Technical part

66 CHAPTER 3. MOMENT-MATCHING OF THE CHANGE OF MEASURE Proof. For the positive boundary integral of (1.4.6) we have

Z _∞

xk−+k+

(1−√

e^θ+x)²x^−1−νe^−λ+xdx≤ Z 1

min(xk−+k+,1)

(1−√

e^θ+x)²x^−1−νe^−λ+xdx+ Z _∞

max(xk−+k+,1)

e^θ+xx^−1−νe^−λ+xdx.

The inequality is valid because0 ≤(1−√

e^θ+x)² = (1−2√

e^θ+x) +e^θ+x ≤e^θ+x for x >0. The first integral poses no problem because it is either zero or finite because the integration of the continuous integrand is over a compact interval. The finiteness of the second integral can be shown with the same reasoning as above given the restrictions onθ₊ and θ₋. The negative boundary works the same way. Hence the above integral is finite.

The integral (1.4.6) in the neighbourhood of the origin is zero, and the middle part means again integrating a continuous function over a compact interval.

For the central part ofy we have to check the integrability of a constant, the middle part requires consideration of a functiony=mx+b-mandbare arbitrary constants in R- whereas for the boundary integral to be finite the exponential function must be integrated.

For (1.4.7) we have for the positive boundary integrals Z _∞

xk−+k+

(e^x−1)e^θ+xx^−1−νe^−λ+xdx <∞. (3.2.3) This is because the integrand(e^x−1)e^θ+xx^−1−νe^−λ+x = (e^{(θ+−λ++1)x}−e^(θ+−λ+)x)x^−1−ν satisfies

xlim→∞

(e^{(θ+−λ++1)x}−e^(θ+−λ+)x)x^−1−ν

1/x² = lim

x→∞(e^{(θ+−λ++1)x}−e^(θ+−λ+)x)x^1−ν = 0 due to θ₊ < λ₊ −1 < λ₊. Hence, the integrand in (3.2.3) is bounded by 1/x² for large x with R_∞

a 1/x²dx being finite for all a > 0. Hence all this results in the existence of the integrals in (1.4.6) and(1.4.7).

3.3. FORMULATION OF THE OPTIMIZATION PROBLEM 67 For an analytic expression of the relevant integrals we can make use of theincomplete gamma function⁵

Γ[v, z] = Z _∞

z

x^−1+ve^−xdx, v >0, z >0. (3.3.2) In order to make the notation more comprehensible, it is useful to give simple expressions of the functionsA₊, A₋, B₊, and B₋ in the four variables a, b, λ, and ν. A₊ and A₋ are used for the formulation of the martingale condition:

A₊[a, b, θ, p] := c Z _b

a

(e^x−1)x^pe^θxx^−1−νe^−λ+xdx A₋[a, b, θ, p] := c

Z b a

(e^x−1)x^pe^θx|x|^−1−νe^−λ−|x|dx.

Furthermore the functionsB₊ andB₋ are helpful for the objective function and the moment conditions:

B₊[a, b, θ, p] := c Z b

a

x^pe^θxx^−1−νe^−λ+xdx B₋[a, b, θ, p] := c

Z _b

a

x^pe^θx|x|^−1−νe^−λ−|x|dx.

The range of parameters as well as simple analytic expressions in terms of the in-complete gamma function can be found in the appendix. The superscripts ‘+’ resp.

‘-’ refer to integration along parts of the positive resp. negative real axis such that b > a >0 resp. 0> b > a.

Objective function

Given a measure change functiony, i.e. a measurable functionR→R₊, the measure change process is given by

Z_t = dQ dP

F^t

=E(N)_t (3.3.3)

whereN_t = (y(x)−1)∗(µ^X−ν^P)_t is the jump-type stochastic integral process of the time-independent random function y(x)−1. This means that y determines entirely the new probability measureQ, and we can express the relative entropy (3.1.3) ofQ with respect toP in terms of the measure change function by the following lemma:

Lemma 3.3. The relative entropy process has the following representation in terms of the measure change function y

I_t(Q,P) =t Z _∞

−∞

(y(x) logy(x)−(y(x)−1))K(dx) <∞. (3.3.4)

5See Abramowitz and Stegun (1972), p.260, 6.5.1 and 6.5.2.

