Processing math: 71%
Research article Special Issues

Convergence rate for integrated self-weighted volatility by using intraday high-frequency data with noise

  • High-frequency financial data are becoming increasingly available and need to be analyzed under the current circumstances for the market prices of stocks, currencies, risk analysis, portfolio management and other financial instruments. An emblematic challenge in econometrics is estimating the integrated volatility for financial prices, i.e., the quadratic variation of log prices. Following this point, in this paper, we study the estimation of integrated self-weighted volatility, i.e., the generalized style of integrated volatility, by using intraday high-frequency data with noise. In order to reduce the effect of noise, the "pre-averaging" technique is used. Both the law of large numbers and the central limit theorem of the estimator of integrated self-weighted volatility are established in this paper. Meanwhile, a studentized version is also given in order to make some statistical inferences. At the end of this article, the simulation results obtained to evaluate the accuracy of approximating the sampling distributions of the estimator are displayed.

    Citation: Erlin Guo, Cuixia Li, Patrick Ling, Fengqin Tang. Convergence rate for integrated self-weighted volatility by using intraday high-frequency data with noise[J]. AIMS Mathematics, 2023, 8(12): 31070-31091. doi: 10.3934/math.20231590

    Related Papers:

    [1] Yousef Jawarneh, Humaira Yasmin, M. Mossa Al-Sawalha, Rasool Shah, Asfandyar Khan . Fractional comparative analysis of Camassa-Holm and Degasperis-Procesi equations. AIMS Mathematics, 2023, 8(11): 25845-25862. doi: 10.3934/math.20231318
    [2] Zheng Dou, Kexin Luo . Global weak solutions of nonlinear rotation-Camassa-Holm model. AIMS Mathematics, 2023, 8(7): 15285-15298. doi: 10.3934/math.2023781
    [3] Yunxi Guo, Ying Wang . The Cauchy problem to a gkCH equation with peakon solutions. AIMS Mathematics, 2022, 7(7): 12781-12801. doi: 10.3934/math.2022707
    [4] A. K. M. Kazi Sazzad Hossain, M. Ali Akbar . Solitary wave solutions of few nonlinear evolution equations. AIMS Mathematics, 2020, 5(2): 1199-1215. doi: 10.3934/math.2020083
    [5] Mahmoud A. E. Abdelrahman, S. Z. Hassan, R. A. Alomair, D. M. Alsaleh . Fundamental solutions for the conformable time fractional Phi-4 and space-time fractional simplified MCH equations. AIMS Mathematics, 2021, 6(6): 6555-6568. doi: 10.3934/math.2021386
    [6] Zhe Ji, Yifan Nie, Lingfei Li, Yingying Xie, Mancang Wang . Rational solutions of an extended (2+1)-dimensional Camassa-Holm- Kadomtsev-Petviashvili equation in liquid drop. AIMS Mathematics, 2023, 8(2): 3163-3184. doi: 10.3934/math.2023162
    [7] M. Ali Akbar, Norhashidah Hj. Mohd. Ali, M. Tarikul Islam . Multiple closed form solutions to some fractional order nonlinear evolution equations in physics and plasma physics. AIMS Mathematics, 2019, 4(3): 397-411. doi: 10.3934/math.2019.3.397
    [8] Umair Ali, Sanaullah Mastoi, Wan Ainun Mior Othman, Mostafa M. A Khater, Muhammad Sohail . Computation of traveling wave solution for nonlinear variable-order fractional model of modified equal width equation. AIMS Mathematics, 2021, 6(9): 10055-10069. doi: 10.3934/math.2021584
    [9] M. TarikulIslam, M. AliAkbar, M. Abul Kalam Azad . Traveling wave solutions in closed form for some nonlinear fractional evolution equations related to conformable fractional derivative. AIMS Mathematics, 2018, 3(4): 625-646. doi: 10.3934/Math.2018.4.625
    [10] Ying Wang, Yunxi Guo . Blow-up solution and analyticity to a generalized Camassa-Holm equation. AIMS Mathematics, 2023, 8(5): 10728-10744. doi: 10.3934/math.2023544
  • High-frequency financial data are becoming increasingly available and need to be analyzed under the current circumstances for the market prices of stocks, currencies, risk analysis, portfolio management and other financial instruments. An emblematic challenge in econometrics is estimating the integrated volatility for financial prices, i.e., the quadratic variation of log prices. Following this point, in this paper, we study the estimation of integrated self-weighted volatility, i.e., the generalized style of integrated volatility, by using intraday high-frequency data with noise. In order to reduce the effect of noise, the "pre-averaging" technique is used. Both the law of large numbers and the central limit theorem of the estimator of integrated self-weighted volatility are established in this paper. Meanwhile, a studentized version is also given in order to make some statistical inferences. At the end of this article, the simulation results obtained to evaluate the accuracy of approximating the sampling distributions of the estimator are displayed.



    There have been many literatures on continuous dependence and structural stability for the past few years, including those of Aulisa et al. [1], Celebi et al. [2,3], Liu et al. [4,5,6], Chen et al. [7,8], Ames and Payne [9,10], Ames and Straughan [11], Ciarletta and Straughan [12], Franchi and Straughan [13,14,15,16], Lin and Payne [17,18], Li et al. [19,20,21], Straughan et al. [22,23] and Zhou et al. [24,25]. Particularly, most researches focus on the continuous dependence on the boundary data, domain geometry, initial time geometry, and the model itself. Hirsch and Smale [26] pointed out the necessity of studying the continuous dependence of solutions. They emphasized the physical significance of this type of research. This means that changes in the coefficients of partial differential equations may be physically reflected through changes in constitutive parameters. We trust that mathematical analysis of these equations will help to disclose their applicability in physics. Since inevitable errors occur in both numerical calculations and physical measurements of data, continuous correlation results are very important. It is relevant to understand the extent to which such errors affect the solution.

    Harfash [27] researched a system of equations to describe the double-diffusion convection in Darcy flow with magnetic field effect. The author assumed the magnetic fields with only the vertical component which was a specific magnetic field. By establishing a priori results, the author illustrates that the solution of the equations depends continuously on changes in the magnetic force and gravity vector coefficients. Some authors have paid attentions to similar problems. By employing Payne's [28] highly innovative procedure for obtaining a priori estimates, Ames and Payne [9] have established a similar result for the Navier-Stokes equations. But it is necessary to restrict the size of the interval or the size of the initial data in their result. A similar result for a Brinkman porous material and for the Darcy equations of flow in porous media has been derived by Franchi and Straughan [29] and Payne and Straughan [30], respectively.

    In this paper, we assume that the Darcy flow with magnetic field effect occupies a bounded region Ω in R3 and that the boundary of the region is denoted by Ω which is sufficient smooth to use the divergence theorem. The variables vi, T, C and p are the fluid velocity vector, the temperature, the salt concentration and the pressure, respectively. The governing equations for Darcy flow with magnetic field effect may be written as

    vi=p,i+giT+hiC+σ[(v×B0)×B0]i, (1.1)
    T,t+viT,i=ΔT, (1.2)
    C,t+viC,i=ΔC+γΔT, (1.3)
    vi,i=0, (1.4)

    where gi and hi are gravity vector terms arising in the density equation of state, Δ is Laplacian operator, γ is the Soret coefficient, σ is magnetic coefficient, and B0=(0,0,B0) is a magnetic field with only the vertical component and v=(v1,v2,v3). In (1.1), we take a particular magnetic field, as in [27,31].

    On the boundary, we impose

    vini=0,Tn+kT=F(x,t),Cn+τC=G(x,t), on Ω×{t>0}, (1.5)

    where F and G are positive functions, ni is the unit outward normal to Ω and k and τ are positive constants. Equation (1.5) may be thought of as expressing Newton's law of cooling with inhomogeneous outside temperature or inhomogeneous outside salt concentration, i.e.

    Tn=k(TTa),Cn=κ(CCa),

    where Ta and Ca are the ambient outside temperature and the ambient outside salt concentration, respectively. The initial conditions are written as

    T(x,0)=T0(x);C(x,0)=C0(x); in Ω, (1.6)

    for prescribed functions T0 and C0.

    In our work, we still consider the same particular equations as in [27]. But our boundary conditions is Newton's law of cooling type with inhomogeneous outside temperature. Thus, the Sobolev inequalities which are used in [27] are not available in our paper. Compared with [9], we no longer need to impose special restrictions on the region Ω. So their method fails to handle the system in this paper. In this paper, we derive the upper bounds of ΩT4dx and ΩC4dx which are difficulty to obtain. By using the these priori results, we derive the continuous dependence on the magnetic coefficient and the boundary parameter. Throughout this paper, the usual summation convention is employed with repeated Latin subscripts summed from 1 to 3. The comma is used to indicate partial differentiation, i.e. ui,j=uixj, ui,jui,j=Σ3i,j=1uixj.

    In this section, we want to derive bounds for various norms of vi, T and C in term of known data which will be used in the next sections. Before we derive these bounds, we prove some lemmas firstly.

    Lemma 2.1. Let functions fi,(i=1,2,3), defined on Ω, be some functions such that

    finif0>0 ,on Ω, (2.1)

    and

    |fi,i|m1,|fi|m2, (2.2)

    where f0>0 is a constant and m1, m2 are both positiveconstants. Then,

    f0Ωφ2dAm3Ωφ2dx+αΩφ,iφ,idx, (2.3)

    for a function φ which is defined on the closure of thedomain Ω. In (2.3), α>0 is an arbitrary constant which may be very small and m3=(m1+m22α).

