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Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Wiener-Hopf factorization and distribution ofextrema for a family of Levy processes

Alexey Kuznetsov

Department of Mathematics and StatisticsYork University

June 20, 2009

Research supported by the Natural Sciences and Engineering Research Council of Canada

W-H factorization and distribution of extrema Alexey Kuznetsov 0/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 0/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Wiener-Hopf factorization

Review of Wiener-Hopf factorization

The characteristic exponent Ψ(z) is

E[eizXt

]= exp(−tΨ(z)),

and by the Levy-Khintchine representation

Ψ(z) = −iµz +σ2z2

2−∫R

(eizx − 1− izx

)ν(x)dx

We define extrema processes Mt = sup{Xs : s ≤ t} andNt = inf{Xs : s ≤ t}, introduce an exponential random variableτ = τ(q) with parameter q > 0, which is independent of the processXt, and use the following notation for characteristic functions of Mτ ,Nτ :

φ+q (z) = E

[eizMτ

], φ−q (z) = E

[eizNτ

]W-H factorization and distribution of extrema Alexey Kuznetsov 1/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Wiener-Hopf factorization

Review of Wiener-Hopf factorization

TheoremRandom variables Mτ and Xτ −Mτ are independent, randomvariables Nτ and Xτ −Mτ have the same distribution, and for z ∈ Rwe have

q

Ψ(z) + q= E

[eizXτ

]= E

[eizMτ

]E[eiz(Xτ−Mτ )

]= φ+

q (z)φ−q (z)

Moreover, random variable Mτ [Nτ ] is infinitely divisible, positive[negative] and has zero drift.

W-H factorization and distribution of extrema Alexey Kuznetsov 2/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 2/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

WH for Brownian motion with drift

Let Xt = Wt + µt. Then Ψ(z) = z2

2 − iµz and Ψ(z) + q = 0 has twosolutions

z1,2 = i(µ±√µ2 + 2q)

Thus function q(Ψ(z) + q)−1 can be factorized as

q

Ψ(z) + q=

qz2

2 − iµz + q

=µ+

õ2 + 2q

iz + µ+√µ2 + 2q

× µ−√µ2 + 2q

iz + µ−√µ2 + 2q

W-H factorization and distribution of extrema Alexey Kuznetsov 3/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

WH for Brownian motion with drift

The main idea: since the random variable Mτ [Nτ ] is positive[negative], its characteristic function must be analytic and have nozeros in C+ [C−], where

C+ = {z ∈ C : Im(z) > 0}, C− = {z ∈ C : Im(z) < 0}, C± = C± ∪ R.

Thus

φ+q (z) =

µ−√µ2 + 2q

iz + µ−√µ2 + 2q

and Mτ is an exponential random variable with parameter√µ2 + 2q − µ.

W-H factorization and distribution of extrema Alexey Kuznetsov 4/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

Computing density of Mt

Let pt(x) be the density function of Mt and pM (q, x) the densityfunction of Mτ(q). Then

pM (q, x) = E[pτ(q)(x)] = q

∞∫0

e−qtpt(x)dt

and therefore

pt(x) =1

2πi

∫q0+iR

pM (q, x)eqtdq

q

In our example we have

pt(x) =1

2πi

∫q0+iR

(√µ2 + 2q − µ

)e−(√µ2+2q−µ)x+qt dq

q

W-H factorization and distribution of extrema Alexey Kuznetsov 5/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

Kou model: double exponential jump diffusion model

Xt is a Levy process with jumps defined by

ν(x) = a1e−b1xI{x>0} + a2e

b2xI{x<0}

Then the characteristic exponent is

Ψ(z) =σ2z2

2− iµz − a1

b1 − iz− a2

b2 + iz+a1

b1+a2

b2

Thus equation Ψ(z) + q = 0 is a fourth degree polynomial equation,and we have explicit solutions and exact WH factorization.

W-H factorization and distribution of extrema Alexey Kuznetsov 6/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Well-known examples

Phase-type distributed jumps

DefinitionThe distribution of the first passage time of the finite state continuoustime Markov chain is called phase-type distribution.

q(x) = p0exLe1 =

N∑k=1

aiebix

where bi are eigenvalues of the Markov generator L. Thus if Xt hasphase-type jumps, its characteristic exponent Ψ(z) is a rationalfunction, and Ψ(z) + q = 0 is reduced to a polynomial equation, andthe Wiener-Hopf factors are given in closed form (in terms of roots ofthis polynomial equation).