68 CHAPTER 3. MOMENT-MATCHING OF THE CHANGE OF MEASURE Proof. See Cont and Tankov (2004a).

The existence of the integral (3.3.4) for a linex measure change functionyfollows from the following discussion. Note that the integrand is always nonnegative⁶, and it is zero if and only ify(x)≡1 K(dx)−a.s. Unfortunately this integral cannot easily be calculated for the inner part. By defining

f(x) := y^I(x) log(y^I(x))−(y^I(x)−1)

k(x) (3.3.5)

andf_i :=f(x_i) for i∈K where y^I(x_i) =y_i, we can approximate the integral in the inner part by the trapezoidal rule.

Z xk−+k+

xk−+1

(y^I(x) logy^I(x)−(y^I(x)−1))k+(dx)

=

k−+k+−1

X

i=k−+1

Z xi+1

xi

f(x)dx

≈ 1 2

k−+k+−1

X

i=k−+1

(x_i+1−x_i)(f_i+1+f_i)

= 1 2



(x_k−+2−x_k−+1)f_k−+1+

k−+k+−1

X

i=k−+2

(xi+1−xi−1)fi





+1

2(x_k−+k+ −x_k−+k+−1)f_k−+k+

= 1 2





k−+k+−1

X

i=k−+2

(x_i+1−x_i−1)f_i+ (x_k−+k+−x_k−+k+−1)f_k−+k+



.

The last equality is a sort of telescope sum⁷. It holds because f_k−+1 = 0 due to y_k−+1 = 1. Likewise we have f_k− = 0 for the following term for the integration on

6See Abramowitz and Stegun (1972), p.68, 4.1.33.

7For the negative integral this can be seen by

k−−1

X

i=1

(xi+1−xi)(fi+1+fi)

= (x2−x1)(f2+f1) + (x3−x2)(f3+f2) + (x4−x3)(f4+f3) +. . .+ (xk₋−1−xk₋−2)(fk₋−1+fk₋−2) + (xk₋−xk₋−1)(fk₋+fk₋−1)

= (x2−x1)f1+ (x3−x1)f2+ (x4−x2)f3+. . .+ (xk₋−xk₋−2)fk₋−1+ (xk₋−xk₋−1)fk₋

3.3. FORMULATION OF THE OPTIMIZATION PROBLEM 69 the negative real line:

Z xk−

x1

(y^I(x) logy^I(x)−(y^I(x)−1))k₋(dx)

≈ 1 2



(x2−x1)f1+

k−−1

X

i=2

(xi+1−xi−1)fi



.

Especially easy is the origin part where due to the constancy ofythe relative entropy is zero. The boundary part can again exactly be calculated. Purely formal calculation for the negative part yields

Z x1

−∞

(y^B−(x) logy^B−(x)−(y^B−(x)−1))k₋(x)dx

= Z x1

−∞

y₁e^{θ−(x1−x)}log

y₁e^{θ−(x1−x)}

−(y₁e^{θ−(x1−x)}−1)

k₋(x)dx

= Z x1

−∞

(logy₁−1)y₁e^{θ−(x1−x)}+θ₋(x₁−x)y₁e^{θ−(x1−x)}+ 1

k₋(x)dx

= (logy₁−1 +θ₋x₁)y₁e^θ−x1 Z _x1

−∞

e^−θ−xk₋(x)dx−θ₋y₁e^θ−x1 Z _x1

−∞

xe^−θ−xk₋(x)dx +

Z x1

−∞

k₋(x)dx

= (logy1−1 +θ₋x1)y1e^θ−x1B₋(−∞, x1,−θ₋,0)−θ₋y1e^θ−x1B₋(−∞, x1,−θ₋,1) +B₋(−∞, x₁,0,0)

=: f⁻(y₁)

As all these terms in the last line are finite we can read the set of equations backwards and confirm the finiteness of the relative entropy associated with y. Likewise we obtain for the positive part

Z _∞

xk−+k+

(y^B+(x) logy^B+(x)−(y^B+(x)−1))k₊(x)dx

= (logy_k−+k+ −1−θ₊x_k−+k+)y_k−+k+e^{−θ+xk−+k+}B₊(x_k−+k+,∞, θ₊,0) +θ₊y_k−+k+e^{−θ+xk−+k+}B₊(x_k−+k+,∞, θ₊,1) +B₊(x_k−+k+,∞,0,0)

=: f⁺(y_k−+k+)

Hence we can write down the approximate relative entropy I˜(y) = ˜I(Q,P) in terms of they_i. Via definition of

f˜_i := ˜f(y_i) := (y_ilogy_i−(y_i−1)) andK⁰=K− {1, k₋, k₋+ 1, k₋+k₊}we obtain

I˜(y) = 1

2[(x₂−x₁) ˜f(y₁)k(x₁) +X

i∈K⁰

(x_i+1−x_i−1) ˜f(y_i)k(x_i)

+(x_k−+k+ −x_k−+k+−1) ˜f(y_k−+k+)k(x_k−+k+)] +f⁻(y₁) +f⁺(y_k−+k+).