    Proof. We began with the identity

    (fiφ2),i=fi,iφ2+2fiφφ,i. (2.4)

    Integrating (2.4) over Ω, using (2.1) and the divergence theorem, we have

    f0Ωφ2dAΩ(fiφ2),idx=Ωfi,iφ2dx+2Ωfiφφ,idx. (2.5)

    The Hölder inequality and (2.2) allow us to obtain

    f0Ωφ2dAm1Ωφ2dx+2m2(Ωφ2dx)12(Ωφ,iφ,idx)12, (2.6)

    from which it follows that

    f0Ωφ2dA(m1+m22α)Ωφ2dx+αΩφ,iφ,idx. (2.7)

    Lemma 2.2. Let T,vH1(Ω), T0L2P(Ω) and FL2P(Ω). Then, the solution for (1.2) satisfies

    supΩ×[0,ς]|T|Tm,

    where Tm=max{|T0|,|F|}.

    Proof. We began with

    ddtΩT2pdx=2pΩT2p1T,tdx.

    Using (1.2), the divergence theorem and the Young inequality, we are leaded to

    ddtΩT2pdx2pΩT2p1FdA2pkΩT2pdA2p(2p1)ΩT2p2T,iT,idx(2p1)2p1(2pk)2p1ΩF2pdA.

    An integration of this inequality allows that

    (ΩT2pdx)12p(2p12pkΩF2pdA+ΩT2p0dx)12p.

    Allowing p, we obtain

    supΩ×[0,ς]|T|Tm,

    where Tm depends on the initial-boundary conditions of T.

    Lemma 2.3. Let T,vH1(Ω) and C be thesolutions for (1.2) and (1.3) and T0,C0C2(Ω), F,GC2(Ω×{t>0}). Then,

    ΩT2dxA1(t),ΩC2dxA2(t), (2.8)

    where A1(t) and A2(t) are positive functions which will be given later.

    Proof. Using (1.2) and the divergence theorem, we compute

    12ddtΩT2dx=ΩTT,tdx=ΩT[ΔTviT,i]dx=ΩTFdAkΩT2dAΩT,iT,idx. (2.9)

    By the Hölder inequality and the Young inequality, from (2.9) we have

    12ddtΩT2dx+ΩT,iT,idx14kΩF2dA. (2.10)

    Integrating (2.10) from 0 to t, we have

    ΩT2dx+2t0ΩT,iT,idxdη12kt0ΩF2dAdη+ΩT20dxA1(t). (2.11)

    From the identity

    ΩC(C,t+viC,iΔCγΔT)dx=0,

    we get

    12ddtΩC2dx+ΩC,iC,idx=ΩGCdAτΩC2dA+γΩFCdAkγΩTCdAγΩT,iC,idx. (2.12)

    Upon employing the Cauchy-Schwarz inequality and the arithmetic-geometric mean inequality, we can get

    ΩGCdA1τΩG2dA+τ4ΩC2dA,γΩFCdAγ2τΩF2dA+τ4ΩC2dA,kγΩTCdA12τk2γ2ΩT2dA+τ2ΩC2dA,γΩT,iC,idx12γ2ΩT,iT,idx+12ΩC,iC,idx.

    We use these inequalities together with (2.12) to arrive at

    ddtΩC2dx+ΩC,iC,idx2τΩG2dA+2γ2τΩF2dA+k2γ2τΩT2dA+γ2ΩT,iT,idx. (2.13)

    Letting φ=T in Lemma 2.1 and using (2.11), we have

    f0ΩT2dAm3ΩT2dx+αΩT,iT,idxm3A1(t)+αΩT,iT,idx. (2.14)

    Thus, (2.13) can be rewritten as

    ddtΩC2dx+ΩC,iC,idx2τΩG2dA+2γ2τΩF2dA+k2m3γ2f0τA1(t)+2γ2ΩT,iT,idx, (2.15)

    with α=f0τk2. An integration of (2.15) leads to

    ΩC2dx+t0ΩC,iC,idxdη2τt0ΩG2dAdη+2γ2τt0ΩF2dAdη+k2m3γ2f0τt0A1(η)dη+2γ2t0ΩT,iT,idxdη+ΩC20dx. (2.16)

    In light of (2.11), we have

    ΩC2dx+t0ΩC,iC,idxdη2τt0ΩG2dAdη+2γ2τt0ΩF2dAdη+k2m3γ2f0τt0A1(η)dη+γ2A1(t)+ΩC20dxA2(t). (2.17)

    Lemma 2.4. Let T and C be the solutions for(1.2) and (1.3), and T,vH1(Ω), T0,C0C4(Ω), F,GC4(Ω×{t>0}). Then,

    ΩT4dxA3(t),ΩC4dxA4(t), (2.18)

    where A3(t) and A4(t) will be given later.

    Proof. We first let H be a solution of the problem

    H,t+viH,i=ΔH, in Ω×{t>0},Hn+τH=G(x,t),on Ω×{t>0},H(x,0)=C0(x),in Ω. (2.19)

    Using (2.19) and the divergence theorem, we find

    14ddtΩH4dx=ΩH3H,tdx=ΩH3[ΔHviH,i]dx=ΩH3GdAτΩH4dA34Ω(H2),i(H2),idx. (2.20)

    By the Hölder inequality, we have

    ΩH4dx+3t0Ω(H2),i(H2),idxdη2764τ3ΩG4dA+ΩC40dx. (2.21)

    From (2.21), it is clear that ΩH4dx can be bounded by known data. Now, we set

    ψ(x,t)=CH.

    Then, ψ satisfies the initial-boundary condition problem

    ψ,t+viψ,i=Δψ+γΔT,in Ω×{t>0},ψn+τψ=0,on Ω×{t>0},ψ(x,0)=0,in Ω. (2.22)

    Next, we also define a new function

    Φ(t)=δ1ΩT4dx+δ2ΩT2ψ2dx+Ωψ4dx, (2.23)

    where δ1 and δ2 are positive constants to be determined later. Now, it is easy to see that

    Φ(t)=4δ1ΩT3(ΔTviT,i)dx+2δ2ΩTψ2(ΔTviT,i)dx+2δ2ΩT2ψ(Δψ+γΔTviψ,i)dx+4Ωψ3(Δψ+γΔTviψ,i)dx, (2.24)

    from which we may get that

    Φ(t)=3δ1Ω(T2),i(T2),idx3Ω(ψ2),i(ψ2),idx2δ2Ω(ψT,i+ψ,iT)(ψT,i+ψ,iT)dx4δ2ΩTψψ,iT,idx4δ2γΩTψT,iT,idx2δ2γΩT2ψ,iT,idx12γΩψ2ψ,iT,idx4δ1kΩT4dA4τΩψ4dA+4δ1ΩT3FdA+2δ2Ωψ2TFdA+2δ2γΩψT2FdA2δ2(k+τ)Ωψ2T2dA2δ2kγΩψT3dA+4γΩψ3FdA4kγΩψ3TdA=161Ji. (2.25)

    Now using the arithmetic-geometric mean and the Schwarz inequalities, we find that

    J412δ2ε1Ω(T2),i(T2),idx+δ22ε1Ω(ψ2),i(ψ2),idx, (2.26)

    and

    J5+J6=4δ2γΩTT,i[Tψ,i+T,iψ]dx+2δ2γΩT2ψ,iT,idxδ2ε2Ω(T2),i(T2),idx+δ2γ2ε2Ω[Tψ,i+T,iψ][Tψ,i+T,iψ]dx+2δ2T2mγ(Ω|ψ|2dxΩ|T|2dx)12, (2.27)

    where Tm is defined in Lemma 2.2. Furthermore,

    J7=12γΩψψ,i[ψT,i+ψ,iT]dx+12γΩTψ|ψ|2dx3ε3Ω(ψ2),i(ψ2),idx+3γ2ε3Ω[Tψ,i+T,iψ][Tψ,i+T,iψ]dx+3γ2ε4T2mΩ|ψ|2dx+3ε4Ω(ψ2),i(ψ2),idx, (2.28)

    Inserting (2.26)–(2.28) into (2.25), and using the Hölder and the Young inequalities to the integrals on the boundary, we have

    Φ(t)(3δ112δ2ε1δ2ε2)Ω(T2),i(T2),idx(3δ22ε13ε33ε4)Ω(ψ2),i(ψ2),idx(2δ2δ2γ2ε23γ2ε3)Ω(ψT,i+ψ,iT)(ψT,i+ψ,iT)dx+2δ2T2mγ(Ω|ψ|2dxΩ|T|2dx)12+3γ2ε4T2mΩ|ψ|2dx(4δ1γ3δ1ε5δ2ε72ε6δ2ε8δ2(κ+τ)ε1032δ2kγε11γε313)ΩT4dA(4κδ2ε6δ2ε92ε8δ2(κ+τ)ε1012δ2kε3113γε123γε13)Ωψ4dA+(δ1ε35+δ22ε6ε7+δ22ε8ε9+γε312)ΩF4dA, (2.29)

    where εi (i=1,2,,13) are positive constants to be determined. To ensure that the coefficients of the first three terms and the sixth and seventh terms to be non-positive, we choose that

    δ1=max{5γ4,27γ3(k+τ)2k+(92)43kγ3+12(92)3γ3k3}, δ2=6γ2,ε1=3γ2, ε2=γ2, ε3=12, ε4=6, ε5=γ3, ε6=k9γ2, ε7=kδ154γ3, ε8=δ112γ,ε9=kδ1108γ3, ε10=k9(κ+τ)γ2, ε11=392γ, ε12=ε13=2k9γ.

