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Citation: Tetsuya Takaishi. Volatility estimation using a rational GARCH model[J]. Quantitative Finance and Economics, 2018, 2(1): 127-136. doi: 10.3934/QFE.2018.1.127
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A Reinforced material is a composite building material consisting of two or more materials with different properties. The main objective of studies of reinforced materials is the prediction of their macroscopic behavior from the properties of their individual components as well as from their microstructural characteristics.
The theory of ideal fiber-reinforced composites was initiated by Adkins and Rivlin [4] who studied the deformation of a structure reinforced with thin, flexible and inextensible cords, which lie parallel and close together in smooth surfaces. This theory was further developed by the authors in [44], [1], [2], [3], [45].
The homogenization of elastic materials reinforced with highly contrasted inclusions has been considered by several authors in the two last decades (see for instance [6], [10], [17], [21], [18], and the references therein). The main result is that the materials obtained by the homogenization procedure have new elastic properties.
The homogenization of structures reinforced with fractal inclusions has been considered by various authors, among which [39], [31], [40], [41], [12], [42], [13], and [14]. Lancia, Mosco and Vivaldi studied in [31] the homogenization of transmission problems across highly conductive layers of iterated fractal curves. In [40], Mosco and Vivaldi dealt with the asymptotic behavior of a two-dimensional membrane reinforced with thin polygonal strips of large conductivity surrounding a pre-fractal curve obtained after
The homogenization of insulating fractal surfaces of Koch type approximated by three-dimensional insulating layers has been performed by Capitanelli et al. in [12], [13], and [14]. Due to the physical characteristics of the inclusions, singular energy forms containing fractal energies are obtained in these articles as the limit of non-singular full-dimensional energies. On the other hand, the effective properties of elastic materials fixed on rigid thin self-similar micro-inclusions disposed along two and three dimensional Sierpinski carpet fractals have been recently obtained in [20].
In the present work, we consider the deformation of a three-dimensional elastic material reinforced with highly contrasted thin vertical strips constructed on horizontal iterated Sierpinski gasket curves. Our main purpose is to describe the macroscopic behavior of the composite as the width of the strips tends to zero, their material coefficients tend to infinity, and the sequence of the iterated Sierpinski gasket curves converges to the Sierpinski gasket in the Hausdorff metric.
The asymptotic analysis of problems of this kind was previousely studied in [11], [26], [9], and [5], where the authors considered media comprising low dimensional thin inclusions or thin layers of higher conductivity or higher rigidity. The limit problems consist in second order transmission problems. Problems involving thin highly conductive fractal inclusions have been addressed in a series of papers (see for instance [39], [31], [12], [14], and [19]). The obtained mathematical models are elliptic or parabolic boundary value problems involving transmission conditions of order two on the interfaces. The homogenization of three-dimensional elastic materials reinforced by highly rigid fibers with variable cross-section, which may have fractal geometry, has been carried out in [21]. The authors showed that the geometrical changes induced by the oscillations along the fiber-cross-sections can provide jumps of displacement fields or stress fields on interfaces, including fractal ones, due to local concentrations of elastic rigidities. Note that the numerical approximation of second order transmission problems across iterated fractal interfaces has been considered in some few papers among which [32] and [15].
Let us first consider the points
Vh+1=Vh∪(2−hA2+Vh)∪(2−hA3+Vh). | (1) |
Let us set
V∞=∪h∈NVh. | (2) |
The Sierpinski gasket, which is denoted here by
Σ=¯V∞. | (3) |
We define the graph
The edges which belong to
Let
Σ∩∂ω=V0. | (4) |
Let
limh→∞εh2h=0. | (5) |
We define
Tkh=(ω∩Skh)×(−εh,εh) | (6) |
and set
Th=∪k∈IhT,kh, | (7) |
where
|Th|=εh3h+12h. | (8) |
Let
σij(u)=λemm(u)δij+2μeij(u) ; i,j=1,2,3, | (9) |
where the summation convention with respect to repeated indices has been used and will be used in the sequel, and
σhij(u)=λhemm(u)δij+2μheij(u) ; i,j=1,2,3, |
with
λh=ηhλ0 and μh=ηhμ0, | (10) |
where
ηh=1εh(56)h. | (11) |
The special scaling (10) and (11) of the Lamé -coefficients depend on the structural constants of
We suppose that a perfect adhesion occurs between
Fh(u)={∫Ω∖Thσij(u)eij(u)dx+∫Thσhij(u)eij(u)dsdx3 if u∈H10(Ω,R3)∩H1(Th,R3),+∞ otherwise, | (12) |
where
minu∈L2(Ω,R3)∩L2(Th,R3){Fh(u)−2∫Ωf.udx}. | (13) |
We use
γ=limh→∞(−3h+12hlnεh), | (14) |
which is associated with the size of the boundary layers taking place in the neighbourhoods of the fractal strips, we prove that if
F∞(u,v)={∫Ωσij(u)eij(u)dx+μ0∫ΣdLΣ(¯v)+πμγHd(Σ)(ln2)2∫ΣA(s)(u−v).(u−v)dHd(s) if (u,v)∈H10(Ω,R3)×DΣ,E×L2Hd(Σ),+∞ otherwise, | (15) |
where
d=ln3/ln2, | (16) |
A(s)={Diag(1,2(1+κ),2(1+κ)) if n(s)=±(0,1),(7+κ4(1+κ)√3(κ−1)4(1+κ)0√3(κ−1)4(1+κ)3κ+54(1+κ)0002(1+κ))if n(s)=±(−√32,12),(7+κ4(1+κ)√3(1−κ)4(1+κ)0√3(1−κ)4(1+κ)3κ+54(1+κ)0002(1+κ))if n(s)=±(√32,12), | (17) |
where
The effective energy (15) contains new degrees of freedom implying nonlocal effects associated with thin boundary layer phenomena taking place near the fractal strips and a singular energy term supported on the Sierpinski gasket
{ [σα3|x3=0]Σ=πμγHd(Σ)(ln2)2Aαβ(s)(Uβ−Vβ)Hd on Σ,πμγ(ln2)2Aαβ(s)(Uβ−Vβ)=−μ0ΔΣVα in Σ; α,β=1,2, | (18) |
where
[σα3|x3=0]Σ=σα3|Σ×{0+}−σα3|Σ×{0−}; α=1,2, | (19) |
is the jump of
If
F∞(u)={∫Ωσij(u)eij(u)dx+μ0∫ΣdLΣ(¯u) if u∈H10(Ω,R3)∩(DΣ,E×L2Hd(Σ)), +∞ otherwise. | (20) |
If
F0(u)={∫Ωσij(u)eij(u)dx if u∈H10(Ω,R3), +∞ otherwise. | (21) |
The paper is organized as follows: in Section 2 we introduce the energy form and the notion of a measure-valued local energy on the Sierpinski gasket
In this Section we introduce the energy form and the notion of a measure-valued local energy (or Lagrangian) on the Sierpinski gasket. For the definition and properties of Dirichlet forms and measure energies we refer to [24], [35], and [37].