W-H factorization and distribution of extrema Alexey Kuznetsov 7/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Compound Poisson process

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 7/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Compound Poisson process

Definition

Let Xt be a compound Poisson process defined by

ν(x) =eαx

cosh(x)

The characteristic exponent of Xt is given by

Ψ(z) = −∫R

(eixz − 1

)ν(x)dx =

π

cos(π2α) − π

cosh(π2 (z − iα)

)

W-H factorization and distribution of extrema Alexey Kuznetsov 8/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Compound Poisson process

WH factorization

Theorem

Assume that q > 0. Define

η =2π

arccos

q + π sec(π2α))

p0 =Γ(

14 (1− α)

)Γ(

14 (3− α)

)Γ(

14 (η − α)

)Γ(

14 (4− η − α)

)Then for Im(z) > i(α− η) we have

φ+q (z) = p0

Γ(

14 (η − α− iz)

)Γ(

14 (4− η − α− iz)

)Γ(

14 (1− α− iz)

)Γ(

14 (3− α− iz)

)We have P(Mτ = 0) = p0 and the density of Mτ is given by explicitformula in terms of 2F1(a, b; c; z) – the Gauss hypergeometricfunction.

W-H factorization and distribution of extrema Alexey Kuznetsov 9/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Compound Poisson process

Uniquenes of Wiener-Hopf factorization

Lemma

Assume we have two functions f+(z) and f−(z), such that f±(0) = 1,f±(z) is analytic in C±, continuous and has no roots in C± andz−1 ln(f±(z))→ 0 as z →∞, z ∈ C±. If

q

Ψ(z) + q= f+(z)f−(z), z ∈ R

then f±(z) = φ±q (z).

W-H factorization and distribution of extrema Alexey Kuznetsov 10/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 10/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Definition

Let Xt be defined by a triple (µ, σ, ν), where the jump measure ν(x)is given by

ν(x) =eαx[

sinh(x2 )]2

with | α |< 1.

Proposition

The characteristic exponent of Xt is given by

Ψ(z) =σ2z2

2+ iρz + 4π(z − iα) coth (π(z − iα))− 4γ,

where

γ = πα cot (πα) , ρ = 4π2α+4γ(γ − 1)

α− µ

W-H factorization and distribution of extrema Alexey Kuznetsov 11/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Theorem on zeros of Ψ(z) + q

TheoremAssume that q > 0.(i) Equation Ψ(iζ) + q = 0 has infinitely many solutions, all of which

are real and simple. They are located as follows:

ζ−0 ∈ (α− 1, 0)ζ+0 ∈ (0, α+ 1)ζn ∈ (n+ α, n+ α+ 1), n ≥ 1ζn ∈ (n+ α− 1, n+ α), n ≤ −1

(ii) If σ 6= 0 we have as n→ ±∞

ζn = (n+ α) +8σ2

(n+ α)−1

− 8σ2

(2ρσ2

+ α

)(n+ α)−2 +O(n−3)

W-H factorization and distribution of extrema Alexey Kuznetsov 12/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Theorem on zeros of Ψ(z) + q

Theorem

(iii) If σ = 0 we have as n→ ±∞

ζn = (n+ α+ ω0) + c0(n+ α+ ω0)−1

− c0ρ

(4γ − q − 4π2c0

)(n+ α+ ω0)−2 +O(n−3),

where

c0 = −4(4γ − q + αρ)16π2 + ρ2

, ω0 =1π

arcctg( ρ

)− 1.

(iv) Function q(Ψ(z) + q)−1 can be factorized as follows

q

Ψ(z) + q=

1(1 + iz

ζ+0

)(1 + iz

ζ−0

) ∏|n|≥1

1 + izn+α

1 + izζn

W-H factorization and distribution of extrema Alexey Kuznetsov 13/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Proof

4π(ζ − α) cot(π(ζ − α))− (ρ+ µ)ζ − 4γ =σ2ζ2

2− µζ − q

W-H factorization and distribution of extrema Alexey Kuznetsov 14/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Proof

LemmaAssume that α and β are not equal to a negative integer, andbn = O(n−ε1) for some ε1 > 0 as n→∞. Then

∏n≥0

1 + zn+α

1 + zn+β+bn

≈ Czβ−α,

as z →∞, | arg(z) |< π − ε2 < π, where C = Γ(α)Γ(β)

∏n≥0

(1 + bn

n+β

).

