote
those set
for which
for a certain
. Then, for every integrable finite valued
(88)
Let us consider the sequence
(89)
where
,
. Such a sequence
satisfies the conditions (39)-(41) and
(90)
From the formula (90), we obtain
(91)
Further,
(92)
From the formula (92), we obtain
(93)
Lemma 8 is proved. □
The next Theorem 5 is a consequence of Lemma 5.
Theorem 5. On the probability space
, for the nonnegative random value
the set of measures
on the measurable space
, being equivalent to the measure P, satisfies the condition
(94)
if and only if as for
the representation
(95)
is true, where on the measurable space
, the random value
satisfies the conditions
(96)
(97)
(98)
We introduced above the following denotations:
,
is a contraction of the measure P on the set
,
is a contraction of the measure P on the set
,
,
.
It is evident that the set of measure
is a nonempty one, since it contains those measures Q, for which the random value
is bounded, since
. The set of measure
is consistent with the filtration
on the measurable space
, where
.
Theorem 6. On the probability space
with the filtration
on it, the set of measures
, given by the formula (95), is consistent with the filtration
, if and only if, as
, is a local regular martingale.
Proof. The necessity. Let the set of measures
be consistent with the filtration. Then, due to Theorem 3,
, is a local regular martingale.
The sufficiency. Suppose that
, is a local regular martingale. Let us prove that, if
, then the set of measures
(99)
belongs to the set
. For this, it is to prove that
, or
. Really, if
, then
(100)
Theorem 6 is proved. □
Theorem 7. On the probability space
with the filtration
on it, the set of measures
, given by the formula (95), is consistent with the filtration
, if and only if there exists not depending on
the random process
such that
(101)
for those
that have the full measure
, where
(102)
Proof. The necessity. Suppose that the set of measures
, given by the formula (95), is consistent with the filtration
. Due to Theorem 6,
, is a local regular martingale. Then,
. Using Lemma 8, we obtain
for those
that have the full measure
.
The sufficiency. If the formula (101) is true, then
. From this, it follows that
, is a local regular martingale. Theorem 7 is proved. □
Definition 5. On the probability space
with the filtration
on it, the consistent with the filtration
subset of the measures M of the set of the measures
generating by the nonnegative random value
,
, we call the regular set of measures.
Let
be a probability space. On the measurable space
with the filtration
on it, let
be a set of regular measures, where the set
is generated by the nonnegative random value
. Denote by
the regular martingale, where
. Assume that
is a contraction of the set of regular measures M onto the σ-algebra
. Every
is equivalent to
, where
is a contraction of the measure P on the σ-algebra
. For every
, we have
. Therefore, for the measure
the representation
(103)
is true, where, on the measurable space
, the random value
satisfies the conditions
(104)
(105)
(106)
Here, the measure
is given on the measurable space
and it is a direct product of the measures
and
, where the measure
is a contraction of the measure
on the σ-algebra
and
is a contraction of the measure
on the σ-algebra
. It is evident that the regular set of measures M is a convex set of measure.
Definition 6. On the probability space
with the filtration
on it, denote by
the set of all nonnegative integrable random values
relative to the set of regular measures M, satisfying the conditions:
(107)
Due to Theorem 3,
is a regular martingale relative to the set of measures M.
It is evident that the set
is a nonempty one, since it contains the random value
. The more interesting case is as
contains more than one element. So, further we consider the regular set of measure M with the set
, containing more than one element.
In the next Lemma 9, using Lemma 5, we construct a set of measures consistent with the filtration. On the probability space
, let us consider a nonnegative random value
, satisfying the conditions
(108)
where we introduced the denotation
. Described in Lemma 5 the set of equivalent measures to the measure
and such that
, we denote by
. Let us construct the infinite direct product of the measurable
spaces
, where
. Denote
. On
the space
, under the σ-algebra
we understand the minimal σ-algebra,
generated by the sets
, where in the last product only the finite set
of
do not equal
. On the measurable space
, under the filtration
we understand the minimal σ-algebra generated by the sets
,
where
for
. We consider the probability space
, where
.
On the measurable space
, we introduce into consideration the set of
measures M, where Q belongs to M, if
. We denote by
a subset of the set M of those measures
, for which only the
finite set of
does not coincide with the measure
.
Lemma 9. On the measurable space
with the filtration
on it, there exists consistent with the filtration
the set of measures
and the nonnegative random variable
such that
, if the random value
, satisfying the conditions (108), is bounded.