For any function
EhΣ(w)=(53)h∑p,q∈Vh|p−q|=2−h|w(p)−w(q)|2. | (22) |
Let us define the energy
EΣ(z)=limh→∞EhΣ(z), | (23) |
with domain
D={z∈C(Σ,R2):EΣ(z)<∞}, | (24) |
where
DE=¯D‖.‖DE, | (25) |
where
‖z‖DE={EΣ(z)+‖z‖2L2Hd(Σ,R2)}1/2. | (26) |
The space
DΣ,E={z∈DE:z(A1)=z(A2)=z(A3)=0}. | (27) |
We denote
EΣ(w,z)=12(EΣ(w+z)−EΣ(w)−EΣ(z)), ∀w,z∈DΣ,E. | (28) |
One can see that
EΣ(w,z)=limh→∞EhΣ(w,z), | (29) |
where
EhΣ(w,z)=(53)h∑p,q∈Vh|p−q|=2−h(w(p)−w(q)).(z(p)−z(q)). | (30) |
The form
1. (local property)
2. (regularity)
The second property implies that
Now, applying [29,Chap. 6], we have the following result:
Lemma 2.1. There exists a unique self-adjoint operator
DΔΣ={w=(w1w2)∈L2Hd(Σ,R2):ΔΣw=(ΔΣw1ΔΣw2)∈L2Hd(Σ,R2)}⊂DΣ,E |
dense in
EΣ(w,z)=−∫Σ(ΔΣw).zdHdHd(Σ). |
Let us consider the sequence
νh=1Card(Vh)∑p∈Vhδp, | (31) |
where
Lemma 2.2. The sequence
ν=1Σ(s)dHd(s)Hd(Σ), |
where
Proof. Let
limh→∞∫Σφ(x)dνh=limh→∞∑p∈Vhφ(p)Card(Vh)=1Hd(Σ)∫Σφ(s,0)dHd(s). |
We note that the approximating form
EhΣ(w,z)=∫Σ∇hw.∇hz dνh, | (32) |
where
∇hw.∇hz(p)=∑q: |p−q|=2−h(w(p)−w(q))|p−q|ϰ/2.(z(p)−z(q))|p−q|ϰ/2, |
where
Proposition 1. For every
LhΣ(w,z)(A)=∫A∩Σ∇hw.∇hzdνh,∀A⊂Σ, |
weakly converges in the topological dual
EΣ(w,z)=∫ΣdLΣ(w,z),∀w,z∈DΣ,E. |
Proof. The proof follows the lines of the proof of [23,Proposition 2.3] for the von Koch snowflake. Let us set, for every
∫ΣφdLhΣ(w)=EhΣ(φw,w)−12EhΣ(φe1,|w|2e1), | (33) |
we deduce, taking into account the regularity of the form
limh→∞∫ΣφdLhΣ(w)=EΣ(φw,w)−12EΣ(φe1,|w|2e1). | (34) |
On the other hand, according to [33,Proposition 1.4.1], the energy form
EΣ(w)=∫ΣdLΣ(w), | (35) |
where
∫ΣφdLΣ(w)=EΣ(φw,w)−12EΣ(φe1,|w|2e1), ∀φ∈DΣ,E∩C(Σ). |
Thus, combining with (34), the sequence
LhΣ(w,z)=12(LhΣ(w+z)−LhΣ(w)−LhΣ(z)), |
we deduce that the sequence
In this Section we establish the compactness results which is very useful for the proof of the main homogenization result.
Lemma 3.1. For every sequence
1.
2.