W-H factorization and distribution of extrema Alexey Kuznetsov 15/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Process with jumps of infinite variation

Main Results

TheoremFor q > 0

φ−q (z) =1

1 + izζ+0

∏n≥1

1 + izn+α

1 + izζn

, φ+q (z) =

11 + iz

ζ−0

∏n≤−1

1 + izn+α

1 + izζn

Infinite products converge uniformly on compact subsets of C \ iR.The density of Mτ is given by

pM (q, x) =d

dxP(Mτ < x) = −c−0 ζ

−0 e

ζ−0 x −∑k≥1

c−k ζ−keζ−kx

where

c−0 =∏n≤−1

1− ζ−kn+α

1− ζ−kζn

, c−k =1− ζ−k

−k+α

1− ζ−kζ−0

∏n≤−1, n 6=−k

1− ζ−kn+α

1− ζ−kζn

W-H factorization and distribution of extrema Alexey Kuznetsov 16/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 16/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Definition of the β-family

DefinitionWe define a β-family of Levy processes by the generating triple(µ, σ, ν), where the jump density is

ν(x) = c1e−α1β1x

(1− e−β1x)λ1I{x>0} + c2

eα2β2x

(1− eβ2x)λ2I{x<0}

and parameters satisfy αi > 0, βi > 0, ci ≥ 0 and λi ∈ (0, 3).

W-H factorization and distribution of extrema Alexey Kuznetsov 17/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Levy processes similar to β-family

The generalized tempered stable family

ν(x) = c+

e−α+x

xλ+I{x>0} + c−

eα−x

|x|λ−I{x<0}.

can be obtained as the limit as β → 0+ if we let

c1 = c+βλ+ , c2 = c−β

λ− , α1 = α+β−1, α2 = α−β

−1, β1 = β2 = β

Particular cases:λ1 = λ2 −→ tempered stable, or KoBoL processesc1 = c2, λ1 = λ2 and β1 = β2 −→ CGMY processesci = 4, βi = 1/2, λi = 2, α1 = 1− α and α2 = 1 + α −→ theprocess discussed in previous section

W-H factorization and distribution of extrema Alexey Kuznetsov 18/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Computing characteristic exponent

Proposition

If λi ∈ (0, 3) \ {1, 2} then

Ψ(z) =σ2z2

2+ iρz

− c1β1

B(α1 −

iz

β1; 1− λ1

)− c2β2

B(α2 +

iz

β2; 1− λ2

)+ γ

where constants γ and ρ are expressed in terms of beta and digammafunctions.

W-H factorization and distribution of extrema Alexey Kuznetsov 19/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Theorem on zeros of Ψ(z) + q

TheoremAssume that q > 0.(i) Equation Ψ(iζ) + q = 0 has infinitely many solutions, all of which

are real and simple. They are located as follows:

ζ−0 ∈ (−β1α1, 0)ζ+0 ∈ (0, β2α2)ζn ∈ (β2(α2 + n− 1), β2(α2 + n)), n ≥ 1ζn ∈ (β1(−α1 + n), β1(−α1 + n+ 1)), n ≤ −1

(ii) If σ 6= 0 we have as n→ +∞

ζn = β2(n+ α2) +2c2

σ2β22Γ(λ2)

(n+ α2)λ2−3 +O(nλ2−3−ε)

W-H factorization and distribution of extrema Alexey Kuznetsov 20/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Theorem on zeros of Ψ(z) + q

Theorem

(iii) If σ = 0 we have as n→ +∞

ζn = β2(n+ α2 + ω0) +A(n+ α2 + ω0)λ +O(nλ−ε)

(iv) Function q(Ψ(z) + q)−1 can be factorized as follows

q

Ψ(z) + q=

1(1 + iz

ζ+0

)(1 + iz

ζ−0

∏n≥1

1 + izβ2(n+α2)

1 + izζn

∏n≤−1

1 + izβ1(n−α1)

1 + izζn

W-H factorization and distribution of extrema Alexey Kuznetsov 21/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Proof

The proof of (ii) and (iii) is based on the following two asymptoticformulas as ζ → +∞

B(α+ ζ; γ) = Γ(γ)ζ−γ[1− γ(2α+ γ − 1)

2ζ+O(ζ−2)

]B(α− ζ; γ) = Γ(γ)

sin(π(ζ − α− γ))sin(π(ζ − α))

ζ−γ[1 +

γ(2α+ γ − 1)2ζ

+O(ζ−2)]

If σ 6= 0 and ζ → +∞ we use the above formulas and rewrite equationΨ(iζ) + q = 0 as

sin(π(ζβ2− α2 + λ2

))sin(π(ζβ2− α2

)) =σ2βλ2

2

2c2Γ(1− λ2)ζ3−λ2 +O(ζ1−λ2) +O(ζλ1−λ2)

W-H factorization and distribution of extrema Alexey Kuznetsov 22/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