70 CHAPTER 3. MOMENT-MATCHING OF THE CHANGE OF MEASURE Martingale condition

The aim is to get the martingale condition in an analytic form, i.e. to calculate Z _∞

−∞

(e^x−1)y(x)K(dx) =r−b. (3.3.6) To start with, the measure change function y for the inner part can be written as

y^I(x) =X

i∈K

[(y_i−x_ih_i(y_i+1−y_i)) +h_i(y_i+1−y_i)x]1_[xi,xi+1)(x) (3.3.7)

usingh_i:= 1/(x_i+1−x_i), and we obtain

Z xk−+k+

xk−+1

(e^x−1)y^I(x)k+(x)dx

= c

k−+k+−1

X

i=k−+1

Z xi+1

xi

(e^x−1)[(y_i−x_ih_i(y_i+1−y_i)) +h_i(y_i+1−y_i)x]x^−1−νe^−λ+xdx

=

k−+k+−1

X

i=k−+1

(A+[xi, xi+1,0,0](yi−xihi(yi+1−yi)) +A+[xi, xi+1,0,1]hi(yi+1−yi))

=

k−+k+−1

X

i=k−+1

{(A₊[x_i, x_i+1,0,0](1 +x_ih_i)−A₊[x_i, x_i+1,0,1]h_i)y_i + (A₊[x_i, x_i+1,0,1]−A₊[x_i, x_i+1,0,0]x_i)h_iy_i+1}

and likewise Z xk−

x1

(e^x−1)y^I(x)k₋(x)dx

=

k−−1

X

i=1

{(A₋[x_i, x_i+1,0,0](1 +x_ih_i)−A₋[x_i, x_i+1,0,1]h_i)y_i + (A₋[x_i, x_i+1,0,1]−A₋[x_i, x_i+1,0,0]x_i)h_iy_i+1}

For the origin part we have Z 0

xk−

(e^x−1)y^O(x)k₋(x)dx +

Z xk−+1

0

(e^x−1)y^O(x)k₊(x)dx

= A₋[x_k−,0,0,0] +A₊[0, x_k−+1,0,0].

3.3. FORMULATION OF THE OPTIMIZATION PROBLEM 71 Finally, the boundary region is equal to

Z x1

−∞

(e^x−1)y^B−(x)k₋(x)dx+ Z _∞

xk−+k+

(e^x−1)y^B+(x)k₊(x)dx

= c Z x1

−∞

(e^x−1)y₁e^{θ−(x1−x)}|x|^−1−νe^−λ−|x|dx +c

Z _∞

xk−+k+

(e^x−1)y_k−+k+e^{θ+(x−xk−+k+)}x^−1−νe^−λ+xdx

= A₋[−∞, x₁,−θ₋,0]e^θ−x1y₁+A₊[x_k−+k+,∞, θ₊,0]e^{−θ+xk−+k+}y_k−+k+. Using all this information we can define the vector µ¯ ∈R^k−+k+−2 of coefficients of the values y_i:

¯ µ_i:=















































(A₋[x₁, x₂,0,0](1 +x₁h₁)−A₋[x₁, x₂,0,1]h₁) +

A₋[−∞, x₁,−θ₋,0]e^θ−x1 i= 1 (A₋[x_i, x_i+1,0,0](1 +x_ih_i)−A₋[x_i, x_i+1,0,1]h_i) +

(A₋[x_i−1, x_i,0,1]−A₋[x_i−1, x_i,0,0]x_i−1)h_i−1 i= 2, . . . , k₋−1 (A₊[x_i, x_i+1,0,0](1 +x_ih_i)−A₊[x_i, x_i+1,0,1]h_i) +

(A₊[x_i−1, x_i,0,1]−A₊[x_i−1, x_i,0,0]x_i−1)h_i−1 i=k₋+ 2, . . . , k₋+k₊−1 A₊[x_k−+k+−1, x_k−+k+,0,1]−