Proof. 1. Observing that
Fh(uh)≥∫Ωσij(uh)eij(uh)dx, |
we have, using Korn's inequality (see for instance [43]), that
suph∫Ω|∇uh|2dx<+∞. | (36) |
2. Let
(ss⊥)=(1/2√3/2−√3/21/2)(x1x2) if Skh⊥(−√3/2,1/2), | (37) |
by
(ss⊥)=(−1/2√3/2√3/21/2)(x1x2) if Skh⊥(√3/2,1/2), | (38) |
and by
(ss⊥)=(1001)(x1x2) if Skh⊥(0,1), | (39) |
where the symbol
ukh(x1(s),x2(s),r,θ)=uh(s,rsinθ+sk2h,rcosθ). | (40) |
Then, according to (36), we have, for
∑k∈Ih∫Skh∫r2r1|∂ukh(x1(s),x2(s),r,θ)∂r|2rdrds≤C. | (41) |
Solving the Euler equation of the following one dimensional minimization problem:
min{∫r2r1(ψ′)2rdr: ψ(r1)=0, ψ(r2)=1}, |
we deduce that, for every
lnr2r1∫r2r1|∂ukh(x1(s),x2(s),r,θ)∂r|2rdr ≥|ukh(x1(s),x2(s),r2,θ)−ukh(x1(s),x2(s),r1,θ)|2. | (42) |
Then, using (41) and (42), we obtain that
∑k∈Ih∫Skh∫2π0|ukh(x1(s),x2(s),r2,θ)−ukh(x1(s),x2(s),r1,θ)|2dθds≤Clnr2r1. | (43) |
Let us define
ϝ(r,θ)=∑k∈Ih∫Skh|ukh(x1(s),x2(s),r,θ)|2ds. | (44) |
We deduce from the inequality (43) that, for
∫2π0ϝ(r1,θ)dθ≤C(∫2π0ϝ(r2,θ)dθ+lnr2r1). | (45) |
Observing that, for
|ukh(x1(s),x2(s),r,θ)−ukh(x1(s),x2(s),r,θ0)|2=|∫θθ0∂ukh∂θ(x1(s),x2(s),r,ϕ)dϕ|2≤Cr∫2π0|1r∂ukh∂θ(x1(s),x2(s),r,ϕ)|2rdϕ, |
we deduce that
∑k∈Ih∫Skh∫2π0∫εh0|ukh(x′(s),r,θ)−ukh(x′(s),r,θ0)|2drdθds ≤ Cεh∑k∈Ih∫Ckh|∇uh|2dx1dx2dx3≤Cεh∫Ω|∇uh|2dx, | (46) |
where
∫εh0ϝ(ρ,θ0)drdθ≤C{∫2π0∫εh0ϝ(r,θ)drdθ+εh}. | (47) |
Now, using (45) and (47), we deduce, by setting
mh∫Th|uh|2dsdx3=mh∫εh0ϝ(r,0)dr+mh∫εh0ϝ(r,π)dr≤Cmh(∫2π0∫εh0ϝ(r,θ)drdθ+εh)≤C(mh∫εh0(∫2π0ϝ(r2,θ)dθ+lnr2r)dr+2h3h+1)≤C2h3h+1(∫2π0ϝ(r2,θ)dθ+lnr2εh+1)≤C((23)h/2r2∫2π0ϝ(r2,θ)dθ+2h3h+1(−lnεh+1)). |
Integrating with respect to
1|Th|∫Th|uh|2dsdx3≤C(∫bhah∫2π0ϝ(r,θ)rdrdθ−2h3h+1lnεh)≤C{‖uh‖2L2(Ω,R3)−2h3h+1lnεh}. |
Let
Lemma 3.2. Let
suph1|Th|∫Th|uh|2dsdx3<+∞. |
Then, there exists a subsequence of
uh1Th(x)|Th|dsdx3∗⇀h→∞v1Σ(s)dHd(s)⊗δ0(x3)Hd(Σ)inM(R3), |
with
Proof. Let us consider the sequence of Radon measures
ϑh=1Th(x)|Th|dsdx3. |
Let
limh→∞∫R3φ(x)dϑh=limh→∞∑k∈Ih23h+1φ(xkh,0)=limh→∞∑k∈Ih1Nhφ(xkh,0)=1Hd(Σ)∫Σφ(s,0)dHd(s), |
from which we deduce that
ϑ=1Σ(s)dHd(s)⊗δ0(x3)Hd(Σ). |
Let
suph1|Th|∫Th|uh|2dsdx3<+∞. |
As
|∫R3uhdϑh|2≤∫R3|uh|2dϑh=1|Th|∫Th|uh|2dsdx3, |
from which we deduce that the sequence
liminfh→∞12∫R3|uh|2dϑh≥liminfh→∞(∫R3uh.φdϑh−12∫R3|φ|2dϑh)≥⟨χ,φ⟩−12∫R3|φ|2dϑ. |
As the left hand side of this inequality is bounded, we deduce that
sup{⟨χ,φ⟩; φ∈C0(R3,R3), ∫Σ|φ|2(s,0)dHd(s)≤1}<+∞, |
from which we deduce, according to Riesz' representation theorem, that there exists
Proposition 2. Let
1.