    We drop the non-positive terms in (2.29) to have

    Φ(t)2δ2T2mγ(Ω|ψ|2dxΩ|T|2dx)12+6γ2ε4T2mΩ|ψ|2dx+(δ1ε35+δ22ε6ε7+δ22ε8ε9+γε312)ΩF4dA.

    Using the arithmetic-geometric mean inequality and integrating the above formula from 0 to t, we obtain

    Φ(t)˜m1t0Ω|ψ|2dxdη+˜m2t0Ω|T|2dxdη+˜m3t0ΩF4dAdη, (2.30)

    where ˜m1=δ2T2mγ+6γ2ε4T2m, ˜m2=δ2T2mγ and ˜m3=(δ1ε35+δ22ε6ε7+δ22ε8ε9+γε312).

    Next, we multiply (2.22)1 with ψ, integrate in Ω and use Cauchy-Schwarz's inequality to obtain

    ddt||ψ||2=2Ωψ,iψ,idx2τΩψ2dA2γΩT,iψ,idx2γΩFψdA2kγΩTψdAΩψ,iψ,idx+γ2ΩT,iT,idx+γ2τΩF2dA+k2γ2τΩT2dA. (2.31)

    In light of (2.14), (2.31) yields that

    ddtΩψ2dxΩψ,iψ,idx+(k2γ2αf0τ+γ2)ΩT,iT,idx+γ2τΩF2dA+k2m3γ2f0τA1(t). (2.32)

    Integrating (2.32) from 0 to t, we have

    Ωψ2dx+t0Ωψ,iψ,idxdη(k2γ2αf0τ+γ2)t0ΩT,iT,idxdη+γ2τt0ΩF2dAdη+k2m3γ2f0τt0A1(η)dη. (2.33)

    With the aid of (2.11), inequality (2.33) can be rewritten as

    Ωψ2dx+t0Ωψ,iψ,idxdη12(k2γ2αf0τ+γ2)A1(t)+γ2τt0ΩF2dAdη+k2m3γ2f0τt0A1(η)dη. (2.34)

    Inserting (2.34) into (2.30) and using (2.11) again, we have

    Φ(t)m(t), (2.35)

    where

    m(t)=12~m1(k2γ2αf0τ+γ2)A1(t)+~m1γ2τt0ΩF2dAdη+~m1k2m3γ2f0τt0A1(η)dη+m22A1(t)+˜m3t0ΩF4dAdη.

    Recalling the definition of Φ(t) in (2.23), we may get

    Ω|T|4dx1δ1m(t)A3(t),Ω|ψ|4dxm(t). (2.36)

    By the triangle inequality, we have

    (ΩC4dx)14(Ωψ4dx)14+(ΩH4dx)14.

    Combining (2.21) and (2.36), we have

    ΩC4dxA4(t), (2.37)

    where

    A4(t)={m14(t)+[2764τ3ΩG4dA+ΩC40dx]14}4.

    Next, we pay our attention to seek the bound for L2 norm of vi as well as v. We obtain the following lemma which will be used in the continuous dependence proof.

    Lemma 2.5. Let vi, T and C are the solutions of(1.1)–(1.3) with the initial-boundary conditions (1.5) and(1.6), and T0,C0C4(Ω), F,GC4(Ω×{t>0}). Then,

    ΩvividxA5(t),t0Ωvi,jvi,jdxdηA6(t), (2.38)

    where A5(t) and A6(t) are positive functions which will bederived later.

    Proof. We start with the identity

    Ωvividx=Ωvi{p,i+giT+hiC+σ[(v×B0)×B0]i}dx.

    Since B0=(0,0,B0), it is clear that [(v×B0)×B0]i=B20(¯kiv3vi), where ¯k=(¯k1,¯k2,¯k3)=(0,0,1). Obviously,

    [(v×B0)×B0]v=B20(¯kiv3vi)vi=B20[v21+v22]0, (2.39)

    so by the Hölder inequality and the arithmetic-geometric mean inequality, we have

    Ωvividx2g2ΩT2dx+2h2ΩC2dx.

    Combining (2.8) and Lemma 2.3, we obtain

    Ωvividx2g2A1(t)+2h2A2(t)A5(t). (2.40)

    We commence bounding the L2 norm for the velocity gradient. To do this, we split the velocity into symmetric and skew parts. We write

    Ωvi,jvi,jdx=Ωvi,j(vi,jvj,i)dx+Ωvi,jvj,idx. (2.41)

    To bound the first term of (2.41), we use the Eq (1.1) to have

    Ωvi,j(vi,jvj,i)dx=Ω{p,ij+giT,j+hiC,j+σB20(¯kiv3vi),j}vi,jdxΩ{p,ij+gjT,i+hjC,i+σB20(¯kjv3vj),i}vi,jdx=Ω(giT,jgjT,i)vi,jdx+Ω(hiC,jhjC,i)vi,jdx+σB20Ω(¯kiv3,j¯kjv3,i)vi,jdxσB20Ω(vi,jvj,i)vi,jdx. (2.42)

    Using Hölder inequality and arithmetic-geometric inequality again in (2.42), we arrive at

    Ω(giT,jgjT,i)vi,jdxΩ(giT,jgjT,i)(giT,jgjT,i)dx+14Ωvi,jvi,jdx=2Ω(g2T,iT,igiT,igjT,j)dx+14Ωvi,jvi,jdx2Ω(g2T,iT,i+12gigiT,iT,i+12gjgjT,jT,j)dx+14Ωvi,jvi,jdx4g2ΩT,iT,idx+14Ωvi,jvi,jdx. (2.43)

    Similarly, we also have

    Ω(hiC,jhjC,i)vi,jdx4h2ΩC,iC,idx+14Ωvi,jvi,jdx. (2.44)

    In view of ¯k=(0,0,1), the third term of (2.42) yields

    σB20Ω(¯kiv3,j¯kjv3,i)vi,jdx=12σB20Ω(¯kiv3,j¯kjv3,i)(vi,jvj,i)dx=σB20Ω¯kiv3,j(vi,jvj,i)dx=σB20Ωv3,j(v3,jvj,3)dxσB20Ω(vi,jvj,i)vi,jdx. (2.45)

    Inserting (2.43)–(2.45) into (2.42), we have

    Ωvi,j(vi,jvj,i)dx4g2ΩT,iT,idx+4h2ΩC,iC,idx+12Ωvi,jvi,jdx. (2.46)

    To handle the second term of (2.41), we use the divergence theorem and integrate by parts to obtain

    Ωvi,jvj,idx=Ωvi,jvjnidA=Ω(vini),jvjdAΩvivjni,jdA. (2.47)

    The first term of (2.47) is zero, since vini=0 on Ω. If the region Ω is convex, Lin and Payne [18] state Ωvivjni,jdA0 which leads to

    Ωvi,jvj,idx0.

    For non-convex Ω,

    Ωvi,jvj,idxk0ΩvividA.

    Using Lemma 2.1 with φ=vi, we conclude that

    Ωvi,jvj,idxk0m3f0Ωvividx+k0f0αΩvi,jvi,jdx. (2.48)

    Choosing α=f04k0 and then inserting (2.46) and (2.48) into (2.41), we have

    Ωvi,jvi,jdx4g2ΩT,iT,idx+4h2ΩC,iC,idx+k0m3f0Ωvividx+34Ωvi,jvi,jdx,

    from which it follows that

    Ωvi,jvi,jdx16g2ΩT,iT,idx+16h2ΩC,iC,idx+4k0m3f0Ωvividx.

    By (2.11), (2.19) and (2.48), we have

    t0Ωvi,jvi,jdxdη8g2A1(t)+16h2A2(t)+4k0m3f0t0A5(η)dηA6(t),

    where we have used (2.11), (2.17) and (2.40).

    Let (vi,p,T,C) and (vi,p,T,C) be the solutions to the problem (1.1)–(1.6) for the same initial-boundary data, but for different magnetic coefficients σ1 and σ2, respectively. Differential variables wi, π, θ, Σ and σ are defined by

    wi=vivi,θ=TT,Σ=CC,π=pp,σ=σ1σ2.

    Then,

    wi=π,i+giθ+hiΣ+σ[(v×B0)×B0]i+σ1[(w×B0)×B0]i, (3.1)
    θ,t+viθ,i+wiT,i=Δθ, (3.2)
    Σ,t+viΣ,i+wiC,i=ΔΣ+γΔθ, (3.3)
    wi,i=0, (3.4)

    with the initial-boundary conditions

    wini=0,θn=kθ,Σn=τΣ,on  Ω×{t>0}, (3.5)
    θ(x,0)=Σ(x,0)=0, xΩ. (3.6)

    We have the following theorem.

    Theorem 3.1. If T0,C0L(Ω), F,GC4(Ω×{t>0}), then the solutions of (1.1)–(1.6)depend continuously on the magnetic coefficient σ, asshown explicit in inequalities (3.26) and (3.27) whichderives a relation of the form

    βΩθ2dx+ΩΣ2dxL1σ2,

    and

    ΩwiwidxL2σ2,

    where L1 and L2 are priori constants and β>0 is acomputable constant.