A₊[x_k−+k+−1, x_k−+k+,0,0]x_k−+k+−1 h_k−+k+−1+

A₊[x_k−+k+,∞, θ₊,0]e^{−θ+xk−+k+} i=k₋+k₊. Observe that in this representation we have already accounted for the condition y_k−=y_k−+1 = 1. Henceµ¯ has dimension k₋+k₊−2 and notk₋+k₊. We define the real constant β¯by:

β¯ = r−b−(A₋[x_k−−1, x_k−,0,1]−A₋[x_k−−1, x_k−,0,0]x_k−−1)h_k−−1

−A+[x_k−+1, x_k−+2,0,0](1 +x_k−+1h_k−+1) +A+[x_k−+1, x_k−+2,0,1]h_k−+1

−A₋[x_k−,0,0,0]−A₊[0, x_k−+1,0,0].

Then the drift parameterb in the Lévy triplet is given by⁸ b = µ−

Z _∞

−∞

xK(dx)

= µ+c Z _∞

0

x^{−1+(1−ν)}e^−λ−xdx−c Z _∞

0

x^{−1+(1−ν)}e^−λ+xdx

= µ+c(λ^ν₋⁻¹−λ^ν₊⁻¹)Γ(1−ν).

8For the parameterµ=E^P[X1]see Section 1.5.2.

72 CHAPTER 3. MOMENT-MATCHING OF THE CHANGE OF MEASURE Moment conditions

The objective is to compute the risk-neutral moments in a preferably simple way in terms of the measure change function y resp. the values (y_j)_{j=1,...,k−+k+}. The starting point is provided by the standardized central moments of the distribution of X1, which correspond to the log returns over one period of the considered asset price process. These moments, namely mean value mean, volatility vol, skewness skew, and kurtosiskurtare the ones which can intuitively be grasped and compared to the respective values of other assets. Given the mean and standard deviation of a distribution they are defined according to

skew= µ₃

vol³ and kurt= µ₄ vol⁴

where µ_n, n= 1, . . . ,4 denote the central moments. Put differently we have

µ₁ =mean, µ₂=vol², µ₃ =skew∗vol³, µ₄=kurt∗vol⁴. (3.3.8) It is well-known that the n-th moment is obtained by n-fold differentiation of the characteristic function z → E^Q[e^izX1] evaluated at z = 0 whereas the cumulants of order n correspond to the n-th derivative of the cumulant function at z = 0 according to (3.1.1). Putting this together we can represent the cumulants in terms of the moments:⁹

κ₁ =µ₁, κ₂ =µ₂, κ₃=µ₃, κ₄=µ₄−3µ²₂. (3.3.9) The cumulants are now the quantities whose theoretical values can be relatively com-fortably computed in terms of(y_j)_{j=1,...,k−+k+} by the formula (3.1.1). The following inequality holds:

Z _∞

−∞|x|^ky(x)k(x)dx <∞ ∀k∈N. (3.3.10) Given the parameter restrictions the only thing which is to be checked is the finiteness around zero. But this is clear due tok−ν >0fork ≥1which is necessary and - given the admissible range of parameters - sufficient for the convergence of the integrals that possess the structure of the gamma function and that will be calculated in the sequel. Due to Lemma 1.17 equation (3.3.10) entails the existence of all moments of integer order of the risk-neutral probability measure. We observe that X is no longer a tempered stable Lévy process underQ.

Lemma 3.4.

ψ_Q^(k)(0) =i^k Z _∞

−∞

x^ky(x)k(x)dx ∀k∈N\ {1}. (3.3.11) Proof. We have to differentiate the cumulant function of the risk-neutral probability measureQ, i.e.

ψ_Q(z) =ib⁰z+ Z _∞

−∞

(e^izx−1)K⁰(dx).

9See e.g. Abramowitz and Stegun (1972), 26.1.13.

3.3. FORMULATION OF THE OPTIMIZATION PROBLEM 73 The k-th derivative of the integrand is (ix)^ke^izx and we have |(ix)^ke^izx| = |x|^k for any non-trivial interval containing zero. As seen above |x|^k is K⁰-integrable.

Hence according to the differentiation lemma (see Bauer (1992), Lemma 16.2) we can interchange differentiation and integration. Forz= 0the result is obtained.

Formula (3.1.1) and Lemma 3.4 provide us with analytical expressions for the cumulants of the distribution ofX₁ under the risk-neutral probability measureQ.