2. If
uh1Th(x)|Th|dsdx3∗⇀h→∞v(s,0)1Σ(s)dHd(s)Hd(Σ), |
with
3. If
liminfh→∞∫Thσhij(uh)eij(uh)dsdx3≥μ0EΣ(¯v). |
Proof. 1. Thanks to Lemma 3.1
2. If
uh1Th(x)|Th|dsdx3∗⇀h→∞v(s,0)1Σ(s)dHd(s)Hd(Σ), |
with
3. One can easily check that
∫Thσhij(uh)eij(uh)dsdx3 ≥2μh(∫Th((e11(uh))2+2(e12(uh))2+(e22(uh))2)dsdx3). | (48) |
Computing the strain tensor in the local coordinates (37) and (38), observing that for
∫Skh((e11(uh))2+2(e12(uh))2+(e22(uh))2)ds=∫Skh(14(∂uk1,h∂s)2+38(∂uk2,h∂s)2)ds≥14∫Skh((∂uk1,h∂s)2+(∂uk2,h∂s)2)ds. | (49) |
For
∫Skh((e11(uh))2+2(e12(uh))2+(e22(uh))2)ds=∫Skh(∂uk1,h∂x1)2+12(∂uk2,h∂x1)2ds≥14∫Skh((∂uk1,h∂x1)2+(∂uk2,h∂x1)2)ds. | (50) |
According to (48) and (10), we deduce from (49) and (50) that
∫Thσhij(uh)eij(uh)dsdx3≥μh2∫Th(∂uk1,h∂s)2+(∂uk2,h∂s)2dsdx3 ≥2hεhμh∑k∈Ih12εh∫εh−εh(uα,h(pk,x3)−uα,h(qk,x3))2dx3 =2hεhηhμ0∑k∈Ih12εh∫εh−εh(uα,h(pk,x3)−uα,h(qk,x3))2dx3 ≥μ0(53)h∑p,q∈Vh|p−q|=2−h(12εh∫εh−εh(uα,h(p,x3)−uα,h(q,x3))dx3)2. | (51) |
Let us set
We define the function
min{Eh+1Σ(w); w:Vh+1⟶R2, w=˜¯uh on Vh}. | (52) |
Then
Hm˜¯uh=Hm(Hm−1(...(Hh+1˜¯uh))). |
For every
EmΣ(Hm˜¯uh)=EhΣ(˜¯uh). | (53) |
Now we define, for a fixed
H˜¯uh(p)=Hm˜¯uh(p). | (54) |
As
suphEΣ(H˜¯uh)=suphEhΣ(˜¯uh)<+∞, | (55) |
from which we deduce, using Section
EΣ(¯u∗)≤ liminfh→∞EΣ(H˜¯uh)≤ liminfh→∞EhΣ(˜¯uh). | (56) |
On the other hand, using Lemma 3.2, we have, for every
limh→∞1Hd(Σ)∫ΣH˜¯uh.φdHd(s)=limh→∞∫R3¯uh.φdυh=1Hd(Σ)∫Σ¯v(s,0).φdHd(s) , |
which implies that
liminfh→∞ ∫Thσhij(uh)eij(uh)dsdx3≥μ0EΣ(¯v). |
In this Section we state the main result of this work. According to Proposition 2 we introduce the following topology
Definition 4.1. We say that a sequence
{uh⇀h→∞u in H1(Ω,R3)-weak,uh1Th(x)|Th|dsdx3∗⇀h→∞v1Σ(s)dHd(s)⊗δ0(x3)Hd(Σ) in M(R3), |
with
Our main result in this work reads as follows:
Theorem 4.2. If
1. (
limsuph→∞Fh(uh)≤F∞(u,v), |
where
2. (
liminfh→∞Fh(uh)≥F∞(u,v). |
Before proving this Theorem, let us write the homogenized problem obtained at the limit as
Corollary 1. Problem (13) admits a unique solution
{−σij,j(U)=fiinΩ,−μ0Δα,Σ(Vα)=πμγ(ln2)2Aαβ(s)(Uβ−Vβ);α,β=1,2,inΣ,[σα3|x3=0]Σ=πμHd(Σ)(ln2)2Aαβ(s)(Uβ−Vβ)HdonΣ,U3=V3onΣ,U=0on∂Ω,Vα=0;α=1,2,onV0. | (57) |
Proof. One can easily check that problem (13) has a unique solution
Fh(Uh)−2∫Ωf.Uhdx≤Fh(0)=0, |
we deduce, using the fact that
∫Ω|∇Uh|2dx≤∫Ωσij(Uh)eij(Uh)dx+∫Thσhij(Uh)eij(Uh)dsdx3≤2‖f‖L2(Ω,R3)‖Uh‖L2(Ω,R3)≤C‖∇Uh‖L2(Ω,R9), |
from which we deduce that
min(ξ,ζ)∈V{∫Ωσij(ξ)eij(ξ)dx+μ0∫ΣdLΣ(ζ)+πμγHd(Σ)(ln2)2∫ΣA(s)(ξ−ζ).(ξ−ζ)dHd(s)−2∫Ωf.ξdx }, | (58) |
where
∫Σ|ψ(x)|2dHd(x)+∫Σ∫Σ|x−y|<1|ψ(x)−ψ(y)|2|x−y|2ddHd(x)dHd(y)<+∞. | (59) |
Then, according to Lemma 2.1, we obtain from (58), using for example [46,Theorems 3.1 and 3.3], that
∫Ω(−σij,j(U)−fi)ξidx−μ0Hd(Σ)∫Σ(Δα,Σ¯V)ζαdHd(s)+πμγHd(Σ)(ln2)2∫ΣA(s)(U−V).(ξ−ζ)dHd(s)−⟨[σi3|x3=0]Σ,ξi⟩B2−d(Σ,R3),B2d(Σ,R3)=0, | (60) |
−μ0Δα,Σ(Vα)=πμγ(ln2)2Aαβ(s)(Uβ−Vβ); α,β=1,2, in Σ, |
in problem (57) is well posed.
The proof of Theorem 4.2 is given in three steps.