    Proof. Multiplying (3.16) with wi and integrating over Ω, then using Cauchy-Schwarz's inequality and the arithmetic-geometric mean inequality, we obtain

    Ωwiwidxg(Ωθ2dx)12(Ωwiwidx)12+h(ΩΣ2dx)12(Ωwiwidx)12+σB20Ω(¯kiv3vi)widx+σ1B20Ω(¯kiw3wi)widx, (3.7)

    where g=max{gigi}, h=max{hihi}. Since ¯k=(0,0,1), it is easy to find

    σ1B20Ω(¯kiw3wi)widx0 (3.8)

    as in (2.39). By the Cauchy-Schwarz inequality, we have

    σB20Ω(¯kiv3vi)widxσB20(Ω(v3)2dx)12(Ωwiwidx)12+σB20(Ωvividx)12(Ωwiwidx)122σB20(Ωvividx)12(Ωwiwidx)12. (3.9)

    Inserting (3.8) and (3.9) into (3.7) and applying the arithmetic-geometric mean inequality, we have

    Ωwiwidx4g2Ωθ2dx+4h2ΩΣ2dx+8σ2B40Ωvividx. (3.10)

    In view of (2.38) in Lemma 2.5, from (3.10) we have

    Ωwiwidx4g2Ωθ2dx+4h2ΩΣ2dx+8σ2B40A5(t). (3.11)

    Next, we compute

    ddt(βΩθ2dx+ΩΣ2dx)=2βΩθθ,tdx+2ΩΣΣ,tdx=2βΩθ[Δθviθ,iwiT,i]dx+2ΩΣ[ΔΣ+γΔθviΣ,iwiC,i]dx=2βΩθ,iθ,idx2ΩΣ,iΣ,idx2βkΩθ2dA2τΩΣ2dA+2βΩθ,iwiTdx+2ΩΣ,iwiCdx2γΩθ,iΣ,idx2kγΩθΣdA. (3.12)

    Using Cauchy-Schwarz inequality and the arithmetic-geometric mean inequality and Lemma 2.4, we have

    2βΩθ,iwiTdx2β(Ωθ,iθ,idx)12(Ω(wiwi)2dx)14(ΩT4dx)14βΩθ,iθ,idx+β(Ω(wiwi)2dx)12A123(t), (3.13)

    and

    2ΩΣ,iwiCdxΩΣ,iΣ,idx+(Ω(wiwi)2dx)12A124(t). (3.14)

    Inserting these two inequalities into (3.12) and using the Cauchy-Schwarz inequality in the last two terms on the right of (3.12), we have

    ddt(βΩθ2dx+ΩΣ2dx)(βγβ1)Ωθ,iθ,idx(1γβ1)ΩΣ,iΣ,idxk(2βγβ2)Ωθ2dA(2τkγβ2)ΩΣ2dA+(Ω(wiwi)2dx)12[βA123(t)+A124(t)], (3.15)

    for some arbitrary positive constants β1 and β2.

    Now, we use the bound for L4 norm of wi which has been derived in [18] (see (B.17)). We write here as the form

    (Ω(wiwi)2dx)12M{(1+δ4)Ωwiwidx+34δ13Ωwi,jwi,jdx}, (3.16)

    where M is a positive computable constant and δ>0 is an arbitrary constant. To get the bound for Ωwi,jwi,jdx, we use a similar methods which were used in (2.41) and (2.48) with \alpha = \frac{f_0}{2k_0} to have

    \begin{equation} \begin{split} \int_\Omega w_{i,j}w_{i,j}dx\leq2\int_\Omega w_{i,j}(w_{i,j}-w_{j,i})dx+\frac{2k_0m_3}{f_0}\int_\Omega w_{i}w_{i}dx. \end{split} \end{equation} (3.17)

    To handle the first term of (3.17), we compute

    \begin{equation} \begin{split} &\int_\Omega(w_{i,j}-w_{j,i})(w_{i,j}-w_{j,i})dx\\ = &2\int_\Omega w_{i,j}(w_{i,j}-w_{j,i})dx\\ = &2\int_\Omega w_{i,j}[-\pi_{,ij}+g_i\theta_{,j}+h_i\Sigma_{,j}+\sigma B_0^2(\overline{k}_iv^*_{3,j}-v^*_{i,j})+\sigma_1 B_0^2(\overline{k}_iw_{3,j}-w_{i,j})]dx\\ &-2\int_\Omega w_{i,j}[-\pi_{,ij}+g_j\theta_{,i}+h_j\Sigma_{,i}+\sigma B_0^2(\overline{k}_jv^*_{3,i}-v^*_{j,i})+\sigma_1 B_0^2(\overline{k}_jw_{3,j}-w_{j,i})]dx\\ = &2\int_\Omega[g_i\theta_{,j}-g_j\theta_{,i}]w_{i,j}dx+2\int_\Omega[g_j\Sigma_{,i}-g_i\Sigma_{,j}]w_{i,j}dx \\ &+2\sigma B_0^2\int_\Omega[\overline{k}_iv^*_{3,j}-\overline{k}_jv^*_{3,i}]w_{i,j}dx-2\sigma B_0^2\int_\Omega[v^*_{i,j}-v^*_{j,i}]w_{i,j}dx\\ &+2\sigma_1 B_0^2\int_\Omega[\overline{k}_iw_{3,j}-\overline{k}_jw_{3,i}]w_{i,j}dx -2\sigma_1B_0^2\int_\Omega[w_{i,j}-w_{j,i}]w_{i,j}dx. \end{split} \end{equation} (3.18)

    Using the Cauchy-Schwarz inequality and the arithmetic-geometric mean inequality, we have

    \begin{equation} \begin{split} 2\int_\Omega[g_i\theta_{,j}-g_j\theta_{,i}]w_{i,j}dx& = \int_\Omega[g_i\theta_{,j}-g_j\theta_{,i}][w_{i,j}-w_{j,i}]dx = 2\int_\Omega g_i\theta_{,j}[w_{i,j}-w_{j,i}]dx\\ &\leq8g^2\int_\Omega\theta_{,j}\theta_{,j}dx+\frac{1}{8}\int_\Omega(w_{i,j}-w_{j,i})(w_{i,j}-w_{j,i})dx\\ &\leq8g^2\int_\Omega\theta_{,j}\theta_{,j}dx+\frac{1}{4}\int_\Omega(w_{i,j}-w_{j,i})w_{i,j}dx, \end{split} \end{equation} (3.19)

    and

    \begin{equation} \begin{split} 2\int_\Omega[h_i\Sigma_{,j}-h_j\Sigma_{,i}]w_{i,j}dx\leq8h^2\int_\Omega\Sigma_{,j}\Sigma_{,j}dx+\frac{1}{4}\int_\Omega(w_{i,j}-w_{j,i})w_{i,j}dx. \end{split} \end{equation} (3.20)

    Using the Cauchy-Schwarz inequality and the arithmetic-geometric mean inequality, we have

    \begin{equation} \begin{split} &2\sigma B_0^2\int_\Omega[\overline{k}_iv^*_{3,j}-\overline{k}_jv^*_{3,i}]w_{i,j}dx -2\sigma B_0^2\int_\Omega[v^*_{i,j}-v^*_{j,i}]w_{i,j}dx\\ = &2\sigma B_0^2\int_\Omega\overline{k}_iv^*_{3,j}[w_{i,j}-w_{j,i}]dx-2\sigma B_0^2\int_\Omega v^*_{i,j}[w_{i,j}-w_{j,i}]dx\\ \leq&8\sigma^2B_0^4\int_\Omega v^*_{3,j}v^*_{3,j}dx+8\sigma^2B_0^4\int_\Omega v^*_{i,j}v^*_{i,j}dx+\frac{1}{2}\int_\Omega(w_{i,j}-w_{j,i})w_{i,j}dx. \end{split} \end{equation} (3.21)

    Since \overline {\underline {\textbf{k}} } = (0, 0, 1) , we have

    \begin{equation} \begin{split} &2\sigma_1 B_0^2\int_\Omega[\overline{k}_iw_{3,j}-\overline{k}_jw_{3,i}]w_{i,j}dx\\ = &\; 2\sigma_1 B_0^2\int_\Omega\overline{k}_iw_{3,j}(w_{i,j}-w_{j,i})dx\\ = &\; 2\sigma_1 B_0^2\int_\Omega w_{3,j}(w_{3,j}-w_{j,3})dx\\ \leq&\; 2\sigma_1 B_0^2\int_\Omega w_{i,j}(w_{i,j}-w_{j,i})dx. \end{split} \end{equation} (3.22)

    Inserting (3.19)–(3.21) and (3.22) into (3.18), we obtain

    \begin{equation*} \begin{split} \int_\Omega w_{i,j}(w_{i,j}-w_{j,i})dx\leq8g^2\int_\Omega\theta_{,j}\theta_{,j}dx+8h^2\int_\Omega\Sigma_{,j}\Sigma_{,j}dx+8\sigma^2B_0^4\int_\Omega v^*_{3,j}v^*_{3,j}dx+8\sigma^2B_0^4\int_\Omega v^*_{i,j}v^*_{i,j}dx. \end{split} \end{equation*}

    It follows from (3.17) that

    \begin{equation} \begin{split} \int_\Omega w_{i,j}w_{i,j}dx\leq&16g^2\int_\Omega\theta_{,j}\theta_{,j}dx+16h^2\int_\Omega\Sigma_{,j}\Sigma_{,j}dx\\ &+16\sigma^2B_0^4\int_\Omega v^*_{3,j}v^*_{3,j}dx+16\sigma^2B_0^4\int_\Omega v^*_{i,j}v^*_{i,j}dx+\frac{2k_0m_3}{f_0}\int_\Omega w_{i}w_{i}dx. \end{split} \end{equation} (3.23)