A β-family of Levy processes

Main results

TheoremFor q > 0

φ−q (z) =1

1 + izζ+0

∏n≥1

1 + izβ2(n+α2)

1 + izζn

, φ+q (z) =

11 + iz

ζ−0

∏n≤−1

1 + izβ1(n−α1)

1 + izζn

Infinite products converge uniformly on compact subsets of C \ iR.The density of Mτ is given by

pM (q, x) =d

dxP(Mτ < x) = −c−0 ζ

−0 e

ζ−0 x −∑k≥1

c−k ζ−keζ−kx

where

c−0 =∏n≤−1

1− ζ−kβ1(n−α1)

1− ζ−kζn

, c−k =1− ζ−k

β1(−k−α1)

1− ζ−kζ−0

∏n≤−1, n 6=−k

1− ζ−kβ1(n−α1)

1− ζ−kζn

W-H factorization and distribution of extrema Alexey Kuznetsov 23/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 23/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Computing density of Mτ

(i) compute ζ±0 , ζn - Newton’s method for large n. For small n –localization, bisection and then Newton’s method.

(ii) compute coefficients c−k – use asymptotic expansion of ζn toaccelerate convergence of products.

(iii) series for density of Mτ is exponentially converging

W-H factorization and distribution of extrema Alexey Kuznetsov 24/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Computing density of Mt

As before, pt(x) can be recovered as the inverse Laplace transform ofpM (q, x)/q. Thus

pt(x) =1

2πi

∫q0+iR

eqtpM (q, x)dq

q=eq0t

π

∞∫0

Re[pM (q0 + iu, x)

q0 + iu

]cos(tu)du

Question: How do we solve Ψ(z) + q = 0 for q ∈ C ?

W-H factorization and distribution of extrema Alexey Kuznetsov 25/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Solving Ψ(z) + q = 0 for q ∈ C: ODE method

We need to compute the solutions of equation Ψ(iζ) + q = 0 for allq ∈ [q0, q0 + iu0]. First we compute the initial values: the roots ζ±0 , ζnfor real value of q = q0 using the method discussed above. Next weconsider each root as an implicit function of u: ζn(u) is defined as

Ψ(iζn(u)) + (q0 + iu) = 0, ζn(0) = ζn

Using implicit differentiation we obtain a first order differentialequation

dζn(u)du

= − 1Ψ′(iζn(u))

W-H factorization and distribution of extrema Alexey Kuznetsov 26/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Solving ODE: Runge-Kutta & Newton’s method

W-H factorization and distribution of extrema Alexey Kuznetsov 27/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Results: surface plot of pM(q, x) and pt(x)

W-H factorization and distribution of extrema Alexey Kuznetsov 28/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Outline

1 IntroductionWiener-Hopf factorizationWell-known examples

2 Processes with meromorphic characteristic exponentCompound Poisson processProcess with jumps of infinite variation (analogue of NIGprocess)A β-family of Levy processes

3 Numerical results

4 Conclusion and future work

W-H factorization and distribution of extrema Alexey Kuznetsov 28/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞

We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)Infinite products can be computed efficiently using convergenceacceleration techniquesTo find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methodsβ-family of Levy processes is very general, many existing modelscan be obtained as a limitFuture work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)

Infinite products can be computed efficiently using convergenceacceleration techniquesTo find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methodsβ-family of Levy processes is very general, many existing modelscan be obtained as a limitFuture work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)Infinite products can be computed efficiently using convergenceacceleration techniques

To find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methodsβ-family of Levy processes is very general, many existing modelscan be obtained as a limitFuture work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)Infinite products can be computed efficiently using convergenceacceleration techniquesTo find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methods

β-family of Levy processes is very general, many existing modelscan be obtained as a limitFuture work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)Infinite products can be computed efficiently using convergenceacceleration techniquesTo find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methodsβ-family of Levy processes is very general, many existing modelscan be obtained as a limit

Future work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29

Introduction Processes with meromorphic Ψ(z) Numerical results Conclusion

Conclusion and future work

Wiener-Hopf factors can be identified explicitly for certain Levyprocesses, if Ψ(z) is meromorphic and if we know its asymptoticsas z →∞We have a lot of information about solutions of Ψ(z) + q = 0(localization, asymptotics)Infinite products can be computed efficiently using convergenceacceleration techniquesTo find solutions of Ψ(z) + q = 0 for complex values of q we canuse ODE/Newton’s methodsβ-family of Levy processes is very general, many existing modelscan be obtained as a limitFuture work: applying these processes to price variousderivatives in Mathematical Finance

W-H factorization and distribution of extrema Alexey Kuznetsov 29/29