Proof. To prove Lemma 9, we need to construct a nonnegative bounded random value
on the measurable space
and a set of equivalent measures
on it, such that
, and to prove that the set of measures
is consistent with the filtration
. From the Lemma 9 conditions, the random value
is also bounded. Let us put
(109)
where the random values
are
-measurable,
, they satisfy the conditions
. The constants
are such
that
, the random value
is given on
and is
distributed as
on
. From this, it follows that the random
value
is bounded by the constant
, where
and it is such
that
. It is evident that
. Really,
(110)
where
,
(111)
From the last equality, we have
(112)
Since
, from the equality (112) and the
possibility to go to the limit under the mathematical expectation, we prove the needed statement. Let us prove the existence of the set of measures
consistent with the filtration
. If
, then
(113)
Due to Lemma 4, there exists a set of measures
such that it is consistent with the filtration and
,
. The set
is a linear convex span of the set
. It means that the set of measures
is consistent with the filtration. Lemma 9 is proved. □
Remark 2 The boundedness of the random value
is not essential. For the applications, the case, as
, is very important (see Section 8). In this case, Lemma 9 is true as the random value
is an integrable one. The random value
is also integrable one relative to every measures from the set
and it is
-measurable one.
Below, we describe one class of evolutions of risk assets satisfying no arbitrage condition [15] - [20] and give the complete description of the set of equivalent martingale measures.
On the introduced measurable space
we consider the evolution of the risk asset given by the law
(114)
where the random values
are
-measurable,
, they satisfy the conditions
,
, the random value
is given on
and is distributed as
on
. The main aim is to describe the set of martingale measures for the evolution of risk asset given by the formula (114). This problem we solve in Theorem 8.
Below, we describe completely the regular set of measures in the case as
,
, and
the random value
is an integrable one relative to the measure
. For this purpose, we introduce the denotations:
,
,
is a contraction of the measure
on the σ-algebra
,
is a contraction of the measure
on the σ-algebra
,
,
.
Denote
and introduce the measure
on the σ-algebra
. Let us introduce the measurable space
,
where
, is a direct product of the spaces
,
,
,
is a direct product of the σ-algebras
. At last, let
be a direct product of the measures
, and let
,
be a direct product of the measures
, which is a countable additive function on the σ-algebra
for every
, where
(115)
for
.
In the next Theorem 8, we assume that the random value
is an integrable one.
Theorem 8. On the measurable space
with the filtration
on it, every measure Q of the regular set of measures M for the random value
,
, has
the representation
(116)
where the random value
satisfies the conditions
(117)
(118)
(119)
Proof. To prove Theorem, it needs to prove that the countable additive measure
at every fixed
is a measurable map from the measurable space
into the measurable space
for every fixed
. For
,
is a measurable map from the measurable space
into the measurable space
. The family of sets of the kind
,
, where
, the set I is an
arbitrary finite set, forms the algebra of the sets that we denote by
. From the
countable additivity of
,
is a measurable map
from the measurable space
into the measurable space
. Let T be a class of the sets from the minimal σ-algebra
generated by
for every subset E of that
is a measurable map from the measurable space
into the measurable space
. Let us prove that T is a monotonic class. Suppose that
. Then,
. From this, it follows that
is a measurable map from the measurable space
into the measurable space
. But,
is a measurable map from
into
. From this equality, it follows that the set
belongs to
the class T. Since
, we have
(120)
The equalities (120) mean that
belongs to T, since
is a
measurable map of
into
. Suppose that
. Then, this case is reduced to the previous one by the
note that the sequence
is monotonically increasing. From this, it follows that
. Therefore,
.
Thus, T is a monotone class. But,
. Hence, T contains the minimal monotone class generated by the algebra
, that is,
, therefore,
. Thus,
is a measurable map of
into
for
. The fact that the random value
satisfies the conditions (117)-(119) means that Q, given by the formula (116), is a countable additive function of sets and
. Moreover,
. It is evident that
. Due to Lemma 4, this proves that the set M is a regular set of measure. Theorem 8 is proved. □
Remark 3. The representation (116) for the regular set of measures M means that M is a convex set of equivalent measures. Since the random value
runs all bounded random values, satisfying the conditions (117 - 119), it is easy to show that the set of measures
, is the set of extreme points for the set M.
Let us introduce the denotations
(121)
(122)
Note that the σ-algebra
is generated by sets of the kind
, where
. Denote
the contraction of the measure
onto the σ-algebra
. Further we use the denotations
and
which are the contractions the measure
onto the σ-algebras
and
, correspondingly. If the measure Q belongs to the set of martingale measures (116), then
, or
. From this, for the measure Q the representation
(123)
is true if the random value
satisfies the condition
(124)
Since for the set
the representation
, is true, where
, then for the contraction
of the measure Q onto the σ-algebra
the representation
(125)
is true, where we introduced the denotations
and
which are the contractions of the measure
onto the σ-algebras
and
, correspondingly,
(126)
In the set
let us introduce the transformation
(127)
By the definition we put that for
the transformation
is identical one. Introduce the denotations
(128)
(129)
(130)
(131)
Theorem 9. Let
be a complete separable metric space and
be a Borel σ-algebra on it. If the condition
(132)
is true for
-measurable nonnegative random value
, then the closure of the set of points
, in metrics
on the real line contains the set of points
(133)
Proof. Let us find the conditions for the measurable functions
under which
. Introduce the denotation
(134)
Let the set B belongs to
, then
(135)
If to take into account the relations
(136)
and introduce the denotations
(137)
(138)
we obtain