    Combining (3.15), (3.16) and (3.23), we conclude

    \begin{equation} \begin{split} \frac{d}{dt}\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\leq&-M_1\int_\Omega\theta_{,i}\theta_{,i}dx-M_2\int_\Omega\Sigma_{,i}\Sigma_{,i}dx-M_3\int_{\partial\Omega}\theta^2dA\\ &-M_4\int_{\partial\Omega}\Sigma^2dA+M_5\int_\Omega w_iw_idx[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)]\\ &+M_6\sigma^2\Big[\int_\Omega v^*_{3,j}v^*_{3,j}dx+\int_\Omega v^*_{i,j}v^*_{i,j}dx\Big]\Big[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)\Big], \end{split} \end{equation} (3.24)

    where

    \begin{equation*} \begin{split} M_1& = \beta-\frac{\gamma}{\beta_1}-12g^2M\delta^{-\frac{1}{3}}[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)], \\ M_2& = 1-\gamma\beta_1-12h^2M\delta^{-\frac{1}{3}}[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)],\\ M_3& = k(2\beta-\frac{\gamma}{\beta_2}),\ M_4 = 2\tau-k\gamma\beta_2,\\ M_5& = M(1+\frac{1}{4}\delta+\frac{3}{4}\delta^{-\frac{1}{3}}),\ M_6 = 12M\delta^{-\frac{1}{3}}B^4_0. \end{split} \end{equation*}

    Choosing \beta_1 = \frac{1}{2\gamma} , \beta_2 = \frac{2\tau}{k\gamma} and \beta = \max\{\frac{k\gamma^2}{4\tau}, 2\gamma^2 \}, we note that M_3 > 0 , M_4 = 0 , \beta-\frac{\gamma}{\beta_1} > 0 and 1-\gamma\beta_1 > 0 . Since the constant \delta is at our disposal then provided A_3(t) and A_4(t) are bounded, we may choose \delta so large that M_1\geq0 and M_2\geq0 . Dropping the non-positive terms in (3.24) and using Lemma 2.5 and (3.11), we have

    \begin{equation} \begin{split} \frac{d}{dt}\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)&\leq \mathcal{F}_1(t)\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)+\sigma^2\mathcal{F}_2(t), \end{split} \end{equation} (3.25)

    where

    \begin{equation*} \begin{split} \mathcal{F}_1(t)& = 4M_5(\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t))\max\{\frac{g^2}{\beta},h^2\},\\ \mathcal{F}_2(t)& = 8M_5(\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t))B_0^4A_5(t)+2M_6(\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t))B_0^4A_6(t). \end{split} \end{equation*}

    From (3.25), we have

    \begin{equation*} \begin{split} \frac{d}{dt}\Big\{\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\exp\Big(-\int_0^t\mathcal{F}_1(\eta)d\eta\Big)\Big\}\leq \sigma^2\mathcal{F}_2(t)\exp\Big(-\int_0^t\mathcal{F}_1(\eta)d\eta\Big), \end{split} \end{equation*}

    which follows that

    \begin{equation} \begin{split} \beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\leq\sigma^2\int_0^t\mathcal{F}_2(t)\exp\Big(-\int_\eta^t\mathcal{F}_1(\zeta)d\zeta\Big)d\eta. \end{split} \end{equation} (3.26)

    This is the continuous dependence result we want to prove. By (3.11), we may obtain the continuous dependence for {\textbf{v}} ,

    \begin{equation} \begin{split} \int_\Omega w_iw_idx\leq\sigma^2\Big[\int_0^t\mathcal{F}_2(t)\exp\Big(-\int_\eta^t\mathcal{F}_1(\zeta)d\zeta\Big)d\eta+8B_0^4A_5(t)\Big]. \end{split} \end{equation} (3.27)

    In this section, we derive the continuous dependence on the cooling coefficients and we let (u_i, p, T, C) and (u^*_i, p^*, T^*, C^*) be the solutions to the problem (1.1)–(1.3) for the same initial-boundary data and the same F and G , but for different the cooling coefficients k_1 , k_2 , \tau_1 and \tau_2 , respectively. As in Section 3, we still set

    \begin{equation*} \begin{split} w_i = v_i-v^*_i,\quad \theta = T-T^*,\quad \Sigma = C-C^*,\quad \pi = p-p^*,\quad k = k_1-k_2, \quad \tau = \tau_1-\tau_2. \end{split} \end{equation*}

    Then (w_i, \theta, \Sigma, \pi) satisfy

    \begin{align} & w_i = -\pi_{,i}+g_i\theta+h_i\Sigma+\sigma[({\textbf{w}}\times{\textbf{B}}_0)\times{\textbf{B}}_0]_i, \end{align} (4.1)
    \begin{align} &\theta_{,t}+v^*_i\theta_{,i}+w_iT_{,i} = \Delta \theta, \end{align} (4.2)
    \begin{align} &\Sigma_{,t}+v^*_i\Sigma_{,i}+w_iC_{,i} = \Delta\Sigma+\gamma\Delta\theta, \end{align} (4.3)
    \begin{align} &w_{i,i} = 0, \end{align} (4.4)

    with the initial-boundary conditions

    \begin{equation} \begin{split} w_in_i = 0,\quad \frac{\partial\theta}{\partial n}+k_1\theta = -kT^*,\quad \frac{\partial\Sigma}{\partial n}+\tau_1\Sigma = -\tau C^*, \quad on \ \ \partial\Omega \times\{t > 0\}, \end{split} \end{equation} (4.5)
    \begin{equation} \begin{split} \theta(x,0) = \Sigma(x,0) = 0,\ x\in\Omega. \end{split} \end{equation} (4.6)

    We now prove the following theorem.

    Theorem 4.1. If T_0, C_0\in L^\infty(\Omega) , F, G\in C^4({\partial\Omega\times\{t > 0\}}) , then the solution of Eq (1.1)–(1.3) withinitial-boundary conditions (1.5) and (1.6) dependscontinuously on the boundary parameters k and \tau in thesense that

    \begin{equation*} \begin{split} \beta\int_\Omega \theta^2dx+\int_\Omega\Sigma^2dx\leq L_3k^2+L_4\tau^2. \end{split} \end{equation*}

    Further, {\boldsymbol{v}} depends continuously on k and \tau inthe manner

    \begin{equation*} \begin{split} \int_\Omega w_iw_idx\leq L_5k^2+L_6\tau^2, \end{split} \end{equation*}

    where L_3 L_6 are a priori constants.

    Proof. Employing a similar methods of the last section, we have

    \begin{equation} \begin{split} \int_\Omega w_iw_idx\leq4g^2\int_\Omega \theta^2dx+4h^2\int_\Omega\Sigma^2dx, \end{split} \end{equation} (4.7)

    and

    \begin{equation} \begin{split} \int_\Omega w_{i,j}w_{i,j}dx\leq16g^2\int_\Omega\theta_{,j}\theta_{,j}dx+16h^2\int_\Omega\Sigma_{,j}\Sigma_{,j}dx+\frac{8k_0m_3}{f_0}\Big(g^2\int_\Omega \theta^2dx+h^2\int_\Omega\Sigma^2dx\Big). \end{split} \end{equation} (4.8)

    By using (4.2) , (4.3) and the divergence theorem, as the calculation in (3.12), we get

    \begin{equation} \begin{split} &\frac{d}{dt}\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big) \\ = &-2\beta\int_\Omega\theta_{,i}\theta_{,i}dx-2\int_\Omega\Sigma_{,i}\Sigma_{,i}dx-2\beta k_1\int_{\partial\Omega}\theta^2dA\\ &-2\beta k\int_{\partial\Omega}\theta T^*dA-2\tau_1\int_{\partial\Omega}\Sigma^2dA-2\tau\int_{\partial\Omega}\Sigma C^*dA +2\beta\int_\Omega\theta_{,i}w_iTdx\\ &+2\int_\Omega\Sigma_{,i}w_iCdx-2\gamma\int_\Omega\theta_{,i}\Sigma_{,i}dx-2k_1\gamma\int_{\partial\Omega}\theta\Sigma dA-2k\gamma\int_{\partial\Omega}T^*\Sigma dA. \end{split} \end{equation} (4.9)

    We note that (3.13) and (3.14) are still valid in this section. We inserting them into (4.9) and use Cauchy-Schwarz inequality in the other terms on the right of (4.9) to have

    \begin{equation} \begin{split} &\frac{d}{dt}(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx)\\ \leq&-(\beta-\frac{\gamma}{\beta_1})\int_\Omega\theta_{,i}\theta_{,i}dx-(1-\gamma\beta_1)\int_\Omega\Sigma_{,i}\Sigma_{,i}dx\\ &-(2\beta k_1-\beta\beta_3-\frac{k_1\gamma}{\beta_2})\int_{\partial\Omega}\theta^2dA-(2\tau_1-\beta_4-k_1\gamma\beta_2-\gamma\beta_5)\int_{\partial\Omega}\Sigma^2dA\\ &+\Big(\int_\Omega(w_iw_i)^2dx\Big)^\frac{1}{2}[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)]+k^2(\frac{\beta}{\beta_3}+\frac{\gamma}{\beta_5})\int_{\partial\Omega}(T^*)^2dA+\frac{\tau^2}{\beta_4}\int_{\partial\Omega}(C^*)^2dA. \end{split} \end{equation} (4.10)