Examining the integral R_∞

−∞x^ky(x)k(x)dx more closely we obtain for the positive inner part the expression

Z xk−+k+

xk−+1

x^ky^I(x)k+(x)dx

= c

k−+k+−1

X

i=k−+1

Z xi+1

xi

x^k[(y_i−x_ih_i(y_i+1−y_i)) +h_i(y_i+1−y_i)x]x^−1−νe^−λ+xdx

=

k−+k+−1

X

i=k−+1

(B₊[x_i, x_i+1,0, k](y_i−x_ih_i(y_i+1−y_i)) +B₊[x_i, x_i+1,0, k+ 1]h_i(y_i+1−y_i))

=

k−+k+−1

X

i=k−+1

{(B₊[x_i, x_i+1,0, k](1 +x_ih_i)−B₊[x_i, x_i+1,0, k+ 1]h_i)y_i + (B+[xi, xi+1,0, k+ 1]−B+[xi, xi+1,0, k]xi)hiyi+1},

and for the negative one Z xk−

x1

x^ky^I(x)k₋(x)dx

=

k−−1

X

i=1

{(B₋[xi, xi+1,0, k](1 +xihi)−B₋[xi, xi+1,0, k+ 1]hi)yi

+ (B₋[x_i, x_i+1,0, k+ 1]−B₋[x_i, x_i+1,0, k]x_i)h_iy_i+1}.

For the neighbourhood around the origin and for the boundary part we obtain

Z 0 xk−

x^ky^O(x)k₋(x)dx +

Z xk−+1

0

x^ky^O(x)k₊(x)dx

= B₋[x_k−,0,0, k] +B₊[0, x_k−+1,0, k]

74 CHAPTER 3. MOMENT-MATCHING OF THE CHANGE OF MEASURE and

Z _x1

−∞

x^ky^B−(x)k₋(x)dx+ Z _∞

xk−+k+

x^ky^B+(x)k₊(x)dx

= c Z x1

−∞

x^ky₁e^{θ−(x1−x)}|x|^−1−νe^−λ−|x|dx +c

Z _∞

xk−+k+

x^ky_k−+k+e^{θ+(x−xk−+k+)}x^−1−νe^−λ+xdx

= B₋[−∞, x₁,−θ₋, k]e^θ−x1y₁+B₊[x_k−+k+,∞, θ₊, k]e^{−θ+xk−+k+}y_k−+k+. Analogously to the case of the martingale condition the vectorsµ⁽ⁿ⁾ ∈R^k−+k+−2, n= 2,3,4, of coefficients of the values y_i are the following ones:

¯ µ⁽ⁿ⁾_i :=















































(B₋[x₁, x₂,0, n](1 +x₁h₁)−B₋[x₁, x₂,0, n+ 1]h₁)

+B₋[−∞, x₁,−θ₋, n]e^θ−x1 i= 1 (B₋[x_i, x_i+1,0, n](1 +x_ih_i)−B₋[x_i, x_i+1,0, n+ 1]h_i)

+ (B₋[x_i−1, x_i,0, n+ 1]−B₋[x_i−1, x_i,0, n]x_i−1)h_i−1 i= 2, . . . , k₋−1 (B₊[x_i, x_i+1,0, n](1 +x_ih_i)−B₊[x_i, x_i+1,0, n+ 1]h_i)

+ (B₊[x_i−1, x_i,0, n+ 1]−B₊[x_i−1, x_i,0, n]x_i−1)h_i−1 i=k₋+ 2, . . . , k₋+k₊−1 B₊[x_k−+k+−1, x_k−+k+,0, n+ 1]−

B₊[x_k−+k+−1, x_k−+k+,0, n]x_k−+k+−1 h_k−+k+−1

+B+[x_k−+k+,∞, θ+, n]e^{−θ+xk−+k+} i=k₋+k+

The real constants β¯⁽ⁿ⁾ are equal to

β¯⁽ⁿ⁾ = κ_n−(B₋[x_k−−1, x_k−,0, n+ 1]−B₋[x_k−−1, x_k−,0, n]x_k−−1)h_k−−1

−B₊[x_k−+1, x_k−+2,0, n](1 +x_k−+1h_k−+1) +B₊[x_k−+1, x_k−+2,0, n+ 1]h_k−+1

−B₋[x_k−,0,0, n]−B₊[0, x_k−+1,0, n].

Im Dokument The Change of Measure and Non-Linear Dependence (Seite 76-84)