We consider here a local problem associated with boundary layers in the vicinity of the strips. We denote
{σij,j(wm)(y)=0∀y∈R2+; i=1,2,wm(y1,0)=em∀y1∈]−1,1[,σi2(wm)(y1,0)=0∀y1∈R∖]−1,1[,wmm(y)=−ln|y|ln2as |y|→∞, y2>0 ,|wmp|(y)≤Cfor {p=2 if m=1,p=1 if m=2, | (61) |
where
w11(y)=14πμ∫1−1ξ(t)(−(1+κ)ln(√(y1−t)2+(y2)2)+2(y2)2(y1−t)2+(y2)2)dt,w12(y)=14πμ∫1−1ξ(t)(−(1−κ)arctan(y2y1−t)+2y2(y1−t)(y1−t)2+(y2)2)dt | (62) |
and
w21(y)=14πμ∫1−1ξ(t)((1−κ)arctan(y2y1−t)+2y2(y1−t)(y1−t)2+(y2)2)dt,w22(y)=14πμ∫1−1ξ(t)(−(1+κ)ln(√(y1−t)2+(y2)2)−2(y2)2(y1−t)2+(y2)2)dt, | (63) |
where
ξ(t)={4μ(1+κ)ln21√1−t2if t∈]−1,1[,0otherwise. | (64) |
One can check that
R2−={y=(y1,y2)∈R2 ; y2<0}. |
We introduce the scalar problem
{Δw(y)=0∀y∈R2+; i=1,2,w(y1,0)=1∀y1∈]−1,1[,∂w∂y2(y1,0)=0∀y1∈R∖]−1,1[,w(y)=−ln|y|ln2 as |y|→∞, y2>0. | (65) |
The solution of (65) is given by
w(y)=−1πln2∫1−1ln(√(y1−t)2+(y2)2)√1−t2dt. | (66) |
Observe that
Proposition 3. ([18,Proposition 7]). One has
1.
2.
Let
limh→∞2hrh=limh→∞εhrh=0. | (67) |
We define the rotation
R(xkh)={IdR3if nk=±(0,1),(1/2√3/20−√3/21/20001)if nk=±(−√3/2,1/2),(−1/2√3/20√3/21/20001)if nk=±(√3/2,1/2), | (68) |
where
φkh(x)={4(r2h−R2k,h(x))3r2hif rh/2≤Rkh(x)≤rh,1if Rkh(x)≤rh/2,0if Rkh(x)≥rh, | (69) |
where
Dkh(rh)={((x−xkh).nk,x3)∈R2; Rkh(x)<rh, ∀x∈R3} | (70) |
and the cylinder
Zkh=R(xkh)Skh×Dkh(rh). | (71) |
We then set
Zh=⋃k∈IhZkh. | (72) |
We define, the function
w1kh(x)=φkh(x)R(xkh)(e1−1lnεh(1−w(x3εh,(x′−xkh).nkεh)00)), | (73) |
w2kh(x)=φkh(x)R(xkh)(e2−1lnεh(01−w11(x3εh,(x′−xkh).nkεh)w12(x3εh,(x′−xkh).nkεh))) | (74) |
and
w3kh(x)=φkh(x)R(xkh)(e3−1lnεh(0w21(x3εh,(x′−xkh).nkεh)1−w22(x3εh,(x′−xkh).nkεh))), | (75) |
where
wmh(x)=wmkh(x), ∀k∈Ih, ∀x∈Ω. | (76) |
We have the following result:
Lemma 5.1. If
limh→∞∫Zhσij(wmhΦm)eij(wlhΦl)dx=πμγHd(Σ)(ln2)2∫ΣA(s)Φ(s).Φ(s)dHd(s), |
where
Proof. Let us introduce the change of variables
{y1=x3εh,y2=(x′−xkh).nkεh, |
on
limh→∞∫Zhσij(wmhΦm)eij(wlhΦl)dx=limh→∞∑k∈Ih∫Zkhσij(wmkh)eij(wlkh)ΦmΦldx=limh→∞3h+12hln2εh∫D(0,rhεh)∖D(0,1)σij(wm)eij(wl)dy1dy2×(∑k∈Ih1Nh(R(xkh)Φ)m(R(xkh)Φ)l(xk1h,xk2h,0))=πμγHd(Σ)ln2∫Σ(BR(s)Φ(s))m(R(s)Φ(s))ldHd(s)=πμγHd(Σ)(ln2)2∫ΣRt(s)BR(s)Φ(s).Φ(s)dHd(s), |
where
Rt(s)BR(s)=R(s)BR(s)=A(s), |
we have the result.
In this Subsection we prove the lim-sup condition of the
vk1,h(x′)=v1(xk1h,xk2h)+2hζ1,kh(x′)|v1(pkh)−v1(qkh)|,vk2,h(x′)=v2(xk1h,xk2h)+2hζ2,kh(x′)|v2(pkh)−v2(qkh)|,vk3,h(x′)=v3(xk1h,xk2h), | (77) |
for every
{ζ1,kh(x′)=2√μhs+pkh,1/2−pkh,2√3/2√λh+2μh,ζ2,kh(x′)=2(s+pkh,1/2−pkh,2√3/2)√3, | (78) |
using the local coordinates (38) for
{ζ1,kh(x′)=√2√μhs−pkh,1/2−pkh,2√3/2√λh+2μh,ζ2,kh(x′)=√2(s−pkh,1/2−pkh,2√3/2)√3, |
and, using the local coordinates (39) for
{ζ1,kh(x′)=√μh2(x1−pkh,1)√λh+2μh ,ζ2,kh(x′)=(x1−pkh,1)√2. |
Let us now introduce the intervals
\begin{equation} S_{h}^{k}\cap J_{h}^{p_{h}^{k}} = \left[ p_{h}^{k},p_{h}^{k}+{\bf{s}} _{h}\right) \text{, }S_{h}^{k}\cap J_{h}^{q_{h}^{k}} = \left( q_{h}^{k}- {\bf{s}}_{h},q_{h}^{k}\right] \text{,} \end{equation} | (79) |
where
\begin{equation} \psi _{h}^{k} = \left\{ \begin{array}{ll} 1 & \text{on }\ S_{h}^{k}\backslash J_{h}^{p_{h}^{k}}\cup J_{h}^{q_{h}^{k}} \text{,} \\ 0 & \text{on }J_{h}^{p_{h}^{k}}\cup J_{h}^{q_{h}^{k}}\backslash \left( \left( p_{h}^{k},p_{h}^{k}+{\bf{s}}_{h}\right) \cup \left( q_{h}^{k}- {\bf{s}}_{h},q_{h}^{k}\right) \right) \text{.} \end{array} \right. \end{equation} | (80) |
We define the test-function
\begin{equation} v_{h} = \psi _{h}^{k}v_{h}^{k}\text{, }\forall k\in I_{h}\text{.} \end{equation} | (81) |
We have the following convergences:
Lemma 5.2. We have
1.
2.
Proof. 1. Let
\begin{equation*} \left. \begin{array}{r} \underset{h\rightarrow \infty }{\lim }\int_{\mathbb{R}^{3}}\varphi \left( x\right) .v_{h}\left( x^{\prime }\right) \dfrac{\boldsymbol{1}_{T_{h}}\left( x\right) }{\left\vert T_{h}\right\vert }dsdx_{3}\mathcal{ = }\underset{ h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum\limits }\dfrac{2v\left( x_{1h}^{k},x_{2h}^{k}\right) }{3^{h+1}}.\varphi \left( x_{1h}^{k},x_{2h}^{k},0\right) \\ +C\underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\underset{ \underset{i = 1,2,3}{\alpha = 1,2}}{\sum\limits }}\dfrac{2}{3^{h+1}}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \varphi _{i}\left( x_{1h}^{k},x_{2h}^{k},0\right) \text{,} \end{array} \right. \end{equation*} |
where
\begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\sum \limits}\dfrac{2}{ 3^{h+1}}v\left( x_{1h}^{k},x_{2h}^{k}\right) .\varphi \left( x_{1h}^{k},x_{2h}^{k},0\right) & = & \underset{h\rightarrow \infty }{\lim } \underset{k\in I_{h}}{\sum \limits}\dfrac{1}{N_{h}}v\left( x_{h}^{k}\right) .\varphi \left( x_{h}^{k},0\right) \\ & = & \dfrac{1}{\mathcal{H}^{d}\left( \Sigma \right) }\int_{\Sigma }v\left( s\right) .\varphi \left( s,0\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \end{equation*} |
On the other hand, since
\begin{equation*} \left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \leq C\left\vert p_{h}^{k}-q_{h}^{k}\right\vert \end{equation*} |
and
\begin{equation*} \underset{h\rightarrow \infty }{\lim }\underset{k\in I_{h}}{\underset{ \underset{i = 1,2,3}{\alpha = 1,2}}{\sum \limits}}\dfrac{2}{3^{h+1}}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert \varphi _{i}\left( x_{1h}^{k},x_{2h}^{k},0\right) = 0\text{.} \end{equation*} |
2. Computing tensors in local coordinates (37) and (38), we obtain, for
\begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \dfrac{\left( \lambda _{h}+2\mu _{h}\right) }{4}\left( \dfrac{\partial v_{1,h}^{k}}{\partial s}\right) ^{2}+\dfrac{3\mu _{h}}{4}\left( \dfrac{ \partial v_{2,h}^{k}}{\partial s}\right) ^{2}\text{,} \end{equation*} |
and if
\begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \left( \lambda _{h}+2\mu _{h}\right) \left( \dfrac{\partial v_{1,h}^{k}}{ \partial x_{1}}\right) ^{2}+\mu _{h}\left( \dfrac{\partial v_{2,h}^{k}}{ \partial x_{1}}\right) ^{2}\text{.} \end{equation*} |
Thus, according to (77)-(78), we obtain on each
\begin{equation*} \sigma _{ij}^{h}\left( v_{h}^{k}\right) e_{ij}\left( v_{h}^{k}\right) = \mu _{h}2^{2h}\left\{ \left\vert v_{1}\left( p_{h}^{k}\right) -v_{1}\left( q_{h}^{k}\right) \right\vert ^{2}+\left\vert v_{2}\left( p_{h}^{k}\right) -v_{2}\left( q_{h}^{k}\right) \right\vert ^{2}\right\} \text{,} \end{equation*} |
which implies that
\begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int_{T_{h}}\sigma _{ij}^{h}\left( v_{h}\right) e_{ij}\left( v_{h}\right) dsdx_{3} \\ \mathcal{ = \mu }_{0}\underset{h\rightarrow \infty }{\lim }\eta _{h}\underset{ k\in I_{h},\alpha = 1,2}{\sum \limits}\varepsilon _{h}2^{h}\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert ^{2} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{k\in I_{h},\alpha = 1,2}{\sum\limits }\left\vert v_{\alpha }\left( p_{h}^{k}\right) -v_{\alpha }\left( q_{h}^{k}\right) \right\vert ^{2} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{\underset{\left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h}}}{\underset{\alpha = 1,2}{\sum\limits }}\left\vert v_{\alpha }\left( p\right) -v_{\alpha }\left( q\right) \right\vert ^{2}\text{.} \end{array} \right. \end{equation*} |
We prove here the lim-sup condition of the
Proposition 4. If
\begin{equation*} \underset{h\rightarrow \infty }{\lim \sup }F_{h}\left( u_{h}\right) \leq F_{\infty }\left( u,v\right) \mathit{\text{.}} \end{equation*} |
Proof. Let
\begin{equation} u_{n,h}^{0} = u_{n}-w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \text{,} \end{equation} | (82) |
where
We have
\begin{equation} \left. \begin{array}{l} F_{h}\left( u_{n,h}^{0}\right) = \int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ \ \ }+\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx+\int\nolimits_{T_{h}}\sigma _{ij}^{h}\left( v_{n,h}\right) e_{ij}\left( v_{n,h}\right) dsdx_{3}\text{.} \end{array} \right. \end{equation} | (83) |
We immediately obtain
\begin{equation*} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx = \int\nolimits_{\Omega }\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{n}\right) dx\text{.} \end{equation*} |
Using Lemma 5.1, it follows that
\begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ = \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \right) e_{ij}\left( w_{h}^{m}\left( \left( u_{n}\right) _{m}-\left( v_{n,h}\right) _{m}\right) \right) \\ = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \end{array} \right. \end{equation} | (84) |
and, using Lemma 5.2 and Proposition 1, we obtain
\begin{equation*} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{T_{h}}\sigma _{ij}^{h}\left( v_{n,h}\right) e_{ij}\left( v_{n,h}\right) dsdx_{3} \\ = \mathcal{\mu }_{0}\underset{h\rightarrow \infty }{\lim }\left( \dfrac{5}{3} \right) ^{h}\underset{\underset{\left\vert p-q\right\vert = 2^{-h}}{p,q\in \mathcal{V}_{h}}}{\underset{\alpha = 1,2}{\sum\limits }}\left\vert v_{\alpha ,n}\left( p,0\right) -v_{\alpha ,n}\left( q,0\right) \right\vert ^{2} \\ = \mu _{0}\mathcal{E}_{\Sigma }\left( \overline{v}_{n}\right) \\ = \mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}_{n}\right) \text{.} \end{array} \right. \end{equation*} |
This yields
\begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }F_{h}\left( u_{n,h}^{0}\right) = \int\nolimits_{\Omega }\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{n}\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}_{n}\right) \\ { \ \ \ \ \ \ \ \ \ \ \ }+\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2}\int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \\ { \ \ \ \ \ \ \ \ \ \ \ } = F_{\infty }\left( u_{n},v_{n}\right) \text{.} \end{array} \right. \end{equation} | (85) |
The continuity of
\begin{equation*} \underset{h\rightarrow \infty }{\lim \sup }F_{h}\left( u_{h}\right) \leq F_{\infty }\left( u,v\right) . \end{equation*} |
In this Subsection we prove the second assertion of Theorem 4.2.
Proposition 5. If
\begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) \geq F_{\infty }\left( u,v\right) \mathit{\text{.}} \end{equation*} |
Proof. Let
\begin{equation} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }\text{ }\int_{T_{h}}\sigma _{ij}^{h}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dsdx_{3} & \geq & \mu _{0}\mathcal{E}_{\Sigma }\left( \overline{v}\right) \\ & = & \mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v} \right) \text{.} \end{array} \end{equation} | (86) |
Let
\begin{equation*} u_{n}\underset{n\rightarrow \infty }{\longrightarrow }u\ H^{1}\left( \Omega , \mathbb{R}^{3}\right) -\text{strong,} \end{equation*} |
\begin{equation} \left. \begin{array}{c} \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dx\geq \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }+2\int\nolimits_{Z_{h}} \sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx\text{.} \end{array} \right. \end{equation} | (87) |
Due to the structure of the sequence
\begin{equation} \left. \begin{array}{r} \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx = \int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx \\ -\int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx\text{ .} \end{array} \right. \end{equation} | (88) |
Since
\begin{equation} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n}\right) e_{ij}\left( u_{h}-u_{n,h}^{0}\right) dx = 0\text{.} \end{equation} | (89) |
Using the definition of the perturbation
\begin{equation} \begin{array}{l} \left\vert \int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx\right\vert \\ \quad \leq C_{n}^{m}\left( \int\nolimits_{Z_{h}}\left\vert \left( u_{h}-u_{n,h}^{0}\right) \right\vert ^{2}dx\right) ^{1/2}\left( 1+\left( \int\nolimits_{Z_{h}}\left\vert \nabla w_{h}^{m}\left( x\right) \right\vert ^{2}dx\right) ^{1/2}\right) \text{,} \end{array} \end{equation} | (90) |
where
\begin{equation} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij,j}\left( w_{h}^{m}\left( u_{n}-v_{n,h}\right) _{m}\right) \left( u_{h}-u_{n,h}^{0}\right) _{i}dx = 0\text{.} \end{equation} | (91) |
We deduce from (84) that
\begin{equation} \left. \begin{array}{l} \underset{h\rightarrow \infty }{\lim }\int\nolimits_{Z_{h}}\sigma _{ij}\left( u_{n,h}^{0}\right) e_{ij}\left( u_{n,h}^{0}\right) dx \\ { \ \ } = \dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2}\int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \right. \end{equation} | (92) |
On the other hand, as
\begin{equation} \underset{h\rightarrow \infty }{\lim \inf }\int\nolimits_{\Omega \backslash Z_{h}}\sigma _{ij}\left( u_{h}\right) e_{ij}\left( u_{h}\right) dx\geq \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx \text{.} \end{equation} | (93) |
We deduce from (86)-(93) that
\begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) & \geq & \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}\right) \\ & & +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u_{n}-v_{n}\right) .\left( u_{n}-v_{n}\right) d\mathcal{H}^{d}\left( s\right) \text{.} \end{array} \end{equation*} |
Letting
\begin{equation*} \begin{array}{lll} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) & \geq & \int\nolimits_{\Omega }\sigma _{ij}\left( u\right) e_{ij}\left( u\right) dx+\mu _{0}\int_{\Sigma }d\mathcal{L}_{\Sigma }\left( \overline{v}\right) \\ & & +\dfrac{\pi \mu \gamma }{\mathcal{H}^{d}\left( \Sigma \right) \ln 2} \int\nolimits_{\Sigma }A\left( s\right) \left( u-v\right) .\left( u-v\right) d\mathcal{H}^{d}\left( s\right) \text{,} \end{array} \end{equation*} |
which is equivalent to
\begin{equation*} \underset{h\rightarrow \infty }{\lim \inf }F_{h}\left( u_{h}\right) \geq F_{\infty }\left( u,v\right) \text{.} \end{equation*} |
This ends the proof of Theorem 4.2.
[1] | Alderweireld T, Nuyts J (2004) Detailed empirical study of the term structure of interest rates. Emergence of power laws and scaling laws. Physica A 331: 602–616. |
[2] |
Andersen TG, Bollerslev T (1998) Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Int Econ Rev 39: 885–905. doi: 10.2307/2527343
![]() |
[3] |
Aasi M (2006) Comparison of MCMC methods for estimating GARCH models. J Japan Stat Society 36: 199–212. doi: 10.14490/jjss.36.199
![]() |
[4] |
Barndorff-Nielsen OE, Shephard N (2001) Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. J Roy Stat Soc B 63: 167–241. doi: 10.1111/1467-9868.00282
![]() |
[5] |
Bollerslev T (1986) Generalized Autoregressive Conditional Heteroskedasticity. J Econometrics 31: 307–327. doi: 10.1016/0304-4076(86)90063-1
![]() |
[6] | Chen TT, Takaishi T (2013) Empirical study of the GARCH model with rational errors. JPCS 454: 9714–9722. |
[7] | Clark MA (2006) The rational hybrid Monte Carlo algorithm. POS 75: 453–456. |
[8] | Clark MA, Kennedy AD (2006) Accelerating dynamical-fermion computations using the Rational Hybrid Monte Carlo algorithm with multiple pseudofermion fields. Phys Rev lett 98: 051601. |
[9] |
Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50: 987–1007. doi: 10.2307/1912773
![]() |
[10] |
Glosten LR, Jagannathan R, Runkle DE (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J Financ 48: 1779–1801. doi: 10.1111/j.1540-6261.1993.tb05128.x
![]() |
[11] |
Hansen PR, Lunde A (2005) A forecast comparison of volatility models: does anything beat a GARCH(1, 1)? J appl Econom 20: 873–889. doi: 10.1002/jae.800
![]() |
[12] |
Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109. doi: 10.1093/biomet/57.1.97
![]() |
[13] |
Metropolis N, Rosenbluth AW, Rosenbluth MN, et al. (1953) Equation of state calculations by fast computing machines. J Chem phys 21: 1087–1092. doi: 10.1063/1.1699114
![]() |
[14] | Mitsui H,Watanabe T (2003) Bayesian analysis of GARCH option pricing models. J Japan Statist Soc (Japanese Issue) 33: 307–324. |
[15] |
Nelson DB (1991) Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59: 347–370. doi: 10.2307/2938260
![]() |
[16] |
Nuyts J, Platten I (2001) Phenomenology of the term structure of interest rates with Padé Approximants. Physica A 299: 528–546. doi: 10.1016/S0378-4371(01)00320-X
![]() |
[17] |
Patton AJ (2011) Volatility forecast comparison using imperfect volatility proxies. J Econom 160: 246–256. doi: 10.1016/j.jeconom.2010.03.034
![]() |
[18] |
Sentana E (1995) Quadratic ARCH models. Rev Econ Stud 62: 639–661. doi: 10.2307/2298081
![]() |
[19] |
Spiegelhalter DJ, Best NG, Carlin BP, et al. (2002) Bayesian measures of model complexity and fit. J Roy Stat Soc B 64: 583–639. doi: 10.1111/1467-9868.00353
![]() |
[20] |
de Forcrand P, Takaishi T (1997) Fast fermion Monte Carlo. Nucl Phys B - Proc Sup 53: 968–970. doi: 10.1016/S0920-5632(96)00829-8
![]() |
[21] | Takaishi T, de Forcrand P (2001a) Odd-flavor simulations by Hybrid Monte Carlo. Non-Perturbative Methods and Lattice QCD, World Scientific 112–120. |
[22] | Takaishi T, de Forcrand P (2001b) Simulation of nf= 3 QCD by Hybrid Monte Carlo. Nucl Phys B-Proc Sup 94: 818–822. |
[23] | Takaishi T, de Forcrand P (2001c) Simulations of Odd Flavors QCD by Hybrid Monte Carlo. Int Symposium Quantum Chromodynamics Color Confinement, CONFINEMENT 2000, World Scientific 383–387. |
[24] |
Takaishi T, de Forcrand P (2002) Odd-flavor Hybrid Monte Carlo Algorithm for Lattice QCD. Int J Mod Phys C 13: 343–365. doi: 10.1142/S0129183102003152
![]() |
[25] | Takaishi T (2009a) An Adaptive Markov Chain Monte Carlo Method for GARCH Model. Lecture Notes Inst Computer Sciences, Social Inform Telecommun Engineering. Complex Sciences 5: 1424–1434. |
[26] | Takaishi T (2009b) Bayesian Estimation of GARCH Model with an Adaptive Proposal Density. New Advances Intell Decis Technol, Stud Comput Intell 199: 635–643. |
[27] | Takaishi T (2009c) Bayesian Inference on QGARCH Model Using the Adaptive Construction Scheme. Proc 8th IEEE/ACIS Int Conf Computer Inf Science 525–529. |
[28] | Takaishi T (2010) Bayesian inference with an adaptive proposal density for GARCH models. JPCS 221: 012011. |
[29] |
Takaishi T (2017) Rational GARCH model: An empirical test for stock returns. Physica A 473: 451–460. doi: 10.1016/j.physa.2017.01.011
![]() |
[30] | Takaishi T, Chen TT (2012) Bayesian Inference of the GARCH model with Rational Errors. Int Proc Econ Dev Res 29: 303–307. |
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