    We use the inequality (3.16) again and use (4.8) to have

    \begin{equation} \begin{split} \int_\Omega(w_iw_i)^2dx\leq M\Big\{\int_\Omega w_iw_idx+\delta^{-\frac{1}{3}}\Big[\int_\Omega\theta_{,i}\theta_{,i}dx+\int_\Omega\Sigma_{,i}\Sigma_{,i}dx\Big]\Big\}, \end{split} \end{equation} (4.11)

    where M is a positive computable constant. Inserting (4.11) into (4.10) and letting

    \beta_1 = \frac{1}{\gamma},\ \beta_2 = \frac{\tau_1}{2k_1\gamma},\ \beta_3 = k_1,\ \beta_4 = \tau_1,\ \beta_5 = \frac{\tau_1}{2\gamma},

    and then choosing \beta and \delta large enough such that the coefficients of the first four terms of (4.10) are non-positive, we have

    \begin{equation} \begin{split} &\frac{d}{dt}\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\\ \leq& M\int_\Omega w_iw_idx\Big[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)\Big] +k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\int_{\partial\Omega}(T^*)^2dA+\frac{\tau^2}{\beta_4}\int_{\partial\Omega}(C^*)^2dA, \end{split} \end{equation} (4.12)

    where we have dropped the non-positive terms. Now, we derive bounds for the integrals on \partial\Omega . Using Lemma 2.1, we find

    \begin{equation} \begin{split} \int_{\partial\Omega}(T^*)^2dA\leq\frac{m_3}{f_0}\int_{\Omega}(T^*)^2dx+\int_{\Omega}T^*_{,i}T^*_{,i}dx, \end{split} \end{equation} (4.13)

    and

    \begin{equation} \begin{split} \int_{\partial\Omega}(C^*)^2dA\leq\frac{m_3}{f_0}\int_{\Omega}(C^*)^2dx+\int_{\Omega}C^*_{,i}C^*_{,i}dx, \end{split} \end{equation} (4.14)

    where we have chosen \alpha = 1 . Inserting (4.13) and (4.14) into (4.12) and recalling (2.8) and (4.7), we have

    \begin{equation} \begin{split} \frac{d}{dt}\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\leq& \widetilde{\mathcal{F}}_1(t)\Big[\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big]+k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\frac{m_3}{f_0}A_1(t)\\ &+k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\int_{\Omega}T^*_{,i}T^*_{,i}dx+\frac{\tau^2m_3}{f_0\beta_4}A_2(t)+\frac{\tau^2}{\beta_4}\int_{\Omega}C^*_{,i}C^*_{,i}dx, \end{split} \end{equation} (4.15)

    where

    \begin{equation*} \begin{split} \widetilde{\mathcal{F}}_1(t) = 4M\max\{\frac{g^2}{\beta},h^2\}\Big[\beta A_3^\frac{1}{2}(t)+A_4^\frac{1}{2}(t)\Big]. \end{split} \end{equation*}

    It is obvious that (4.15) yields that

    \begin{equation} \begin{split} &\frac{d}{dt}\Big[\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\cdot\exp\Big(-\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\eta\Big)\Big]\\ \leq& \Big\{k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\frac{m_3}{f_0}A_1(t)+k^2\Big(\frac{\beta}{\beta_3}+\frac{\gamma}{\beta_5}\Big)\int_{\Omega}T^*_{,i}T^*_{,i}dx\\ &+\frac{\tau^2m_3}{f_0\beta_4}A_2(t) +\frac{\tau^2}{\beta_4}\int_{\Omega}C^*_{,i}C^*_{,i}dx\}\cdot\exp\Big(-\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\Big)\\ \leq& k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\frac{m_3}{f_0}A_1(t)+k^2\Big(\frac{\beta}{\beta_3}+\frac{\gamma}{\beta_5}\Big) \int_{\Omega}T^*_{,i}T^*_{,i}dx+\frac{\tau^2m_3}{f_0\beta_4}A_2(t) +\frac{\tau^2}{\beta_4}\int_{\Omega}C^*_{,i}C^*_{,i}dx, \end{split} \end{equation} (4.16)

    where we have used the fact \exp\Big(-\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\eta\Big)\leq1 for t > 0 .

    Integrating (4.16) from 0 to t leads to

    \begin{equation} \begin{split} &\Big(\beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\Big)\cdot\exp\Big(-\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\eta\Big)\\ \leq& k^2\Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\frac{m_3}{f_0}\int_0^tA_1(\eta)d\eta +k^2\Big(\frac{\beta}{\beta_3}+\frac{\gamma}{\beta_5}\Big)\int_0^t\int_{\Omega}T^*_{,i}T^*_{,i}dxd\eta\\ &+\frac{\tau^2m_3}{f_0\beta_4}\int_0^tA_2(\eta)d\eta +\frac{\tau^2}{\beta_4}\int_0^t\int_{\Omega}C^*_{,i}C^*_{,i}dxd\eta. \end{split} \end{equation} (4.17)

    Using (2.11) and (2.17) in (4.17) and setting

    \begin{equation} \begin{split} \widetilde{\mathcal{F}}_2(t)& = \Big(\frac{\beta}{\beta_3} +\frac{\gamma}{\beta_5}\Big)\Big[\frac{m_3}{f_0}\int_0^tA_1(\eta)d\eta+\frac{1}{2}A_1(t)\Big],\quad \widetilde{\mathcal{F}}_3(t) = \frac{1}{\beta_4}\Big[\frac{m_3}{f_0A_2(t)}+\int_0^tA_2(\eta)d\eta\Big], \end{split} \end{equation} (4.18)

    we obtain

    \begin{equation} \begin{split} \beta\int_\Omega\theta^2dx+\int_\Omega\Sigma^2dx\leq k^2\widetilde{\mathcal{F}}_2(t)\cdot\exp\Big(\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\eta\Big) +\tau^2\widetilde{\mathcal{F}}_3(t)\cdot\exp\Big(\int_0^t\widetilde{\mathcal{F}}_1(\eta)d\eta\Big). \end{split} \end{equation} (4.19)

    This is the continuous dependence result for T and C . The continuous dependence for v_i follows directly from (4.7).

    In this paper, the continuous dependence of the solution is obtained by using the methods of energy estimate and a priori estimates. The main innovation is to deal with the influence of boundary conditions and magnetic field. The structural stability of boundary parameters and magnetic field coefficients is proved.

    The work was supported national natural Science Foundation of China (Grant No. 11371175), the science foundation of Guangzhou Huashang College (Grant No. 2019HSDS28).

    The authors declare that they have no competing interests.



    [1] T. Hendershott, R. Riordan, High frequency trading and price discovery, J. Economet., 148 (2009), 131–148. https://doi.org/10.2139/ssrn.1938769 doi: 10.2139/ssrn.1938769
    [2] Y. Aït-Sahalia, J. Jacod, Is Brownian motion necessary to model high frequency data? Ann. Stat., 38 (2010), 3093–3128. https://doi.org/10.1214/09-aos749 doi: 10.1214/09-aos749
    [3] Z. Bai, H. Liu, W. Wong, Enhancement of the applicability of Markowitz's portfolio optimization by utilizing random matrix theory, Math. Finan., 19 (2009), 639–667. https://doi.org/10.1111/j.1467-9965.2009.00383.x doi: 10.1111/j.1467-9965.2009.00383.x
    [4] J. Liu, F. Longstaff, J. Pan, Dynamic asset allocation with event risk, J. Financ., 58 (2003), 231–259. https://doi.org/10.1111/1540-6261.00523 doi: 10.1111/1540-6261.00523
    [5] E. Dimson, Risk measurement when shares are subject to infrequent trading, J. Financ. Econ., 7 (1979), 197–226. https://doi.org/10.1016/0304-405X(79)90013-8 doi: 10.1016/0304-405X(79)90013-8
    [6] J. Q. Fan, Y. Y. Li, K. Yu, Vast volatility matrix estimation using high frequency data for portfolio selection, J. Am. Stat. Assoc., 107 (2012), 412–428. https://doi.org/10.1080/1621459.2012.656041 doi: 10.1080/1621459.2012.656041
    [7] Y. Ding, Y. Y. Li, X. H. Zheng, High dimensional minimum variance portfolio estimation under statistical factor models, J. Economet., 222 (2021), 502–515. https://doi.org/10.1016/j.jeconom.2020.07.013 doi: 10.1016/j.jeconom.2020.07.013
    [8] T. T. Cai, J. Hu, Y. Y. Li, X. H. Zheng, High-dimensional minimum variance portfolio estimation based on high-frequency data, J. Economet., 214 (2020), 482–494. https://doi.org/10.1016/j.jeconom.2019.04.039 doi: 10.1016/j.jeconom.2019.04.039
    [9] O. E. Barndorff-Nielsen, N. Shephard, Econometric analysis of realized volatility and its use in estimating stochastic volatility models, J. R. Stat. Soc. B., 64 (2002), 253–280. https://doi.org/10.1111/1467-9868.00336 doi: 10.1111/1467-9868.00336
    [10] O. E. Barndorff-Nielsen, N. Shephard, Power and bipower variation with stochastic volatility and jumps, J. Financ. Econ., 2 (2004), 1–37. https://doi.org/10.1093/jjfinec/nbh001 doi: 10.1093/jjfinec/nbh001
    [11] J. Jacod, Asymptotic properties of realized power variation and related functionals of semi-martingales, Stoch. Proc. Appl., 118 (2008), 517–559. https://doi.org/10.1016/j.spa.2007.05.005 doi: 10.1016/j.spa.2007.05.005
    [12] C. Mancini, Nonparametric threshold estimation for models with stochastic diffusion coefficient and jumps, Scand. J. Stat., 36 (2009), 270–296. https://doi.org/10.1111/j.1467-9469.2008.00622.x doi: 10.1111/j.1467-9469.2008.00622.x
    [13] L. Zhang, P. Mykland, Y. Aït-Sahalia, A tale of two time scales: determining integrated volatility with noisy high-frequency data, J. Am. Stat. Assoc., 100 (2005), 1394–1411. https://doi.org/10.1198/016214505000000169 doi: 10.1198/016214505000000169
    [14] Y. Aït-Sahalia, P. Mykland, L. Zhang, How often to sample a continuous-time process in the presence of market microstructure noise, Rev. Financ. Stud., 18 (2005), 351–416. https://doi.org/10.1023/A:1004318727672 doi: 10.1023/A:1004318727672
    [15] L. Zhang, Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach, Bernoulli, 12 (2006), 1019–1043. https://doi.org/10.3150/bj/1165269149 doi: 10.3150/bj/1165269149
    [16] O. E. Barndorff-Nielsen, P. R. Hansen, A. Lunde, N. Shephard, Designing realized kernels to measure ex-post variation of equity prices in the presence of noise, Econometrica, 76 (2008), 1481–1536. https://doi.org/10.3982/ECTA6495 doi: 10.3982/ECTA6495
    [17] J. Jacod, Y. Li, P. Mykland, M. Podolskij, M. Vetter, Microstructure noise in the continuous case: the pre-averaging approach, Stoch. Proc. Appl., 119 (2009), 2249–2276. https://doi.org/10.1016/j.spa.2008.11.004 doi: 10.1016/j.spa.2008.11.004
    [18] D. Xiu, Quasi-maximum likelihood estimation of volatility with high frequency data, J. Economet., 159 (2010), 235–250. https://doi.org/10.1016/j.jeconom.2010.07.002 doi: 10.1016/j.jeconom.2010.07.002
    [19] Y. Aït-Sahalia, J. Fan, D. Xiu, High frequency covariance estimates with noisy and asynchronous data, J. Am. Stat. Assoc., 105 (2010), 1504–1517. https://doi.org/10.1198/jasa.2010.tm10163 doi: 10.1198/jasa.2010.tm10163
    [20] J. Jacod, Y. Li, X. Zheng, Statistical properties of microstructure noise, Econometrica, 85 (2017), 1133–1174. https://doi.org/10.3982/ECTA13085 doi: 10.3982/ECTA13085
    [21] J. Jacod, Y. Li, X. Zheng, Estimating the integrated volatility when microstructure noise is dependent and observation times are irregular, J. Economet., 208 (2019), 80–100. https://doi.org/10.2139/ssrn.2659615 doi: 10.2139/ssrn.2659615
    [22] Z. Liu, Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations, Financ. Stoch., 21 (2017), 427–469. https://doi.org/10.1007/s00780-017-0325-7 doi: 10.1007/s00780-017-0325-7
    [23] Z. Liu, X. Kong, B. Jing, Estimating the integrated volatility using high frequency data with zero durations, J. Economet., 204 (2018), 18–32. https://doi.org/10.1016/j.jeconom.2017.12.008 doi: 10.1016/j.jeconom.2017.12.008
    [24] M. Wang, N. Xia, Y. Zhou, On the estimation of high-dimensional integrated covariance matrix based on high-frequency data with multiple transactions, preprint paper, 2021. https://doi.org/10.48550/arXiv.1908.08670
    [25] R. Da, D. Xiu, When moving-average models meet high-frequency data: uniform inference on volatility, Econometrica, 89 (2021), 2787–2825. https://doi.org/10.3982/ECTA15593 doi: 10.3982/ECTA15593
    [26] Y. Z. Wang, J. Zou, Vast volatility matrix estimation for high-frequency financial data, Ann. Stat., 38 (2010), 943–978. https://doi.org/10.1214/09-aos730 doi: 10.1214/09-aos730
    [27] M. Tao, Y. Z. Wang, H. Zhou, Optimal sparse volatility matrix estimation for high-dimensional Itô process with measurement error, Ann. Stat., 41 (2013), 1816–1864. https://doi.org/10.1214/13-aos1128 doi: 10.1214/13-aos1128
    [28] D. Kim, Y. Z. Wang, J. Zou, Asymptotic theory for large volatility matrix estimation based on high-frequency financial data, Stoch. Proc. Appl., 126 (2016), 3527–3577. https://doi.org/10.1016/j.spa.2016.05.004 doi: 10.1016/j.spa.2016.05.004
    [29] Y. He, X. B. Kong, L. Yu, X. S. Zhang, Large-dimensional factor analysis without moment constraints, J. Bus. Exon. Stat., 40 (2022), 302–312. https://doi.org/10.1080/07350015.2020.1811101 doi: 10.1080/07350015.2020.1811101
    [30] D. Kim, X. B. Kong, C. X. Li, Y. Z. Wang, Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data, J. Economet., 203 (2018), 69–79. https://doi.org/10.1016/J.JECONOM.2017.09.006 doi: 10.1016/J.JECONOM.2017.09.006
    [31] B. Y. Jing, X. B. Kong, Z. Liu, Modeling high-frequency financial data by pure jump processes, Ann. Stat., 40 (2012), 759–784. https://doi.org/10.1214/12-AOS977 doi: 10.1214/12-AOS977
    [32] B. Y. Jing, C. X. Li, Z. Liu, On estimating the integrated co-volatility using noisy high-frequency data with jumps, Commun. Stat. Theor. Meth., 43 (2013), 3889–3901. https://doi.org/10.1080/03610926.2011.6399746 doi: 10.1080/03610926.2011.6399746
    [33] E. L. Guo, C. X. Li, F. Q. Tang, The convergence rates of a large volatility matrix estimator based on noise, jumps, and asynchronization, Mathematics, 11 (2023), 1425. https://doi.org/10.3390/math11061425 doi: 10.3390/math11061425
    [34] Y. Aït-Sahalia, P. Mykland, L. Zhang, Ultra high frequency volatility estimation with dependent microstructure noise, J. Economet., 160 (2011), 190–203. https://doi, org/10.2139/ssrn.686131 doi: 10.2139/ssrn.686131
    [35] K. Christensen, S. Kinnebrock, M. Podolskij, Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data, J. Economet., 72 (2010), 885–925. https://doi.org/10.1016/j.jeconom.2010.05.001 doi: 10.1016/j.jeconom.2010.05.001
    [36] C. Dai, K. Lu, D. Xiu, Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data, J. Economet., 208 (2019), 43–79. https://doi.org/10.1016/j.jeconom.2018.09.005 doi: 10.1016/j.jeconom.2018.09.005
    [37] L. Zhang, Estimating Covariation: Epps effect, microstructure noise, J. Economet., 160 (2010), 33–77. https://doi.org/10.1016/j.jeconom.2010.03.012 doi: 10.1016/j.jeconom.2010.03.012
    [38] M. Podolskij, M. Vetter, Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps, Bernoulli, 15 (2009), 634–658. https://doi.org/10.17877/DE290R-7733 doi: 10.17877/DE290R-7733
    [39] J. Jacod, M. Podolskij, M. Vetter, Limit theorems for moving averages of discretized processes plus noise, Ann. Stat., 38 (2010), 1478–1545. https://doi.org/10.1214/09-AOS756 doi: 10.1214/09-AOS756
  • This article has been cited by:

    1. Abdul Ghaffar, Ayyaz Ali, Sarfaraz Ahmed, Saima Akram, Moin-ud-Din Junjua, Dumitru Baleanu, Kottakkaran Sooppy Nisar, A novel analytical technique to obtain the solitary solutions for nonlinear evolution equation of fractional order, 2020, 2020, 1687-1847, 10.1186/s13662-020-02751-5
    2. Haci Mehmet Baskonus, Muzaffer Ercan, Extraction Complex Properties of the Nonlinear Modified Alpha Equation, 2021, 5, 2504-3110, 6, 10.3390/fractalfract5010006
    3. Thilagarajah Mathanaranjan, Solitary wave solutions of the Camassa–Holm-Nonlinear Schrödinger Equation, 2020, 19, 22113797, 103549, 10.1016/j.rinp.2020.103549
    4. Onur Alp Ilhan, M. Nurul Islam, M. Ali Akbar, Construction of Functional Closed Form Wave Solutions to the ZKBBM Equation and the Schrödinger Equation, 2020, 2228-6187, 10.1007/s40997-020-00358-5
    5. Abdulla - Al - Mamun, Samsun Nahar Ananna, Tianqing An, Nur Hasan Mahmud Shahen, , Periodic and solitary wave solutions to a family of new 3D fractional WBBM equations using the two-variable method, 2021, 3, 26668181, 100033, 10.1016/j.padiff.2021.100033
    6. M. Al-Amin, M. Nurul Islam, M. Ali Akbar, Adequate wide-ranging closed-form wave solutions to a nonlinear biological model, 2021, 4, 26668181, 100042, 10.1016/j.padiff.2021.100042
    7. Jiahua Fang, Muhammad Nadeem, Hanan A. Wahash, Arzu Akbulut, A Semianalytical Approach for the Solution of Nonlinear Modified Camassa–Holm Equation with Fractional Order, 2022, 2022, 2314-4785, 1, 10.1155/2022/5665766
    8. Md. Tarikul Islam, Mst. Armina Akter, J. F. Gómez-Aguilar, Md. Ali Akbar, Novel and diverse soliton constructions for nonlinear space–time fractional modified Camassa–Holm equation and Schrodinger equation, 2022, 54, 0306-8919, 10.1007/s11082-022-03602-1
    9. S M Rayhanul Islam, S M Yiasir Arafat, Hanfeng Wang, Abundant closed-form wave solutions to the simplified modified Camassa-Holm equation, 2022, 24680133, 10.1016/j.joes.2022.01.012
    10. Aniqa Zulfiqar, Jamshad Ahmad, Exact solitary wave solutions of fractional modified Camassa-Holm equation using an efficient method, 2020, 59, 11100168, 3565, 10.1016/j.aej.2020.06.002
    11. Ismail Onder, Melih Cinar, Aydin Secer, Mustafa Bayram, Analytical solutions of simplified modified Camassa-Holm equation with conformable and M-truncated derivatives: A comparative study, 2022, 24680133, 10.1016/j.joes.2022.06.012
    12. Mounirah Areshi, Aly R. Seadawy, Asghar Ali, Abdulrahman F. AlJohani, Weam Alharbi, Amal F. Alharbi, Construction of Solitary Wave Solutions to the (3 + 1)-Dimensional Nonlinear Extended and Modified Quantum Zakharov–Kuznetsov Equations Arising in Quantum Plasma Physics, 2023, 15, 2073-8994, 248, 10.3390/sym15010248
    13. M. Al-Amin, M. Nurul Islam, M. Ali Akbar, The closed-form soliton solutions of the time-fraction Phi-four and (2+1)-dimensional Calogero–Bogoyavlenskii–Schiff model using the recent approach, 2022, 5, 26668181, 100374, 10.1016/j.padiff.2022.100374
    14. M. Ayesha Khatun, Mohammad Asif Arefin, M. Hafiz Uddin, Mustafa Inc, Luigi Rodino, Abundant Explicit Solutions to Fractional Order Nonlinear Evolution Equations, 2021, 2021, 1563-5147, 1, 10.1155/2021/5529443
    15. M. Nurul Islam, Onur Alp İlhan, M. Ali Akbar, Fatma Berna Benli, Danyal Soybaş, Wave propagation behavior in nonlinear media and resonant nonlinear interactions, 2022, 108, 10075704, 106242, 10.1016/j.cnsns.2021.106242
    16. Noha M. Rasheed, Mohammed O. Al-Amr, Emad A. Az-Zo’bi, Mohammad A. Tashtoush, Lanre Akinyemi, Stable Optical Solitons for the Higher-Order Non-Kerr NLSE via the Modified Simple Equation Method, 2021, 9, 2227-7390, 1986, 10.3390/math9161986
    17. M. Ayesha Khatun, Mohammad Asif Arefin, M. Ali Akbar, M. Hafiz Uddin, Numerous explicit soliton solutions to the fractional simplified Camassa-Holm equation through two reliable techniques, 2023, 20904479, 102214, 10.1016/j.asej.2023.102214
    18. M. Al-Amin, M. Nurul Islam, Onur Alp İlhan, M. Ali Akbar, Danyal Soybaş, Firdous A. Shah, Solitary Wave Solutions to the Modified Zakharov–Kuznetsov and the (2 + 1)-Dimensional Calogero–Bogoyavlenskii–Schiff Models in Mathematical Physics, 2022, 2022, 2314-4785, 1, 10.1155/2022/5224289
    19. Minzhi Wei, Liping He, Existence of periodic wave for a perturbed MEW equation, 2023, 8, 2473-6988, 11557, 10.3934/math.2023585
    20. Thitthita Iatkliang, Supaporn Kaewta, Nguyen Minh Tuan, Sekson Sirisubtawee, Novel Exact Traveling Wave Solutions for Nonlinear Wave Equations with Beta-Derivatives via the sine-Gordon Expansion Method, 2023, 22, 2224-2880, 432, 10.37394/23206.2023.22.50
    21. Melike Kaplan, Rubayyi T. Alqahtani, Nadiyah Hussain Alharthi, Wave Propagation and Stability Analysis for Ostrovsky and Symmetric Regularized Long-Wave Equations, 2023, 11, 2227-7390, 4030, 10.3390/math11194030
    22. Sujoy Devnath, Shahansha Khan, M. Ali Akbar, Exploring solitary wave solutions to the simplified modified camassa-holm equation through a couple sophisticated analytical approaches, 2024, 59, 22113797, 107580, 10.1016/j.rinp.2024.107580
    23. YANZHI MA, ZENGGUI WANG, BIFURCATION AND EXACT SOLUTIONS OF SPACE-TIME FRACTIONAL SIMPLIFIED MODIFIED CAMASSA–HOLM EQUATION, 2023, 31, 0218-348X, 10.1142/S0218348X23500858
    24. Md. Abde Mannaf, Rajandra Chadra Bhowmik, Mst. Tania Khatun, Md. Ekramul Islam, Udoy S. Basak, M. Ali Akbar, Optical solitons of SMCH model in mathematical physics: impact of wind and friction on wave, 2024, 56, 0306-8919, 10.1007/s11082-023-05641-8
    25. Mrutyunjaya Sahoo, S. Chakraverty, 2024, 9780443154041, 227, 10.1016/B978-0-44-315404-1.00019-9
    26. Ghazala Akram, Maasoomah Sadaf, Saima Arshed, Muhammad Abdaal Bin Iqbal, Simulations of exact explicit solutions of simplified modified form of Camassa–Holm equation, 2024, 56, 1572-817X, 10.1007/s11082-024-06940-4
    27. Md. Tarikul Islam, Shahariar Ryehan, Farah Aini Abdullah, J.F. Gómez-Aguilar, The effect of Brownian motion and noise strength on solutions of stochastic Bogoyavlenskii model alongside conformable fractional derivative, 2023, 287, 00304026, 171140, 10.1016/j.ijleo.2023.171140
    28. Aly R. Seadway, Asghar Ali, Ahmet Bekir, Adem C. Cevikel, Analysis of the(3+1)-Dimensional Fractional Kadomtsev–Petviashvili–Boussinesq Equation: Solitary, Bright, Singular, and Dark Solitons, 2024, 8, 2504-3110, 515, 10.3390/fractalfract8090515
    29. Faisal Yasin, Muhammad Arshad, Ghulam Farid, Mohammad Ali Hoseinzadeh, Hadi Rezazadeh, W-shape and abundant of other solitary wave solutions of the positive Gardner Kadomtsov–Petviashivilli dynamical model with applications, 2024, 56, 1572-817X, 10.1007/s11082-024-06922-6
    30. Aly R. Seadawy, Asghar Ali, Ahmet Bekir, Novel solitary waves solutions of the extended cubic(3+1)-dimensional Schr\ddot{o}dinger equation via applications of three mathematical methods, 2024, 56, 1572-817X, 10.1007/s11082-024-06528-y
    31. Jamilu Sabi’u, Mayssam Tarighi Shaayesteh, Ali Taheri, Hadi Rezazadeh, Mustafa Inc, Ali Akgül, New exact solitary wave solutions of the generalized (3 + 1)-dimensional nonlinear wave equation in liquid with gas bubbles via extended auxiliary equation method, 2023, 55, 0306-8919, 10.1007/s11082-023-04870-1
    32. M. Ashikur Rahman, M. Al-Amin, Mst. Kamrunnaher, M. Abul Kawser, Rajaul Haque, M. Ali Akbar, M. Nurul Islam, Mathematical analysis of some new adequate broad-ranging soliton solutions of nonlinear models through the recent technique, 2024, 9, 26668181, 100634, 10.1016/j.padiff.2024.100634
    33. Hassan Almusawa, Musawa Yahya Almusawa, Adil Jhangeer, Zamir Hussain, A Comprehensive Study of Dynamical Behavior and Nonlinear Structures of the Modified α Equation, 2024, 12, 2227-7390, 3809, 10.3390/math12233809
    34. Md. Asaduzzaman, Farhana Jesmin, Construction of the Closed Form Wave Solutions for TFSMCH and (1 1) Dimensional TFDMBBM Equations via the EMSE Technique+, 2025, 9, 2504-3110, 72, 10.3390/fractalfract9020072
    35. Yimin Liu, Xiaoshan Zhao, Application of homotopy analysis method to solve a class of time-fractional order mCH equations, 2025, 2964, 1742-6588, 012043, 10.1088/1742-6596/2964/1/012043
    36. Kang-Jia Wang, Guo-Dong Wang, Feng Shi, Xiao-Lian Liu, Hong-Wei Zhu, Variational principle, Hamiltonian, bifurcation analysis, chaotic behaviors and the diverse solitary wave solutions of the simplified modified Camassa–Holm equation, 2025, 22, 0219-8878, 10.1142/S0219887825500136
    37. H. A. Ashi, Noufe H. Aljahdaly, Investigation of the damped geophysical KdV equation using the explicit exponential time differencing method, 2025, 81, 1573-0484, 10.1007/s11227-025-07289-5
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1840) PDF downloads(66) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog