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Research article

Impact of risks on forced CEO turnover

  • Received: 05 March 2022 Revised: 26 April 2022 Accepted: 28 April 2022 Published: 07 May 2022
  • JEL Codes: G32, G34, M12

  • Risk management has been an important topic since the 2008 financial crisis, and it has become an important area of focus in business management. It is important for the board of directors to evaluate the ability and competence of the CEO. This study was aimed to investigate the effect of various risks on forced CEO turnover through the use of a linear probability model. The Chinese A-share market from 2010 to 2019 was selected as the sample, and theoretical analysis and empirical research were combined to explore the impact of various risks on forced CEO turnover, further analyzes the relationship under different ownerships. This paper study revealed that the crash risk is positively associated with forced CEO turnover. This paper also found that the idiosyncratic risk increases the likelihood of forced CEO turnover, and that the relationship is more significant in non- state-owned enterprises (non-SOEs) than state-owned enterprises (SOEs). The systematic risk has no effect on forced CEO turnover. Risks can be an important indicator of the CEO's ability and competence. This paper also evaluated the relationships in Chinese circumstances. China is an emerging market that has a different legal and social environment than other countries. The different goals of SOEs and non-SOEs lead to different risk attitudes. It is necessary to distinguish ownership when evaluating the Chinese situation.

    Citation: Xue Chang. Impact of risks on forced CEO turnover[J]. Quantitative Finance and Economics, 2022, 6(2): 177-205. doi: 10.3934/QFE.2022008

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  • Risk management has been an important topic since the 2008 financial crisis, and it has become an important area of focus in business management. It is important for the board of directors to evaluate the ability and competence of the CEO. This study was aimed to investigate the effect of various risks on forced CEO turnover through the use of a linear probability model. The Chinese A-share market from 2010 to 2019 was selected as the sample, and theoretical analysis and empirical research were combined to explore the impact of various risks on forced CEO turnover, further analyzes the relationship under different ownerships. This paper study revealed that the crash risk is positively associated with forced CEO turnover. This paper also found that the idiosyncratic risk increases the likelihood of forced CEO turnover, and that the relationship is more significant in non- state-owned enterprises (non-SOEs) than state-owned enterprises (SOEs). The systematic risk has no effect on forced CEO turnover. Risks can be an important indicator of the CEO's ability and competence. This paper also evaluated the relationships in Chinese circumstances. China is an emerging market that has a different legal and social environment than other countries. The different goals of SOEs and non-SOEs lead to different risk attitudes. It is necessary to distinguish ownership when evaluating the Chinese situation.



    The description of a variety of natural phenomena and engineering problems deal with incompressible quasi-Newtonian flows with viscous heating and buoyancy terms (often referred to as natural convection of fluids). Mantle convection with very large viscosities, waves and currents near shorelines, heat transfer in nanoparticle fluids, creeping thermal plumes, stratified oceanic flows, chemical reactors, and many other examples can be invoked in the context of applicative problems, partial differential equations, and in the construction of numerical schemes [17,32,26,18,22,30,33,40]. In particular, finite element methods approximating the solution of these equations have been developed by the mathematical community in the last two decades. For instance, the model with constant coefficients has been addressed via primal approaches in [13,11,19,23], whereas mixed-type schemes have been employed in [20,24,25], and in particular two different formulations based on a dual-mixed approach for the momentum equation, and a primal and mixed-primal one for the energy equation, are proposed in [21].

    Here we advocate to the study of well-posedness and stability of a weak formulation (as well as the construction of mixed finite element schemes) for the Boussinesq equations with thermally-dependent viscosity. In this regard, we remark that not many finite element methods that provide analysis for the case of non-constant viscosity are available in the literature (we may basically refer to [2,3,5,41,34,36,39,42,43], and some of the references therein). For instance, the authors in [36] (see [35] for the continuous analysis) propose an optimally convergent, primal finite element method for the steady-state problem applied to the flow in channels. In turn, [39] (and a stabilized version of their method, which is suggested in [42]) deal with the unsteady problem, where backward Euler discretization is used in time, and conforming finite elements in space. On the other hand, a conforming finite element method for the problem with temperature-dependent viscosity and thermal conductivity is developed in [34]. In this work, Stokes-stable elements for the velocity and pressure, as well as Lagrange elements for the temperature, and discontinuous piecewise polynomials for the normal heat flux through the boundary, are used to define the associated discrete scheme. Similar hypotheses to those in [34] are also required in primal formulations that may be computationally less expensive than a mixed method, but do not approximate directly other variables of physical interest. The user of a primal scheme would usually appeal to numerical differentiation, with the consequent loss of accuracy.

    Furthermore, just a couple of the above mentioned references consider dual-mixed approaches. In particular, in the recent contribution [5] the authors construct an augmented mixed-primal finite element method for the steady Boussinesq problem without dissipation, restricting the analysis to two-dimensional bounded domains with polygonal boundary. More precisely, in the present case one seeks a velocity field u, a pressure field p and a temperature field φ such that

    div(μ(φ)e(u))+(u)u+pφg=0inΩ, (1.1a)
    divu=0inΩ, (1.1b)
    div(Kφ)+uφ=0inΩ, (1.1c)
    u=uDandφ=φDonΓ, (1.1d)

    where ΩRn, n{2,3}, is a domain with boundary Γ:=Ω, div is the usual divergence operator div acting along the rows of a given tensor, e(u) denotes the strain rate tensor (symmetric part of the velocity gradient tensor u), gL(Ω):=[L(Ω)]n is a body force per unit mass (e.g., gravity), KL(Ω):=[L(Ω)]n×n is a uniformly positive definite tensor describing thermal conductivity and μ:RR+ is a temperature-dependent viscosity, which is assumed bounded and Lipschitz continuous, that is, there exist constants μ2μ1>0 and Lμ>0 such that

    μ1μ(s)μ2 sR,and|μ(s)μ(t)|Lμ|st| s,tR. (1.2)

    The viscosity can be either of Reynolds, Arrhenius, or Sutherland's type, see e.g. [32,35,33]. Note that a viscosity dissipation term σ:u and the power of heat production should also be present in the thermal energy equation. However these contributions are assumed much smaller than heat conduction effects (see e.g. [17]). In the Boussinesq approximation it is also assumed that density fluctuations are linearly related to temperature, and so the last term on the momentum balance is the buoyancy contribution that contains a rescaling implying that, at least for the analysis of the problem, the adimensional number Ra/(Pr Re2) is of the order of the gravity magnitude. In addition, and for sake of notational simplicity of the analysis, here we also take a zero reference temperature and assume that other constants can be absorbed by the model adimensionalization.

    With respect to the boundary conditions for (1.1), we assume that uDH1/2(Γ):=[H1/2(Γ)]n, φDH1/2(Γ), and that uD verifies the compatibility condition

    ΓuDν=0, (1.3)

    where ν denotes the unit outward normal on Γ. The construction of the mixed-primal formulation in [5] begins with the introduction of the stress and vorticity tensors, respectively, defined as

    σ:=μ(φ)e(u)uupIandγ:=12{u(u)t}L2skew(Ω), (1.4)

    where stands for the tensor product operator, I is the identity matrix in Rn×n, the symbol t denotes matrix transpose, and

    L2skew(Ω):={ηL2(Ω):η+ηt=0}. (1.5)

    Note that if we had Neumann boundary conditions in the fluid, then σν would be prescribed on Γ. Therefore, after eliminating the pressure p, problem (1.1) is rewritten as: Find (σ,u,γ,φ) such that

    5uγ1μ(φ) (uu)d=1μ(φ) σdinΩ, (1.6a)
    divσ φg=0inΩ, (1.6b)
    div(Kφ)+uφ=0inΩ, (1.6c)
    u=uDandφ=φDonΓ, (1.6d)
    Ωtr(σ+uu)=0, (1.6e)

    where tr denotes the matrix trace, τd:=τ1ntr(τ)I is the deviatoric tensor of a given τL2(Ω), and (1.6e) constitutes a uniqueness condition for the pressure. At this point we remark that, because of the division by μ(φ) in (1.6a), integration by parts (after multiplication by a test function) is now possible. However, it can be seen that this leads to the usage of a continuous injection from H1(Ω) into L8(Ω), as required by the following estimate:

    Ω|φ(uw)d:τ|φL4(Ω)uL8(Ω)wL8(Ω)τ0,ΩC(Ω)φ1,Ωu1,Ωw1,Ωτ1,Ω,

    with C(Ω)>0 and valid for any φH1(Ω); u,wH1(Ω); τL2(Ω), and more important, for ΩRd, d{1,2}, according to the Sobolev embedding theorem (cf., e.g., [37,Theorem 1.3.5]). This estimate is used in several ways throughout [5], at both continuous and discrete levels (see, [5,Lemmas 3.8,4.5 and 5.3]), and its main purpose is to help in the proof of Lipschitz continuity of the fixed-point operator T (respectively its discrete version Th) that consequently provides well-posedness of the continuous formulation (respectively the Galerkin scheme).

    One of the purposes of this work is to derive a new augmented method for (1.1) that remains valid also for the 3D case. To this end, one can look at e.g. [15] where the strain rate tensor e(u) is considered as a new unknown, in addition to either the stress (or pseudostress) and vorticity tensors. This provides more flexibility in the scheme, as it is no longer necessary (nor much convenient) to divide in (1.6a) by the viscosity to set the gradient free. Instead, the decomposition of the velocity gradient into its symmetric and skew-symmetric parts provides an equation to be integrated by parts. However, proceeding a bit differently than in those works, here we do not impose the symmetry of the stress by testing against the whole space of the skew symmetric tensors, but only against a proper subspace of it, thus yielding what we call an ultra-weak imposition of such constraint. As a consequence, we do not require the vorticity as a further unknown (also interpreted as an associated Lagrange multiplier), and hence the resulting mixed scheme has fewer degrees of freedom without affecting the accuracy of the approximate solutions. Moreover, the pressure and the vorticity are easily recovered by postprocessing formulae that provide the same rates of convergence of the main unknowns. In terms of the analysis, we can suitably modify the approach from [5], thus giving rise to an augmented mixed-primal finite element method using discontinuous piecewise polynomial functions of degree k to approximate the strain rate and normal heat flux, Raviart-Thomas elements of order k for the stress, and Lagrange elements of order k+1 for the velocity and temperature. We stress that the already described extension to the 3D case, the new way of imposing the symmetry of the stress with the consequent decrease in the number of unknowns, and the availability of the aforementioned postprocessing formulae, constitute the main contributions of the present work. Let us also clarify that rather than proposing a cheap method for any reformulation or simplification of the governing equations, our underlying motivation is to rigorously derive properties of numerical schemes that preserve the mixed structure of the PDE system.

    The rest of this work is organized as follows. In Section 2 we rewrite the Boussinesq problem (1.1) considering the strain rate and stress tensors as new variables, to then derive an augmented mixed-primal formulation whose well-posedness is proved by means of a fixed-point approach. Similarly, in Section 3 we provide the corresponding Galerkin scheme and its associated well-posedness, to then, in Section 4, proceed to derive a priori error estimates and state the respective rates of convergence, including those for the postprocessed approximations of pressure and vorticity, when a particular choice of finite element subspaces is made. Finally, to complement our theoretical results, we present in Section 5 a set of numerical examples that serve to confirm the properties of the proposed schemes.

    In order to avoid the division by μ(φ) in (1.6a), we have a look at recent work [15], where the authors develop a augmented mixed finite element method for the Navier-Stokes equations with nonlinear viscosity. This approach relies on the definition of the strain rate tensor as a new unknown

    t:=e(u)L2tr(Ω), (2.1)

    where

    L2tr(Ω)={sL2(Ω):s=standtr(s)=0}.

    In addition, for each vH1(Ω) we let η(v) be the skew-symmetric part of the velocity gradient tensor v, that is

    η(v):=12{v(v)t}, (2.2)

    which certainly lies in L2skew(Ω) (cf. (1.5)). In this way, bearing in mind (2.1), and noting from (1.4) and (2.2) that γ=η(u), the system (1.6) can be rewritten as: Find (t,σ,u,φ) such that

    t+η(u)=uin Ω, (2.3a)
    μ(φ)t(uu)d=σdin Ω, (2.3b)
    divσ φg=0in Ω, (2.3c)
    div(Kφ)+uφ=0in Ω, (2.3d)
    u=uDon Γ, (2.3e)
    φ=φDon Γ, (2.3f)
    Ωtr(σ+uu)=0. (2.3g)

    Before continuing in the following section with the derivation of the mixed-primal formulation of (2.3), we find it important to remark at this point that it is also possible to account the case of a nonlinear thermal conductivity K in the forthcoming analysis. In particular, within the context of coupled flow-transport and sedimentation-consolidation models, in which the equation for the concentration φ reads as the present one for the energy, we may refer to [8,eq. (2.1)] and [9,eq. (2.1)], where diffusion coefficients depending on φ and φ, respectively, were considered for the corresponding analyses. More recently, a slight modification of the present model (1.1), in which K was allowed to depend on the temperature φ, namely in the form κ(φ), with κ:RR+, was introduced and analyzed in [4] by using a fully-mixed variational formulation. In this case, the pseudoheat vector p:=κ(φ)φφu and the temperature gradient are introduced as auxiliary unknowns, so that the energy equation is rewritten as ζ=φ,p:=κ(φ)ζφu,anddiv(p)=0inΩ. Further details on the analysis of such fully-mixed approach are available in [4].

    Multiplying (2.3a) by a test function τH(div;Ω), integrating by parts and using the Dirichlet condition (2.3e), we obtain

    Ωt:τd+Ωη(u):τ+Ωudivτ=τν,uDΓ τH(div;Ω).

    Then, we multiply (2.3b) and (2.3c) by appropriate test functions, imposing at the same time the symmetry of the stress tensor σ in the form

    Ωσ:η(v)=0vH1(Ω), (2.4)

    thus obtaining

    Ωμ(φ)t:sΩ(uu)d:sΩσd:s=0 sL2tr(Ω), (2.5)

    and

    ΩvdivσΩσ:η(v)=Ωφgv vH1(Ω).

    We highlight that (2.4) constitutes what we call an ultra-weak imposition of the symmetry of σ since {η(v): vH1(Ω)} is a proper subspace of L2skew(Ω). This idea has also been applied in [7].

    The weak form of the energy conservation equation is recalled next from [5]:

    ΩKφψ+λ,ψΓ=Ωψuφ ψH1(Ω), (2.6)
    ξ,φΓ=ξ,φDΓ ξH1/2(Γ), (2.7)

    where λ:=KφνH1/2(Γ) is a Lagrange multiplier taking care of the non-homogeneous Dirichlet boundary condition. Notice that, due to the second term in (2.5) and the right hand side of (2.6), the velocity u must live in H1(Ω), since appealing to the continuous injection of H1(Ω) into L4(Ω), there exist positive constants c1(Ω) and c2(Ω) such that

    |Ω(uw)d:s|c1(Ω)u1,Ωw1,Ωs0,Ω u,wH1(Ω),  sL2(Ω), (2.8)

    and

    |Ωψuφ|c2(Ω)u1,Ωψ1,Ω|φ|1,Ω uH1(Ω) φ,ψH1(Ω). (2.9)

    Also, obeying to the orthogonal decomposition

    H(div;Ω)=H0(div;Ω)RI,

    where

    H0(div;Ω):={ζH(div;Ω):Ωtr(ζ)=0},

    we can consider σ and τ in H0(div;Ω) (see [5,Lemma 3.1] for a detailed justification of this change). Having in mind these considerations, at a first glance, the weak formulation reads: Find (t,σ,u,φ,λ)L2tr(Ω)×H0(div;Ω)×H1(Ω)×H1(Ω)×H1/2(Γ) such that

    Ωμ(φ)t:sΩ(uu)d:sΩσd:s=0 sL2tr(Ω),Ωt:τd+Ωη(u):τ+Ωudivτ=τν,uDΓ τH0(div;Ω),ΩvdivσΩσ:η(v)=Ωφgv vH1(Ω),ΩKφψ+λ,ψΓ=Ωψuφ ψH1(Ω),ξ,φΓ=ξ,φDΓ ξH1/2(Γ). (2.10)

    To achieve a conforming scheme, and to properly analyze (2.10), we augment this variational formulation using Galerkin terms arising from (2.3), but tested differently from (2.10), namely:

    κ1Ω{σd+(uu)dμ(φ)t}:τd=0 τH0(div;Ω),κ2Ω{divσ+φg}divτ=0 τH0(div;Ω),κ3Ω{e(u)t}:e(v)=0 vH1(Ω),κ4Γuv=κ4ΓuDv vH1(Ω),

    where κj, j{1,,4} are stabilization (or augmentation) constants to be specified later on. In this way, denoting by H:=L2tr(Ω)×H0(div;Ω)×H1(Ω), tt:=(t,σ,u) and ts:=(s,τ,v), we arrive at the following augmented mixed-primal formulation: Find (tt,(φ,λ))H×H1(Ω)×H1/2(Γ) such that

    Aφ(tt,ts)+Bu(tt,ts)=Fφ(ts)+FD(ts) tsH,a(φ,ψ)+b(ψ,λ)=Fu,φ(ψ) ψH1(Ω),b(φ,ξ)=G(ξ) ξH1/2(Γ), (2.11)

    where, given an arbitrary (w,ϕ)H1(Ω)×H1(Ω), the forms Aϕ, Bw, a, b, and the functionals FD, Fϕ, Fw,ϕ and G are defined as

    Aϕ(tt,ts):=Ωμ(ϕ)t:{sκ1τd}+Ωt:{τdκ3e(v)}Ωσd:{sκ1τd}+ΩudivτΩvdivσ+Ωη(u):τΩσ:η(v)+κ2Ωdivσdivτ+κ3Ωe(u):e(v)+κ4Γuv, (2.12)
    Bw(tt,ts):=Ω(uw)d:{κ1τds}, (2.13)

    for all tt,tsH;

    a(φ,ψ):=ΩKφψ, (2.14)

    for all φ,ψH1(Ω);

    b(ψ,ξ):=ξ,ψΓ, (2.15)

    for all (ψ,ξ)H1(Ω)×H1/2(Γ);

    FD(ts):=τν,uDΓ+κ4ΓuDv, (2.16)
    Fϕ(ts):=Ωϕg{vκ2divτ}, (2.17)

    for all tsH;

    Fw,ϕ(ψ)=Ωψwϕ, (2.18)

    for all ψH1(Ω); and

    G(ξ)=ξ,φDΓ, (2.19)

    for all ξH1/2(Γ).

    A crucial tool in [5] to prove the well-posedness of the continuous and discrete formulations is a technique that decouples the problem into the mixed formulation of the momentum equation and the primal formulation of the energy equation, which further enables us to rewrite the formulation as a fixed-point problem. Hence, we denote H:=H1(Ω)×H1(Ω) and consider in what follows the operator S:HH defined by

    S(w,ϕ)=(S1(w,ϕ),S2(w,ϕ),S3(w,ϕ)):=tt,

    where tt is the solution of the problem: Find ttH such that

    Aϕ(tt,ts)+Bw(tt,ts)=Fϕ(ts)+FD(ts) tsH. (2.20)

    In addition, let ˜S:HH1(Ω) be the operator defined by

    ˜S(w,ϕ):=φ,

    where φ is the first component of a solution to: Find (φ,λ)H1(Ω)×H1/2(Γ) such that

    a(φ,ψ)+b(ψ,λ)=Fw,ϕ(ψ) ψH1(Ω),b(φ,ξ)=G(ξ) ξH1/2(Γ). (2.21)

    In this way, by introducing the operator T:HH as

    T(w,ϕ)=(S3(w,ϕ),˜S(S3(w,ϕ),ϕ)) (w,ϕ)H, (2.22)

    we realize that (2.11) can be rewritten as the fixed-point problem: Find (u,φ)H such that

    T(u,φ)=(u,φ). (2.23)

    As in [5], the objective is to use the Banach fixed-point theorem to prove existence and uniqueness of (2.23). We recall that the key difference in the present work with respect to [5] is in the problem that defines the operator S, and therefore, those results associated to the operator ˜S and the primal formulation of the energy equation will be considered here as well, but we only cite them.

    In what follows, we consider the norms

    ts:={s20,Ω+τ2div;Ω+v21,Ω}1/2 tsH,(ψ,ξ):={ψ21,Ω+ξ21/2,Γ}1/2 (ψ,ξ)H1(Ω)×H1/2(Γ).

    We first recall some results that will be used for ellipticity purposes.

    Lemma 2.1. There exists c3(Ω)>0 such that

    c3(Ω)τ020,Ωτd20,Ω+divτ20,Ω τ=τ0+cIH(div;Ω).

    Proof. See [14,Proposition 3.1], [28,Lemma 2.3].

    Lemma 2.2. There exists ϑ0(Ω)>0 such that

    ϑ0(Ω)v21,Ωe(v)20,Ω+v20,Γ vH1(Ω).

    Proof. See [27,Lemma 3.1].

    The following results establish sufficient conditions for the operators S and ˜S being well-defined, equivalently, (2.20) and (2.21) being well-posed.

    Lemma 2.3. Assume that for δ1(0,2μ2) and δ2(0,2) we choose

    κ1(0,2μ1δ1μ2),κ2,κ4(0,),andκ3(0,2δ2(μ1κ1μ22δ1)).

    Then, there exists r0>0 such that for each r(0,r0), the problem (2.20) has a unique solution tt:=S(w,ϕ)H for each (w,ϕ)H such that w1,Ωr. Moreover, there exists a constant CS>0, independent of (w,ϕ) and r, such that there holds

    S(w,ϕ)=ttCS{g,Ωϕ0,Ω+uD1/2,Γ}. (2.24)

    Proof. Given (w,ϕ)H, we notice from (2.12) that Aϕ is bilinear. Then, by using the upper bound of the viscosity function, the Cauchy-Schwarz inequality and the trace theorem with constant c0(Ω), we see that for any tt,tsH,

    |Aϕ(tt,ts)|μ2(1+κ21)1/2t0,Ωts+(1+κ23)1/2t0,Ωts+(1+κ21)1/2σd0,Ωts+u0,Ωdivτ0,Ω+η(u)0,Ωτ0,Ω+divσ0,Ωv0,Ω+σ0,Ωη(v)0,Ω+κ2divσ0,Ωdivτ0,Ω+κ3u1,Ωv1,Ω+κ4c0(Ω)2u1,Ωv1,Ω,

    and therefore, there exists a constant CA>0 depending only on μ2, c0(Ω) and the stabilization parameters κj, such that

    |Aϕ(tt,ts)|CAttts tt,tsH. (2.25)

    On the other hand, using (2.13) and (2.8), we obtain that for any tt:=(t,σ,u),ts:=(s,τ,v)H there holds

    |Bw(tt,ts)|=|Ω(uw)d:{κ1τds}|c1(Ω)(1+κ21)1/2u1,Ωw1,Ωts, (2.26)

    which together with (2.25) implies the existence of a positive constant denoted by A+B, independent of (w,ϕ) such that

    |(Aϕ+Bw)(tt,ts)|A+Bttts. (2.27)

    To prove that Aϕ+Bw is elliptic, we first prove that Aϕ is elliptic. Indeed, for any tsH we have

    Aϕ(ts,ts)=Ωμ(ϕ)s:sκ1Ωμ(ϕ)s:τdκ3Ωs:e(v)+κ1τd20,Ω+κ2divτ20,Ω+κ3e(v)20,Ω+κ4v20,Γ,

    and then, using the bounds for the viscosity and the Cauchy-Schwarz and Young inequalities, we obtain for any δ1,δ2>0 and any tsH that

    Aϕ(ts,ts)μ1s20,Ωκ1μ22δ1s20,Ωκ1μ2δ12τd20,Ωκ32δ2s20,Ωκ3δ22e(v)20,Ω+κ1τd20,Ω+κ2divτ20,Ω+κ3e(v)20,Ω+κ4v20,Γ(μ1κ1μ22δ1κ32δ2)s20,Ω+κ1(1μ2δ12)τd20,Ω+κ2divτ20,Ω+κ3(1δ22)e(v)20,Ω+κ4v20,Γ.

    Then, defining the following constants:

    α1:=μ1κ1μ22δ1κ32δ2,α2:=min{κ1(1μ2δ12),κ22},α3:=min{κ3(1δ22),κ4},α4:=min{α2c3(Ω),κ22},α5:=α3ϑ0(Ω),

    and using Lemmas 2.1 and 2.2, it is possible to find a constant α(Ω):=min{α1,α4,α5}, independent of (w,ϕ), such that

    Aϕ(ts,ts)α(Ω)ts2 tsH.

    Combining the foregoing inequality with (2.26), we get that, for any tsH, there holds

    (Aϕ+Bw)(ts,ts)(α(Ω)c1(Ω)(1+κ21)1/2w1,Ω)ts2.

    Therefore, we easily see that

    (Aϕ+Bw)(ts,ts)α(Ω)2ts2 tsH, (2.28)

    provided that

    α(Ω)2c1(Ω)(1+κ21)1/2w1,Ω,

    that is,

    w1,Ωα(Ω)2c1(Ω)(1+κ21)1/2=:r0, (2.29)

    thus proving ellipticity for Aϕ+Bw under the requirement (2.29). Finally, the linearity of the functionals FD and Fϕ is clear, and from (2.16), (2.17), using the Cauchy-Schwarz inequality, the trace estimates in H(div;Ω) and H1(Ω), with constants 1 and c0(Ω), and the continuous injection from H1/2(Γ) into L2(Γ) with constant C1/2 we have

    |FD(ts)|(1+κ4c0(Ω)C1/2)uD1/2,Γts,|Fϕ(ts)|(1+κ22)1/2g,Ωϕ0,Ωts, (2.30)

    for all tsH. Thus, there exists a constant MS:=max{(1+κ22)1/2,1+κ4c0(Ω)C1/2} such that

    Fϕ+FDMS{g,Ωϕ0,Ω+uD1/2,Γ},

    and by the Lax-Milgram theorem (see, e.g. [28,Theorem 1.1]), there exists a unique ttH solution of (2.20), and (2.24) is satisfied with CS:=2MSα(Ω), a constant clearly independent of (w,ϕ). In addition, the fact that α(Ω) and MS are both independent of r guarantees that CS does not depend on r either.

    We stress here that, while the viscosity μ could be assumed to be a monotone decreasing function of the temperature ϕ, this fact by itself would not help to simplify the solvability analysis of (2.20) (which defines the operator S), since ϕ does not form part of the corresponding unknowns, but it actually constitutes a datum for that problem. Indeed, all what one needs from μ for the proof of Lemma 2.3 are its upper and lower bounds. A similar remark is valid for the Lipschitz-continuity of S (cf. Lemma 2.6 below) in which the homonimous property of the viscosity plays a key role. As it will become clear later on, the smallness assumptions on the data for the well-posedness of our problem arises from the main properties of the resulting fixed point operator, and particularly from those inherited from S, so that the eventual monotonicity of μ does not help to remove those conditions either.

    Lemma 2.4. For each (w,ϕ)H there exists a unique pair (φ,λ)H1(Ω)×H1/2(Γ) solution of the problem (2.21). Moreover, there exists C˜S>0, independent of (φ,λ) and r, such that

    ˜S(w,ϕ)(φ,λ)C˜S{w1,Ω|ϕ|1,Ω+φD1/2,Γ}. (2.31)

    Proof. The results comes from a direct application of the Babuška-Brezzi theory (see [5,Lemma 3.6] or more precisely [20,Lemma 3.4]). In particular, the right-hand side of (2.31) is obtained after bounding the functionals Fw,ϕ (cf. (2.18)) and G (cf. (2.19)), respectively, whose resulting bounds are independent of r (cf. [20,eq. (3.39)] for Fw,ϕ). In this way, since the constants yielding the inf-sup condition of b and the ellipticity of a in the kernel of b, are both independent of r, the corresponding continuous dependence result for (2.21) confirms that C˜S does not depend on r either.

    From the previous two lemmas, it is clear that T is well-defined for any pair (w,ϕ)W, where

    W:={(w,ϕ)H:(w,ϕ)r}, (2.32)

    which is nothing but the closed ball in H with center (0,0) and radius r(0,r0), with r0 defined in (2.29). We also notice that a particular choice of stabilization parameters is necessary. We begin by selecting the middle points of the ranges for δ1, δ2, κ1 and κ3, that is,

    δ1=1μ2,δ2=1,κ1=μ1δ1μ2=μ1μ22,κ3=δ2(μ1κ1μ22δ1)=μ12,

    to then pick κ2 and κ4 so that α2 and α3 can attain the largest value possible, that is

    κ2=2κ1(1μ2δ12)=μ1μ22,andκ4=κ3(1δ22)=μ14.

    We stress here that the foregoing feasible choices of κj, j{1,2,3,4}, are explicitly computable since they do not depend on the usually unknown constants c3(Ω) and ϑ0(Ω) from Lemmas 2.1 and 2.2, respectively, but only on the known constants μ1 and μ2 bounding the viscosity (cf. (1.2)).

    Although the problem that defines S (i.e. (2.20)) is well-posed, a small further-regularity assumption is needed in order to continue with the analysis. Inspired by [8], we assume that uDH1/2+ε(Γ) for some ε(0,1) when n=2, or ε[12,1) when n=3, and that for each (z,ψ)H with z1,Ωr, r>0 given, there hold (q,ζ,v):=S(z,ψ)L2tr(Ω)Hε(Ω)×H0(div;Ω)Hε(Ω)×H1+ε(Ω) and

    qε,Ω+ζε,Ω+v1+ε,Ω˜CS(r){g,Ωψ0,Ω+uD1/2+ε,Γ}, (2.33)

    with ˜CS(r)>0 independent of z but depending on the upper bound r of its H1-norm.

    Throughout the rest of the paper we assume that the regularity hypotheses of the previous section are valid. We now proceed to directly fulfill the hypotheses of the Banach fixed-point theorem. The following result shows that T can map the ball W into itself.

    Lemma 2.5. Consider the closed ball W defined in (2.32) with r(0,r0) and r0 as given in (2.29). Suppose the data satisfy

    c(r){g,Ω+uD1/2,Γ}+C˜SφD1/2,Γr,

    where

    c(r):=(1+C˜Sr)CSmax{1,r},

    and CS, C˜S are given in Lemmas 2.3 and 2.4, respectively. Then, there holds T(W)W.

    Proof. It follows as a consequence of the continuous dependence results (2.24) and (2.31), much in an identical way as in [5,Lemma 3.7].

    Next, we prove some results that will help us to arrive at the Lipschitz continuity of T.

    Lemma 2.6. Let r(0,r0) with r0 as given in (2.29). Then, there exists a positive constant ˆCS, independent of r, such that

    S(w1,ϕ1)S(w2,ϕ2)ˆCS{S1(w1,ϕ1)ε,Ωϕ1ϕ2Ln/ε(Ω)+S3(w1,ϕ1)1,Ωw1w21,Ω+g,Ωϕ1ϕ20,Ω}, (2.34)

    for all (w1,ϕ1),(w2,ϕ2)H such that w11,Ω,w21,Ωr.

    Proof. Let (w1,ϕ1),(w2,ϕ2)H as indicated and let ttj:=(tj,σj,uj)=S(wj,ϕj)H, j{1,2} be the corresponding solutions of (2.20). Then, adding and subtracting the equality

    Aϕ1(tt1,ts)+Bw1(tt1,ts)=Fϕ1(ts)+FD(ts)tsH,

    we find that

    (Aϕ2+Bw2)(tt1tt2,ts)=Aϕ2(tt1,ts)Aϕ1(tt1,ts)+Bw2w1(tt1,ts)+Fϕ1ϕ2(ts)tsH.

    Thus, using the ellipticity of Aϕ2+Bw2 (cf. (2.28)) and the foregoing expression, we obtain

    α(Ω)2tt1tt2(Aϕ2+Bw2)(tt1tt2,tt1tt2)[2ex]=Ω{μ(ϕ2)μ(ϕ1)}t1:{(t1t2)κ1(σ1σ2)d}[2ex]+Ω{u1(w2w1)}d:{κ1(σ1σ2)d(t1t2)}[2ex]+Ω(ϕ1ϕ2)g{(u1u2)κ2div(σ1σ2)}. (2.35)

    First, we bound the last two terms in the same way as Lemma 2.3 (that is, using (2.26) and (2.30)):

    |Ω{u1(w2w1)}d:{κ1(σ1σ2)d(t1t2)}|[2ex]c1(Ω)(1+κ21)1/2u11,Ωw1w21,Ωtt1tt2, (2.36)

    and

    |Ω(ϕ1ϕ2)g{(u1u2)κ2div(σ1σ2)}|[2ex](1+κ22)1/2g,Ωϕ1ϕ20,Ωtt1tt2, (2.37)

    Next, for the first term, we use the Lipschitz continuity of μ and the Cauchy-Schwarz and Hölder inequalities to show in a similar way to [5,Eq. (3.56)] that

    |Ω{μ(ϕ2)μ(ϕ1)}t1:{(t1t2)κ1(σ1σ2)d}|Lμ(ϕ2ϕ1)t10,Ω(t1t2)κ1(σ1σ2)d0,Ω[2ex]Lμ(1+κ21)1/2ϕ2ϕ1L2q(Ω)t1L2p(Ω)tt1tt2, (2.38)

    with p,q[1,+) such that 1p+1q=1. We then take into consideration the further-regularity assumed in Section 2.4, and recall from the Sobolev embedding theorem that Hε(Ω) is continuously embedded into L2p(Ω), with

    2p={21εif n=2,632εif n=3,

    (cf. [1,Theorem 4.12], [37,Theorem 1.3.4]) meaning that there exists Cε>0 such that

    tL2p(Ω)Cεtε,Ω tHε(Ω).

    In this way,

    2q=2pp1={2εif n=2,3εif n=3=nε,

    and (2.38) now yields

    |Ω{μ(ϕ2)μ(ϕ1)}t1:{(t1t2)κ1(σ1σ2)d}|[2ex]Lμ(1+κ21)1/2Cεt1ε,Ωϕ1ϕ2Ln/ε(Ω)tt1tt2. (2.39)

    Therefore, putting (2.36), (2.37) and (2.39) together into (2.35), we obtain

    tt1tt2ˆCS{t1ε,Ωϕ1ϕ2Ln/ε(Ω)+u11,Ωw1w21,Ω+g,Ωϕ1ϕ20,Ω},

    with

    ˆCS:=2α(Ω)max{Lμ(1+κ21)1/2Cε,c1(Ω)(1+κ21)1/2,(1+κ22)1/2},

    and since t1=S1(w1,ϕ1) and u1=S3(w1,ϕ1), the last inequality is exactly the estimate (2.34). We end the proof by noting that the foregoing expression defining ˆCS confirms that this constant is independent of r.

    We emphasize once again that, differently from [5], the present approach allows to handle both the 2D and 3D cases for the Boussinesq model with temperature-dependent viscosity. Indeed, notice how the foregoing Lemma shows the main difference with respect to [5]: the tensor-product term in (2.36) is no longer multiplied by an H1-term, thus avoiding the use of the injection H1(Ω)L8(Ω) (not ensured for ΩR3) when splitting them as in [5,Eq. (3.54)], yielding a more robust formulation for the two and three-dimensional cases. On the other hand, the analogous result for ˜S remains intact.

    Lemma 2.7. There exists a positive constant ˆC˜S, independent of r, such that

    ˜S(w1,ϕ1)˜S(w2,ϕ2)ˆC˜S{w11,Ω|ϕ1ϕ2|1,Ω+w1w21,Ω|ϕ2|1,Ω}, (2.40)

    for all (w1,ϕ1),(w2,ϕ2)H.

    Proof. We refer to [20,Lemma 3.7] for full details. We just remark here that the resulting constant ˆC˜S depends on the ellipticity constant of a in the kernel of b, and on the constant c(Ω) arising from the boundedness estimates of the functionals Fw1,ϕ1ϕ2 and Fw1w2,ϕ2, namely (cf. [20,eq. (3.39)]):

    Fw,ϕc(Ω)w1,Ω|ϕ|1,Ω(w,ϕ)H,

    from which it is clear that ˆC˜S does not depend on r.

    As a consequence of the previous Lemmas, T is a Lipschitz continuous operator, as shown next.

    Lemma 2.8. Let r(0,r0), with r0 given as in (2.29). Then, there exists a constant CT>0 depending on r, such that

    T(w1,ϕ1)T(w2,ϕ2)CT{g,Ω+uD1/2+ε,Γ}(w1,ϕ1)(w2,ϕ2),

    for all (w1,ϕ1),(w2,ϕ2)W.

    Proof. The result comes from the definition of T (cf. (2.22)) and the estimates obtained in the previous two lemmas (cf. (2.34) and (2.40)) in an identical way to [20,Lemma 3.8] (see also [5,Lemma 3.10]). We omit further details.

    In summary, from Lemmas 2.3 and 2.4, T:WW is well-defined and does map the ball into itself (thanks to Lemma 2.5). Then, Lemma 2.8 shows that the operator T is Lipschitz continuous with a data-depending constant given by CT{g,Ω+uD1/2+ε,Γ}. When this Lipschitz constant is less than 1, the existence and uniqueness of a fixed point follows thanks to the Banach fixed-point theorem. In this regard, we stress that the dependence of CT on a given r does not affect the imposition of the aforementioned restriction since it is clearly achieved by sufficiently small data. By what has been explained in Section 2.2, this fact is equivalent to the well-posedness of the augmented mixed-primal formulation (2.11), and the result is stated as follows.

    Theorem 2.9. Assume that for δ1(0,2μ2) and δ2(0,2) we choose

    κ1(0,2μ1δ1μ2),κ2,κ4(0,),andκ3(0,2δ2(μ1κ1μ22δ1)),

    and consider the ball W (cf. (2.32)) with radius r(0,r0) and r0 as in (2.29). In addition, assume that the data satisfy

    c(r){g,Ω+uD1/2,Γ}+C˜SφD1/2,Γr,

    with c(r) as in Lemma 2.5, and

    CT{g,Ω+uD1/2+ε,Γ}<1.

    Then, the problem (2.11) has a unique solution (tt,(φ,λ))H×H1(Ω)×H1/2(Γ), with (u,φ)W. Moreover, there hold

    ttCS{rg,Ω+uD1/2,Γ}, (2.41)

    and

    (φ,λ)C˜S{ru1,Ω+φD1/2,Γ}, (2.42)

    with CS and C˜S as in Lemmas 2.3 and 2.4, respectively.

    In this section we derive a Galerkin method for the augmented mixed-primal formulation (2.11). Let us consider Th, a regular triangulation of ˉΩ by triangles T (when n=2) or tetrahedra T (when n=3) of diameter hT and define the meshsize h:=max{hT:TTh}. Then, consider arbitrary finite-dimensional subspaces HthL2tr(Ω), HσhH0(div;Ω), HuhH1(Ω), HφhH1(Ω), HλhH1/2(Γ) and denote by Hh:=Hth×Hσh×Huh, tth:=(th,σh,uh) and tsh:=(sh,τh,vh). Hence, according to (2.11), the Galerkin scheme reads: Find (tth,(φh,λh))Hh×Hφh×Hλh such that

    Aφh(tth,tsh)+Buh(tth,tsh)=Fφh(tsh)+FD(tsh) tshHh,a(φh,ψh)+b(ψh,λh)=Fuh,φh(ψh) ψhHφh,b(φh,ξh)=G(ξh) ξhHλh, (3.1)

    where the forms Aφh, Buh, a and b; and the functionals Fφh, FD, Fuh,φh are defined by (2.12)-(2.19). Since the proof of well-posedness follows the steps of the previous section, and moreover, analogously to [5,Section 4], we only state the requirements to be imposed over the finite-dimensional subspaces and the main result, which is analogous to [5,Theorem 4.8].

    It can be seen that no restrictions have to be added to Hth, Hσh and Huh other than being finite-dimensional subspaces of the described spaces. However, for ellipticity purposes of a in the discrete kernel of the operator induced by b (according the the Babuška-Brezzi theory), the following two inf-sup conditions must be met:

    (H.1) There exists a constant ˆα>0, independent of h such that

    supψhVhψh0a(ψh,ϕh)ψh1,Ωˆαϕh1,Ω ϕhVh, (3.2)

    where

    Vh:={ψhHφh:b(ψh,ξh)=0 ξhHλh},

    (H.2) There exists a constant ˆβ>0, independent of h such that

    supψhHφhψh0b(ψh,ξh)ψh1,Ωˆβξh1/2,Γ ξhHλh. (3.3)

    Then, denoting by Wh the closed ball in Hh:=Huh×Hφh of radius r and center (0,0), that is

    Wh:={(wh,ϕh)Hh:(wh,ϕh)r},

    the main result of this section reads as follows.

    Theorem 3.1. Assume that for δ1(0,2μ2) and δ2(0,2) we choose

    κ1(0,2μ1δ1μ2),κ2,κ4(0,),andκ3(0,2δ2(μ1κ1μ22δ1)),

    and consider the ball Wh with radius r(0,r0), r0 as in (2.29). Then, there exist positive constants CS, ˜C˜S and ˜c(r) such that, if the data satisfy

    ˜c(r){g,Ω+uD1/2,Γ}+˜C˜SφD1/2,Γr,

    then the problem (3.1) has at least one solution (tth,(φh,λh))Hh×Hφh×Hλh with (uh,φh)Wh. Moreover, there hold

    tthCS{rg,Ω+uD1/2,Γ}, (3.4)

    and

    (φh,λh)˜C˜S{ruh1,Ω+φD1/2,Γ}.

    Proof. We only mention that (3.1) is transformed into a fixed-point problem that is analyzed by means of the Brouwer fixed-point theorem in the convex and compact set Wh (see [5,Theorem 4.8]).

    Given a set SR:=Rn and an integer k0, we define Pk(S) as the space of polynomial functions on S of degree k, and for each TTh, we define the local Raviart-Thomas spaces of order k as

    RTk(T):=Pk(T)+Pk(T)x,

    where x is a generic vector in R. Hence, the strain rate, stress, velocity and temperature variables can be approximated using the following finite element subspaces:

    Hth:={shL2tr(Ω):sh|TPk(T) TTh}, (3.5)
    Hσh:={τhH0(div;Ω):ctτh|TRTk(T), cR,  TTh}, (3.6)
    Huh:={vhC(ˉΩ):vh|TPk+1(T), TTh}, (3.7)
    Hφh:={ψhC(ˉΩ):ψh|TPk+1(T), TTh}, (3.8)

    whereas for the normal component of the heat flux, we let {˜Γ1,˜Γ2,,˜Γm} be an independent triangulation of Γ (made of straight segments in R2 or triangles in R3), and define ˜h:=maxj{1,,m}|˜Γj|. Then, with the same integer k0 used in definitions (3.5)-(3.8), we approximate λ by piecewise polynomials of degree k over this new mesh, that is

    Hλ˜h:={ξ˜hL2(Γ):ξ˜h|˜ΓjPk(˜Γj) j{1,,m}}. (3.9)

    It can be seen that Hφh and Hλ˜h satisfy the conditions (3.2) and (3.3) as long as hC0˜h for some C0>0 (cf. [5,Section 4.3]). Other problems and corresponding references where some of or all the above finite element subspaces have been employed include, among others, coupled flow-transport [8,9], natural convection with phase-change [7], Navier-Stokes [15,16], and Stokes-Darcy (see, e.g. [29], where the above restriction between the mesh sizes h and ˜h was discussed).

    In turn, the approximation properties of the subspaces defined in (3.5) - (3.8) and (3.9) are (cf. [14,28])

    (APth) There exists C>0, independent of h, such that for each s(0,k+1], and for each tHs(Ω)L2tr(Ω), there holds

    dist(t,Hth)Chsts,Ω,

    (APσh) There exists C>0, independent of h, such that for each s(0,k+1], and for each σHs(Ω)H0(div;Ω) with divσHs(Ω), there holds

    dist(σ,Hσh)Chs{σs,Ω+divσs,Ω},

    (APuh) there exists C>0, independent of h, such that for each s(0,k+1], and for each uHs+1(Ω), there holds

    dist(u,Huh)Chsus+1,Ω,

    (APφh) there exists C>0, independent of h, such that for each s(0,k+1], and for each φHs+1(Ω), there holds

    dist(φ,Hφh)Chsφs+1,Ω,

    (APλ˜h) there exists C>0, independent of ˜h, such that for each s(0,k+1], and for each λH1/2+s(Γ), there holds

    dist(λ,Hλ˜h)C˜hsλ1/2+s,Γ.

    Let (tt,(φ,λ))H×H1(Ω)×H1/2(Γ) with (u,φ)W be the solution of (2.11), and (tth,(φh,λh))Hh×Hφh×Hλh with (uh,φh)Wh be a solution of the discrete problem (3.1), that is,

    3(Aφ+Bu)(tt,ts)=(Fφ+FD)(ts) tsH; (4.1a)
    (Aφh+Buh)(tth,tsh)=(Fφh+FD)(tsh) tshHh, (4.1b)

    and

    3a(φ,ψ)+b(ψ,λ)=Fu,φ(ψ) ψH1(Ω), (4.2a)
    b(φ,ξ)=G(ξ) ξH1/2(Γ); (4.2b)
    a(φh,ψh)+b(ψh,λh)=Fuh,φh(ψh) ψhHφh, (4.2c)
    b(φh,ξh)=G(ξh) ξhHλh. (4.2d)

    In what follows, we denote as usual

    dist(tt,Hh):=inftshHhtttsh,

    and

    dist((φ,λ),Hφh×Hλh):=inf(ψh,ξh)Hφh×Hλh(φ,λ)(ψh,ξh).

    First, the error estimate related to the variables of the momentum equation is obtained by means of the Strang Lemma, applied to the pair (4.1). We recall the Lemma, and its consequent result next.

    Lemma 4.1 (Strang). Let V be a Hilbert space, FV, and A:V×VR be a bounded and V-elliptic bilinear form. In addition, let {Vh}h>0 be a sequence of finite-dimensional subspaces of V, and for each h>0, consider a bounded bilinear form Ah:Vh×VhR and a functional FhVh. Assume that the family {Ah}h>0 is uniformly elliptic in Vh, that is, there exists a constant ˜α>0, independent of h, such that

    Ah(vh,vh)˜αvh2V vhVh,  h>0.

    In turn, let uV and uhVh such that

    A(u,v)=F(v) vVandAh(uh,vh)=F(vh) vhVh.

    Then, for each h>0, there holds

    uuhVCST{supwhVhwh0|F(wh)Fh(wh)|whV+infvhVhvh0(uvhV+supwhVhwh0|A(vh,wh)Ah(vh,wh)whV)},

    where CST:=˜α1max{1,A}.

    Proof. See [38,Theorem 11.1].

    Lemma 4.2. Let CST:=2α(Ω)max{1,Aφ+Bu}, where α(Ω)2 is the ellipticity constant of Aφ+Bu (cf. (2.28)). Then, there holds

    tttthCST{(1+2Aφ+Bu) dist(tt,Hh)+c1(Ω)(1+κ21)1/2u1,Ωuuh1,Ω +{LμCε˜Cε(1+κ21)1/2tε,Ω+(1+κ22)1/2g,Ω}φφh1,Ω}. (4.3)

    Proof. From Lemma 2.3, we see that Aφ+Bu and Aφh+Buh are bilinear, bounded (both with constant Aφ+Bu, w.l.o.g. since it is independent of (u,φ)) and uniformly elliptic (both with constant α(Ω)2). Also, F_{\varphi}+F_D and F_{{\varphi}_h}+F_D are linear bounded functionals in {\mathcal{H}} and {\mathcal{H}}_h , respectively. Hence, a straightforward application of Lemma 4.1 to the pair (4.1) yields

    \begin{multline} {\left\| \, {{\vec{\vphantom t\smash{{{\bf t}}}}}-{\vec{\vphantom t\smash{{{\bf t}}}}}_h} \, \right\|} \leq C_{ST} \left\{ \sup\limits_{\substack{{\vec{\vphantom t\smash{{{\bf s}}}}}_h \in {\mathcal{H}}_h \\ {\vec{\vphantom t\smash{{{\bf s}}}}}_h \neq \vec{\vphantom t\smash{{{\bf 0}}}}}} \dfrac{|F_{\varphi}({\vec{\vphantom t\smash{{{\bf s}}}}}_h) - F_{{\varphi}_h}({\vec{\vphantom t\smash{{{\bf s}}}}}_h)|}{{\left\| \, {{\vec{\vphantom t\smash{{{\bf s}}}}}_h} \, \right\|}} + \inf\limits_{\substack{{\vec{\vphantom t\smash{{{\bf q}}}}}_h \in {\mathcal{H}}_h \\ {\vec{\vphantom t\smash{{{\bf q}}}}}_h \neq \vec{\vphantom t\smash{{{\bf 0}}}}}} \left( {\left\| \, {{\vec{\vphantom t\smash{{{\bf t}}}}}-{\vec{\vphantom t\smash{{{\bf q}}}}}_h} \, \right\|} \phantom{\sup\limits_{\substack{{\vec{\vphantom t\smash{{{\bf q}}}}}_h \\ {\vec{\vphantom t\smash{{{\bf q}}}}}_h}}} \right. \right. \\ \qquad\qquad\qquad\qquad+ \left. \left. \sup\limits_{\substack{{\vec{\vphantom t\smash{{{\bf s}}}}}_h \in {\mathcal{H}}_h \\ {\vec{\vphantom t\smash{{{\bf s}}}}}_h \neq \vec{\vphantom t\smash{{{\bf 0}}}}}} \dfrac{|({\bf A}_{\varphi}+{\bf B}_{\bf u})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h) - ({\bf A}_{{\varphi}_h}+{\bf B}_{{\bf u}_h})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h)|}{{\left\| \, {{\vec{\vphantom t\smash{{{\bf s}}}}}_h} \, \right\|}} \right) \right\}, \qquad \end{multline} (4.4)

    where C_{ST} : = \frac{2}{\alpha(\Omega)} \max\big\{1,{\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|}\big\} . First, we notice that

    \begin{equation} |F_{\varphi}({\vec{\vphantom t\smash{{{\bf s}}}}}_h) - F_{{\varphi}_h}({\vec{\vphantom t\smash{{{\bf s}}}}}_h)| = |F_{{\varphi}-{\varphi}_h}({\vec{\vphantom t\smash{{{\bf s}}}}}_h)| \leq (1+\kappa_2^2)^{1/2} {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} {\left\| \, {{\varphi}-{\varphi}_h} \, \right\|}_{1,\Omega} {\left\| \, {{\vec{\vphantom t\smash{{{\bf s}}}}}_h} \, \right\|} \quad \forall \ {\vec{\vphantom t\smash{{{\bf s}}}}}_h \in {\mathcal{H}}_h. \end{equation} (4.5)

    Then, in order to estimate the last supremum in (4.4), we add and subtract suitable terms to write

    \begin{array}{l} ({\bf A}_{\varphi}+{\bf B}_{\bf u})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h) - ({\bf A}_{{\varphi}_h}+{\bf B}_{{\bf u}_h})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h) \\ [2ex] \qquad = \, ({\bf A}_{\varphi}+{\bf B}_{\bf u})({\vec{\vphantom t\smash{{{\bf q}}}}}_h - {\vec{\vphantom t\smash{{{\bf t}}}}}, {\vec{\vphantom t\smash{{{\bf s}}}}}_h) + ({\bf A}_{{\varphi}}-{\bf A}_{{\varphi}_h})({\vec{\vphantom t\smash{{{\bf t}}}}},{\vec{\vphantom t\smash{{{\bf s}}}}}_h) \\ [2ex] \qquad + \, ({\bf B}_{\bf u}-{\bf B}_{{\bf u}_h})({\vec{\vphantom t\smash{{{\bf t}}}}},{\vec{\vphantom t\smash{{{\bf s}}}}}_h) - ({\bf A}_{{\varphi}_h}+{\bf B}_{{\bf u}_h})({\vec{\vphantom t\smash{{{\bf q}}}}}_h-{\vec{\vphantom t\smash{{{\bf t}}}}},{\vec{\vphantom t\smash{{{\bf s}}}}}_h)\,, \end{array}

    and so, using the boundedness of the bilinear forms {\bf A}_{\varphi}+{\bf B}_{\bf u} and {\bf A}_{{\varphi}_h}+{\bf B}_{{\bf u}_h} (cf. (2.27)), the estimate (2.8), the continuous embedding H^1(\Omega) \hookrightarrow L^{n/\varepsilon}(\Omega) with constant \widetilde{C}_\varepsilon and the further-regularity assumption in a similar way to (2.39), we obtain

    \begin{multline*} \qquad |({\bf A}_{\varphi}+{\bf B}_{\bf u})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h) - ({\bf A}_{{\varphi}_h}+{\bf B}_{{\bf u}_h})({\vec{\vphantom t\smash{{{\bf q}}}}}_h,{\vec{\vphantom t\smash{{{\bf s}}}}}_h)| \leq 2 {\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|} {\left\| \, {{\vec{\vphantom t\smash{{{\bf q}}}}}_h-{\vec{\vphantom t\smash{{{\bf t}}}}}} \, \right\|} {\left\| \, {{\vec{\vphantom t\smash{{{\bf s}}}}}_h} \, \right\|} \\ \begin{aligned} &+ \bigg| \int_\Omega \Big[ \mu({\varphi})-\mu({\varphi}_h) \Big] {\bf t} : \Big\{ {\bf s}_h - \kappa_1 {{\mathit{\boldsymbol{\tau}}}}_h^{{\tt d}} \Big\} \bigg| + \bigg| \int_\Omega \Big[{\bf u} \otimes ({\bf u}-{\bf u}_h)\Big]^{{\tt d}} : \Big\{ \kappa_1 {{\mathit{\boldsymbol{\tau}}}}_h^{{\tt d}} - {\bf s}_h \Big\} \bigg| \\ &\leq \bigg\{ 2 {\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|} {\left\| \, {{\vec{\vphantom t\smash{{{\bf t}}}}}-{\vec{\vphantom t\smash{{{\bf q}}}}}_h} \, \right\|} + L_\mu C_\varepsilon \widetilde{C}_{\varepsilon}(1+\kappa_1^2)^{1/2} {\left\| \, {{\bf t}} \, \right\|}_{\varepsilon,\Omega} {\left\| \, {{\varphi}-{\varphi}_h} \, \right\|}_{1,\Omega} \\ &+ c_1(\Omega) (1+\kappa_1^2)^{1/2} {\left\| \, {{\bf u}} \, \right\|}_{1,\Omega} {\left\| \, {{\bf u}-{\bf u}_h} \, \right\|}_{1,\Omega} \bigg\} {\left\| \, {{\vec{\vphantom t\smash{{{\bf s}}}}}_h} \, \right\|}. \end{aligned} \qquad \end{multline*}

    This inequality, together with (4.5), back into (4.4), results in (4.3), therefore concluding the proof.

    Next, we recall from [20,Lemma 5.4] (see also [5,Lemma 5.4]) the error estimate of the variables in the energy equation.

    Lemma 4.3. There exists a positive constant \widehat{C}_{ST} , depending only on {\left\| \, {{\bf a}} \, \right\|} , {\left\| \, {{\bf b}} \, \right\|} , \widehat{\alpha} and \widehat{\beta} (cf. (3.2), (3.3)), such that

    \begin{multline*} {\left\| \, {({\varphi},\lambda) - ({\varphi}_h,\lambda_h)} \, \right\|} \\ \leq \widehat{C}_{ST} \big\{ c_2(\Omega) \big( {\left| \, {{\varphi}} \, \right|}_{1,\Omega} {\left\| \, {{\bf u}-{\bf u}_h} \, \right\|}_{1,\Omega} + {\left\| \, {{\bf u}_h} \, \right\|}_{1,\Omega} {\left| \, {{\varphi}-{\varphi}_h} \, \right|}_{1,\Omega} \big) + {\mathrm{dist}\left( {({\varphi},\lambda),{H_h^{\varphi}} \times {H_h^\lambda}} \right)} \big\}. \end{multline*}

    Proof. It suffices to apply the generalized Strang-type estimate for saddle point problems provided in [20,Lemma 5.2] (see also [5,Lemma 5.2]), which constitutes a particular case of [38,Theorem 11.12], to the context given by (4.2).

    Hence, adding the estimates obtained in the previous two lemmas, we have a preliminary estimate for the total error:

    \begin{align} & {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|} \leq C_{ST}(1+2{\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|}) \ {\mathrm{dist}\left( {{\vec{\vphantom t\smash{{{\bf t}}}}},{\mathcal{H}}_h} \right)} \\ & \qquad + \widehat{C}_{ST} \ {\mathrm{dist}\left( {({\varphi},\lambda),({H_h^{\varphi}} \times {H_h^\lambda})} \right)} + \Big\{ C_1 {\left\| \, {{\bf u}} \, \right\|}_{1,\Omega} + C_2 {\left| \, {{\varphi}} \, \right|}_{1,\Omega} \Big\} {\left\| \, {{\bf u}-{\bf u}_h} \, \right\|}_{1,\Omega} \\ &\qquad + \Big\{ C_3 {\left\| \, {{\bf t}} \, \right\|}_{\varepsilon,\Omega} + C_4 {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} + C_2 {\left\| \, {{\bf u}_h} \, \right\|}_{1,\Omega} \Big\} {\left\| \, {{\varphi}-{\varphi}_h} \, \right\|}_{1,\Omega}, \end{align} (4.6)

    where

    \begin{gathered} C_1 : = C_{ST} c_1(\Omega)(1+\kappa_1^2)^{1/2}, \quad C_2 : = \widehat{C}_{ST} c_2(\Omega), \\ C_3 : = C_{ST} L_\mu C_\varepsilon \widetilde{C}_{\varepsilon} (1+\kappa_1^2)^{1/2}, \quad C_4 = C_{ST}(1+\kappa_2^2)^{1/2}. \end{gathered}

    Then, bounding the terms {\left\| \, {{\bf u}} \, \right\|}_{1,\Omega} , {\left| \, {{\varphi}} \, \right|}_{1,\Omega} and {\left\| \, {{\bf u}_h} \, \right\|}_{1,\Omega} using the continuous dependence results (2.41), (2.42) and (3.4), and the further-regularity assumption (2.33) to bound {\left\| \, {{\bf t}} \, \right\|}_{\varepsilon,\Omega} , (4.6) becomes

    \begin{align} & {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|} \leq C_{ST}(1+2{\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|}) \ {\mathrm{dist}\left( {{\vec{\vphantom t\smash{{{\bf t}}}}},{\mathcal{H}}_h} \right)} \\ &\quad + \widehat{C}_{ST} {\mathrm{dist}\left( {({\varphi},\lambda),({H_h^{\varphi}} \times {H_h^\lambda})} \right)} \\ &\quad + \bigg\{ (C_1 + C_2 C_{\widetilde{{\bf S}}}r + C_2) C_{\bf S} \Big( r {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} + {\left\| \, {{\bf u}_D} \, \right\|}_{1/2,\Gamma} \Big)+ C_4 {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} \\ &\qquad\quad + C_3 \widetilde{C}_{{\bf S}}(r) \Big(r {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} + {\left\| \, {{\bf u}_D} \, \right\|}_{1/2+\varepsilon,\Gamma} \Big) + C_2 C_{\widetilde{{\bf S}}} {\left\| \, {{\varphi}_D} \, \right\|}_{1/2,\Gamma} \bigg\}\\ &\qquad \times {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|}. \end{align} (4.7)

    Therefore, using the continuous injection H^{1/2+\varepsilon}(\Gamma) \hookrightarrow H^{1/2}(\Gamma) with constant \widehat{C}_i and denoting by

    \begin{gather*} C_5 : = C_2 C_{\widetilde{{\bf S}}}, \quad C_6 : = (C_1 + C_5 r + C_2) C_{\bf S}, \quad C_7 : = C_6 r + C_3 \widetilde{C}_{{\bf S}}(r) r + C_4, \\ C_8 : = C_6 \widehat{C}_i +C_3 \widetilde{C}_{{\bf S}}(r), \quad {\bf C}_0 : = \max\{C_5,C_7,C_8\}, \end{gather*}

    we see from (4.7) that

    \begin{multline} {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|} \\ \leq C_{ST}(1+2{\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|}) \ {\mathrm{dist}\left( {{\vec{\vphantom t\smash{{{\bf t}}}}},{\mathcal{H}}_h} \right)} + \widehat{C}_{ST} \ {\mathrm{dist}\left( {({\varphi},\lambda),({H_h^{\varphi}} \times {H_h^\lambda})} \right)} \\ + {\bf C}_0 \Big( {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} + {\left\| \, {{\bf u}_D} \, \right\|}_{1/2+\varepsilon,\Gamma} + {\left\| \, {{\varphi}_D} \, \right\|}_{1/2,\Gamma} \Big) {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|}, \end{multline} (4.8)

    which leads us the main result of this section.

    Theorem 4.4. Assume that

    \begin{equation} {\bf C}_0 \Big( {\left\| \, {{\bf g}} \, \right\|}_{\infty,\Omega} + {\left\| \, {{\bf u}_D} \, \right\|}_{1/2+\varepsilon,\Gamma} + {\left\| \, {{\varphi}_D} \, \right\|}_{1/2,\Gamma} \Big) < \dfrac{1}{2}. \end{equation} (4.9)

    Then, there exists C > 0 depending only on parameters, data and other constants, all of them independent of h , such that

    \begin{equation} {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|} \leq C \ {\mathrm{dist}\left( { ({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)), {\mathcal{H}}_h \times {H_h^{\varphi}} \times {H_h^\lambda} } \right)}. \end{equation} (4.10)

    Proof. The assumption (4.9) allows us to subtract the total error term in the right-hand side of (4.8), thus verifying the Céa's estimate with C = 2 \max \Big\{ C_{ST}(1+2{\left\| \, {{\bf A}_{\varphi}+{\bf B}_{\bf u}} \, \right\|}),\widehat{C}_{ST} \Big\} .

    Finally, we state the rates of convergence of the Galerkin scheme (3.1) when the finite element subspaces (3.5)-(3.9) are used.

    Theorem 4.5. In addition to the hypotheses of Theorems \rm2.9 , \rm3.1 and \rm4.4 , assume that there exists s>0 such that {\bf t} \in \mathbb{H}^s(\Omega) , {{\mathit{\boldsymbol{\sigma}}}} \in \mathbb{H}^s(\Omega) , {\mathit{\boldsymbol{\mathrm{div}}} \, {{{\mathit{\boldsymbol{\sigma}}}}}} \in {\bf H}^s(\Omega) , {\bf u} \in {\bf H}^{s+1}(\Omega) , {\varphi} \in H^{s+1}(\Omega) and \lambda \in H^{-1/2+s}(\Gamma) . Then, there exists \widehat{C} > 0 , independent of h and \widetilde{h} such that for all h \leq C_0 \widetilde{h} there holds

    \begin{multline*} {\left\| \, {({\vec{\vphantom t\smash{{{\bf t}}}}},({\varphi},\lambda)) - ({\vec{\vphantom t\smash{{{\bf t}}}}}_h,({\varphi}_h,\lambda_h))} \, \right\|} \leq \widehat{C} \widetilde{h}^{\min\{s,k+1\}} {\left\| \, {\lambda} \, \right\|}_{-1/2+s,\Gamma} \\ + \widehat{C} h^{\min\{s,k+1\}} \Big\{ {\left\| \, {{\bf t}} \, \right\|}_{s,\Omega} + {\left\| \, {{{\mathit{\boldsymbol{\sigma}}}}} \, \right\|}_{s,\Omega} + {\left\| \, {{\mathit{\boldsymbol{\mathrm{div}}} \, {{{\mathit{\boldsymbol{\sigma}}}}}}} \, \right\|}_{s,\Omega} + {\left\| \, {{\bf u}} \, \right\|}_{s+1,\Omega} + {\left\| \, {{\varphi}} \, \right\|}_{s+1,\Omega} \Big\}. \end{multline*}

    Proof. It follows from Céa's estimate (4.10) and the approximation properties (\mathbf{AP}_h^{{\bf t}}) , (\mathbf{AP}_h^{{{\mathit{\boldsymbol{\sigma}}}}}) , (\mathbf{AP}_h^{{\bf u}}) , (\mathbf{AP}_h^{{\varphi}}) , and (\mathbf{AP}_{\widetilde{h}}^{\lambda}) described in Section 3.2.

    We now present three numerical examples showing the performance of the augmented mixed-primal method (3.1) with the subspaces specified in Section 3.2. The computational implementation uses the finite element library FEniCS (cf. [6]), in particular the module multiphenics1 that allows a straightforward description of the Lagrange multiplier and a block structure manipulation of the linearized systems arising at each Picard step. The solution of these linear systems is done employing the unsymmetric multi-frontal direct solver MUMPS (cf. [10]).

    http://mathlab.sissa.it/multiphenics

    The implemented iterative method mimics the fixed-point strategy from Section 2.2: it begins with an initial state at rest ({\bf u},{\varphi}) = ({\bf 0},0) and it stops whenever the relative error between two consecutive iterations of the solution vector measured in the \ell^2 -norm (the sum of squared entries of a vector) is sufficiently small, i.e.,

    \dfrac{{\left\| \, {\bf{ coeff}^{\ m+1} - \bf{ coeff}^{\ m}} \, \right\|}_{\ell^2}}{{\left\| \, {\bf{ coeff}^{\ m+1}} \, \right\|}_{\ell^2}} < \texttt{tol},

    where \mathtt{tol} = 10^{-6} is a specified tolerance. We also recall that the pressure is post-processed as

    p_h : = -\dfrac{1}{n}{\mathrm{tr}}{({{\mathit{\boldsymbol{\sigma}}}}_h+{\bf u}_h\otimes{\bf u}_h)}\,,

    which, as explained in [5,Section 5.2], converges to the exact pressure p at the same rate as the other variables (cf. Theorem 4.5). Similarly, as suggested by the second expression in (1.4), the vorticity can be postprocessed as

    {{\mathit{\boldsymbol{\gamma}}}}_h : = \frac12\,\Big\{\nabla {\bf u}_h - \big(\nabla {\bf u}_h\big)^{\tt t}\Big\}\,,

    which easily yields

    \|{{\mathit{\boldsymbol{\gamma}}}} - {{\mathit{\boldsymbol{\gamma}}}}_h\|_{0,\Omega}\,\le\, |{\bf u} - {\bf u}_h|_{1,\Omega}\,\le\,\|{\bf u} - {\bf u}_h\|_{1,\Omega}\,,

    thus proving that it also converges at the same rate provided by Theorem 4.5. In this way, we define the error per variable

    \begin{gather*} e({\bf t}) : = {\left\| \, {{\bf t}-{\bf t}_h} \, \right\|}_{0,\Omega}, \quad e({{\mathit{\boldsymbol{\sigma}}}}) : = {\left\| \, {{{\mathit{\boldsymbol{\sigma}}}}-{{\mathit{\boldsymbol{\sigma}}}}_h} \, \right\|}_{\mathbf{div};\Omega}, \quad e({\bf u}) : = {\left\| \, {{\bf u}-{\bf u}_h} \, \right\|}_{1,\Omega}, \\ e(p) : = {\left\| \, {p-p_h} \, \right\|}_{0,\Omega}, \quad e({{\mathit{\boldsymbol{\gamma}}}}) : = {\left\| \, {{{\mathit{\boldsymbol{\gamma}}}}-{{\mathit{\boldsymbol{\gamma}}}}_h} \, \right\|}_{0,\Omega}, \quad e({\varphi}) : = {\left\| \, {{\varphi}-{\varphi}_h} \, \right\|}_{1,\Omega}, \\ e(\lambda) : = {\left\| \, {\lambda-\lambda_{\widetilde{h}}} \, \right\|}_{0,\Gamma}, \end{gather*}

    as well as their corresponding rates of convergence

    \begin{gather*} r({\bf t}) : = \dfrac{\log(e({\bf t})/e'({\bf t}))}{\log(h/h')}, \quad r({{\mathit{\boldsymbol{\sigma}}}}) : = \dfrac{\log( e({{\mathit{\boldsymbol{\sigma}}}})/e'({{\mathit{\boldsymbol{\sigma}}}}) )}{\log(h/h')}, \quad r({\bf u}) : = \dfrac{\log( e({\bf u})/e'({\bf u}) )}{\log(h/h')}, \\ r(p) : = \dfrac{\log( e(p)/e'(p) )}{\log(h/h')}, \quad r({{\mathit{\boldsymbol{\gamma}}}}) : = \dfrac{\log( e({{\mathit{\boldsymbol{\gamma}}}})/e'({{\mathit{\boldsymbol{\gamma}}}}) )}{\log(h/h')}, \quad r({\varphi}) : = \dfrac{\log( e({\varphi})/e'({\varphi}) )}{\log(h/h')}, \\ r(\lambda) : = \dfrac{\log( e(\lambda)/e'(\lambda) ),}{\log(\widetilde{h}/\widetilde{h}')}, \end{gather*}

    where h and h' (respectively \widetilde{h} and \widetilde{h}' ) denote two consecutive mesh sizes with errors e and e' .

    We first consider \Omega : = (-1,1)^2 , viscosity, thermal conductivity and body force given by \mu({\varphi}) = \exp(-0.25 \, {\varphi}) , {\mathbb{K}} = {\mathbb{I}} , {\bf g} = (0,1)^{{\tt t}} , and boundary conditions such that the exact solution to (1.1) is

    \begin{gather*} {\bf u} = \left( \begin{array}{c} \sin(\pi x)\cos(\pi y) \\ -\cos(\pi x) \sin(\pi y) \end{array} \right), \quad {\bf t} = {\bf e}({\bf u}), \quad {{\mathit{\boldsymbol{\gamma}}}} = \nabla {\bf u} - {\bf e}({\bf u}), \quad p = x^4-y^4, \\ {{\mathit{\boldsymbol{\sigma}}}} = \mu({\varphi}) {\bf e}({\bf u}) - {\bf u} \otimes {\bf u} - p {\mathbb{I}}, \quad {\varphi} = -0.6944 \, y^4 + 1.6944 \, y^2, \quad \lambda = -{\mathbb{K}} \nabla {\varphi} \cdot {{\mathit{\boldsymbol{\nu}}}}. \end{gather*}

    Non-homogeneous terms will then appear in the right-hand sides of the momentum and energy equations. Nevertheless, well-posedness is still ensured, since the smoothness of the exact solution makes these terms immediately belong to L^2(\Omega) , thus requiring only a minor modification in the functionals of the variational formulation. Concerning the stabilization parameters, these are taken as pointed out in Section 2.3, where the viscosity bounds are estimated as \mu_1 = 0.5 , \mu_2 = 1.25 , thus resulting in \kappa_1 = \kappa_2 = 0.32 , \kappa_3 = 0.25 and \kappa_4 = 0.125 . We also remark that the trace condition on the stress is enforced through penalization, here and also in the upcoming examples.

    In Figure 5.1 we show part of the obtained numerical solution with 214,788 DOF and a first order approximation, whereas in Table 5.1 we show the convergence history given the specified data and the finite element spaces from Section 3.2 with successive quasi-uniform mesh refinements. In both cases, it can be seen that the rates of convergence are the expected ones according to Theorem 4.5, that is, \mathcal{O}(h) for the first case, and \mathcal{O}(h^2) for the second one. We also observe that around eight iterations are needed to reach convergence of the Picard algorithm.

    Table 1.  Convergence history for Example 1, with a quasi-uniform mesh refinement and approximations of first and second order.
    Finite Element: \mathbb{P}_0 - \mathbb{RT}_0 - {\bf P}_1 - P_1 - P_0
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    84 1.4140 5.2972 12.870 9.6113 1.4554 2.8012 0.8379 1.5082
    268 0.7071 2.4345 7.0572 4.6912 1.0387 2.2743 0.8278 0.8069
    948 0.3536 1.2700 3.8456 2.4815 0.5934 1.2154 0.3977 0.4969
    3,556 0.1768 0.6461 1.9470 1.2414 0.3021 0.6162 0.2310 0.2353
    13,764 0.0884 0.3248 0.9766 0.6182 0.1502 0.3084 0.0948 0.0703
    54,148 0.0442 0.1626 0.4887 0.3086 0.0749 0.1542 0.0465 0.0199
    214,788 0.0221 0.0814 0.2444 0.1542 0.0375 0.0771 0.0232 0.0091
    IT \widetilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    11 0.5000
    7 0.2500 1.122 0.8665 1.0352 0.4854 0.3012 0.0176 0.8069
    9 0.1250 0.9385 0.8762 0.9189 0.8072 0.9037 1.0583 1.0917
    8 0.0625 0.9751 0.9814 0.9989 0.9739 0.9798 0.7834 1.1912
    9 0.0312 0.9924 0.9957 1.0061 1.0080 0.9988 1.2842 1.2129
    8 0.0156 0.9978 0.9989 1.0020 1.0031 0.9998 1.0271 1.2816
    8 0.0078 0.9994 0.9997 1.0010 1.0010 1.0000 1.0020 1.1434
    Finite Element: \mathbb{P}_1 - \mathbb{RT}_1 - {\bf P}_2 - P_2 - P_1
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    236 1.4140 1.8442 3.3631 3.4822 0.9773 2.4131 0.7265 2.1308
    820 0.7071 0.4471 1.0930 0.9907 0.2911 0.6832 0.1611 0.3965
    3,044 0.3536 0.1252 0.2853 0.2855 0.0805 0.1857 0.0399 0.0833
    11,716 0.1768 0.0328 0.0732 0.0747 0.0209 0.05792 0.0078 0.0213
    45,956 0.0884 0.0083 0.0185 0.0189 0.0053 0.01905 0.0019 0.0056
    182,020 0.0442 0.0021 0.0046 0.0047 0.0013 0.0054 0.0005 0.0011
    724,448 0.0221 0.0006 0.0012 0.0012 0.0004 0.0013 0.0001 0.0002
    IT \widetilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    6 0.5000
    7 0.2500 2.0445 1.6220 1.8130 1.7471 1.6275 2.1722 2.2383
    8 0.1250 1.8378 1.9377 1.7953 1.8544 1.9316 2.0114 2.2469
    8 0.0625 1.9284 1.9619 1.9332 1.9440 1.9827 2.3410 1.9744
    8 0.0312 1.9771 1.9871 1.9845 1.9821 1.9957 2.0073 2.0656
    8 0.0156 1.9898 1.9955 1.9926 1.9932 1.9989 1.9987 2.1131
    8 0.0078 1.9956 1.9970 1.9931 1.9995 1.9997 2.0031 2.2573

     | Show Table
    DownLoad: CSV
    Figure 5.1.  Numerical results for Example 1. From top-left to right-bottom: XX, XY and YY components of the pseudostress, XX component of the strain rate, velocity components and vector fields, postprocessed pressure, postprocessed vorticity magnitude, and temperature. Snapshots obtained from a simulation with 214,788 DOF and a first order approximation.

    We next consider \Omega : = (0,1)^2 ; viscosity, thermal conductivity and body force as in Example 1, and boundary conditions and source terms such that the exact solution is

    \begin{aligned} u_1(x,y) & = \left[ 1 - \cos\left( \dfrac{2\pi(e^{r_1 x}-1)}{e^{r_1}-1} \right) \right] \sin \left( \dfrac{2\pi(e^{r_2 y}-1)}{e^{r_2}-1} \right) \dfrac{r_2}{2\pi} \dfrac{e^{r_2 y}}{e^{r_2}-1}, \\ u_2(x,y) & = - \left[ 1 - \cos\left( \dfrac{2\pi(e^{r_2 y}-1)}{e^{r_2}-1} \right) \right] \sin \left( \dfrac{2\pi(e^{r_1 x}-1)}{e^{r_1}-1} \right) \dfrac{r_1}{2\pi} \dfrac{e^{r_1 x}}{e^{r_1}-1}, \\ p(x,y) & = r_1 r_2 \sin\left( \dfrac{2\pi(e^{r_1 x}-1)}{e^{r_1}-1} \right) \sin\left( \dfrac{2\pi(e^{r_2 y}-1)}{e^{r_2}-1} \right) \dfrac{e^{r_1 x + r_2 y}}{(e^{r_1}-1)(e^{r_2}-1)}, \end{aligned}

    where r_1 and r_2 are positive parameters, and

    \begin{gather*} {\bf u} = \left( \begin{array}{c} u_1(x,y) \\ u_2(x,y) \end{array} \right), \quad {\bf t} = {\bf e}({\bf u}), \quad {{\mathit{\boldsymbol{\gamma}}}} = \nabla {\bf u} - {\bf e}({\bf u}), \quad {{\mathit{\boldsymbol{\sigma}}}} = \mu({\varphi}) {\bf e}({\bf u}) - {\bf u} \otimes {\bf u} - p{\mathbb{I}} \\ {\varphi} = u_1(x,y) + u_2(x,y), \quad \lambda = -{\mathbb{K}} \nabla {\varphi} \cdot {{\mathit{\boldsymbol{\nu}}}}. \end{gather*}

    It is expected to find a counter-clockwise rotating vortex with center (\hat{x},\hat{y}) , where

    \hat{x} = \dfrac{1}{r_1}\log \left( \dfrac{e^{r_1}+1}{2} \right), \quad \hat{y} = \dfrac{1}{r_2} \log \left( \dfrac{e^{r_2}+1}{2} \right).

    In particular, taking r_1 = r_2 = 4.5 , the center of the vortex is expected to appear at the top-right corner of the cavity (that is, (\hat{x},\hat{y}) = (0.829,0.829) ). Then, considering the stabilization parameters as in Section 2.3, estimating the viscosity bounds in \mu_1 = 0.74 , \mu_2 = 1.35 , we obtain the values \kappa_1 = \kappa_2 = 0.406 , \kappa_3 = 0.37 and \kappa_4 = 0.185 .

    In Table 5.2 we show the corresponding convergence history. As expected, when using the finite-element subspaces of Section 3.2 with k = 0 and k = 1 , the rates of convergence are near the optimal linear and quadratic orders, respectively. Part of the solution is shown in Figure 5.2, where a second-order approximation has been used with 724,448 DOF. The second-order method is more convenient than the first-order scheme, in terms of CPU time used to reach the same precision. Notice that a relatively high refinement was required to capture the small features of the solution (e.g. pressure and pseudostress).

    Table 2.  Convergence history for Example 2, with a quasi-uniform mesh refinement and approximations of first and second order.
    Finite Element: \mathbb{P}_0 - \mathbb{RT}_0 - {\bf P}_1 - P_1 - P_0
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    84 0.7071 4.1107 59.150 4.6740 2.1232 3.3978 8.4722 31.684
    268 0.3536 2.9724 48.185 4.8101 1.3070 2.8141 6.1465 17.922
    948 0.1768 1.8371 39.145 4.9700 1.0967 2.8205 17.313 53.771
    3,556 0.0884 1.5104 26.112 2.6239 0.6233 1.6445 2.6532 5.7624
    13,764 0.0442 0.7732 14.525 1.283 0.3384 0.8152 1.2225 2.4021
    54,148 0.0221 0.3889 7.4319 0.6359 0.1707 0.4079 0.5772 1.0130
    214,788 0.0110 0.1948 3.7392 0.3178 0.0848 0.2041 0.2942 0.5675
    IT \widetilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    7 0.5000
    9 0.2500 0.4959 0.5161 0.4126 0.5271 0.2714 0.5463 0.8221
    7 0.1250 0.8588 0.7512 0.7433 0.7982 0.5283 0.7894 0.8585
    7 0.0625 0.9184 0.9209 0.9214 0.8147 0.7781 1.0706 1.0223
    6 0.0312 0.9652 0.9438 1.0327 0.8928 1.0121 1.1193 1.2162
    6 0.0156 0.9912 0.9682 1.0122 0.9854 0.9989 1.0820 1.2245
    6 0.0078 0.9941 0.9778 1.0041 0.9985 0.9991 1.0357 1.1123
    Finite Element: \mathbb{P}_1 - \mathbb{RT}_1 - {\bf P}_2 - P_2 - P_1
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    236 0.7071 3.1423 39.183 4.5951 2.1095 2.8495 7.5735 17.122
    820 0.3536 1.8331 25.442 2.9427 1.5544 1.8810 3.3130 6.5814
    3,044 0.1768 0.6816 10.937 1.3226 0.4762 0.8158 2.4591 3.7876
    11,716 0.0884 0.2082 3.7916 0.3655 0.1364 0.1943 0.3188 0.5948
    45,956 0.0442 0.0531 1.0029 0.0996 0.0322 0.0505 0.0689 0.0355
    182,020 0.0221 0.0136 0.2582 0.0258 0.0082 0.0131 0.0177 0.0052
    724,484 0.0110 0.0034 0.0651 0.0065 0.0021 0.0033 0.0045 0.0011
    IT \widetilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    7 0.5000
    7 0.2500 0.7249 0.4977 0.5891 0.4418 0.5991 1.1924 1.6379
    6 0.1250 1.4279 1.2188 1.1543 1.7063 1.2054 0.4302 1.4443
    6 0.0625 1.7114 1.5283 1.8547 1.8037 2.0781 2.9407 2.1704
    6 0.0312 1.9693 1.9193 1.8755 2.0841 1.9458 2.2135 2.2071
    6 0.0156 1.9614 1.9565 1.9479 1.9752 1.9473 1.9564 2.1078
    6 0.0078 1.9798 1.9884 1.9810 1.9869 1.9776 1.9752 2.1907

     | Show Table
    DownLoad: CSV
    Figure 5.2.  Numerical results for Example 2. From top-left to right-bottom: XX, XY and YY components of the pseudostress, XX component of the strain rate, velocity components and vector fields, postprocessed pressure, postprocessed vorticity magnitude, and temperature. Snapshots obtained from a simulation with 724,448 DOF using a second-order approximation.

    The implementation of the numerical scheme and the accuracy for the three-dimensional case are assessed with this next computational test. The domain is the unit cube \Omega = (0,1)^3 and we consider the following closed-form solutions to the governing equations (1.1)

    \begin{gather*} {\bf u} = \begin{pmatrix} \sin(\pi x) \cos(\pi y) \cos(\pi z) \\ -2\cos(\pi x) \sin(\pi y) \cos(\pi z) \\ \cos(\pi x) \cos(\pi y) \sin(\pi z) \end{pmatrix},\quad {\bf t} = {\bf e}({\bf u}), \quad {{\mathit{\boldsymbol{\gamma}}}} = \nabla {\bf u}-{\bf e}({\bf u}), \\ p = \sin(\pi x) \sin(\pi y) \sin(\pi z), \\ {{\mathit{\boldsymbol{\sigma}}}} = \mu ({\varphi}) {\bf e}({\bf u})- {\bf u} \otimes{\bf u} -p \mathbb I,\quad {\varphi} = 1- \sin(\pi x) \cos(\pi y) \sin(\pi z), \quad \lambda = - \mathbb{K} \nabla {\varphi} \cdot {{\mathit{\boldsymbol{\nu}}}}, \end{gather*}

    with \mathbb{K} = \mathbb{I} , \mu({\varphi}) = \exp(-0.25 \, {\varphi}) , and {\bf g} = (0,0,1)^{{\tt t}} . The manufactured velocity is divergence free, it satisfies the compatibility condition (1.3) and it is used as Dirichlet datum on \Gamma . The exact temperature is uniformly bounded and it is also exploited as Dirichlet datum. In this configuration the viscosity bounds can be set as \mu_1 = 0.5 , \mu_2 = 1 and the augmentation constants take the values \kappa_1 = \kappa_2 = 0.5 , \kappa_3 = 0.25 , and \kappa_4 = 0.125 . The error history, associated with the schemes of order one and two, are performed using six steps of uniform mesh refinement applied to an initial structured tetrahedral mesh. On each level we compute approximate solutions, as well as errors and convergence rates defined as above. The boundary partition is considered conforming with the interior mesh, for sake of convenience and simplicity of the 3D mesh generation. Our findings are collected in Table 5.3, where errors and Picard iteration count are tabulated by number of degrees of freedom and meshsize. As in the 2D case, optimal error decay is observed for all individual errors (including the post-processed variables), and we also note that the errors e({{\mathit{\boldsymbol{\sigma}}}}),e({\bf u}) are dominant. One can also see that (perhaps assisted by the conformity between the interior and boundary meshes) the error associated with the boundary heat flux has a convergence systematically better than the optimal predicted by Theorem 4.5. Finally, we portray in Figure 5.3 a sample of the approximate solutions generated by the lowest-order mixed method on a relatively coarse mesh.

    Table 3.  Convergence history for Example 3, with a quasi-uniform mesh refinement and approximations of first and second order.
    Finite Element: \mathbb{P}_0 - \mathbb{RT}_0 - {\bf P}_1 - P_1 - P_0
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    900 0.7071 2.1535 6.0574 4.7925 0.6109 1.3478 1.9131 0.0137
    2,848 0.4714 1.1357 4.0980 2.9703 0.3282 1.0283 1.3774 0.0065
    12,564 0.2828 0.7437 2.5440 1.8929 0.2057 0.7164 0.7827 0.0027
    71,068 0.1571 0.3899 1.4422 1.1277 0.1254 0.4506 0.4332 0.0011
    451,690 0.0882 0.1972 0.7612 0.6351 0.0694 0.2348 0.2179 0.0006
    IT \tilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    7 0.7071
    7 0.4714 1.0245 0.9075 0.9573 1.0982 0.8846 0.8338 1.6382
    8 0.2828 0.8937 0.9558 0.9879 0.9818 0.8974 1.1057 1.6072
    8 0.1571 0.9152 0.9831 0.9893 0.9874 0.9509 1.0043 1.6075
    8 0.0882 0.9372 0.9852 1.0505 0.9756 0.9534 0.9891 1.6258
    Finite Element: \mathbb{P}_1 - \mathbb{RT}_1 - {\bf P}_2 - P_2 - P_1
    DOF h e({\bf t}) e({{\mathit{\boldsymbol{\sigma}}}}) e({\bf u}) e(p) e({{\mathit{\boldsymbol{\gamma}}}}) e({\varphi}) e(\lambda)
    3,693 0.7071 0.7084 2.5493 2.8720 0.2803 0.7668 1.0241 0.0092
    11,741 0.4714 0.2268 0.8202 0.9132 0.0846 0.1949 0.3093 0.0023
    51,825 0.2828 0.0603 0.2192 0.2609 0.0217 0.0625 0.0794 0.0005
    286,905 0.1571 0.0169 0.0516 0.0689 0.0575 0.0164 0.0197 0.0001
    1,879,712 0.0882 0.0052 0.0135 0.0186 0.0167 0.0043 0.0051 1.84e-5
    IT \tilde{h} r({\bf t}) r({{\mathit{\boldsymbol{\sigma}}}}) r({\bf u}) r(p) r({{\mathit{\boldsymbol{\gamma}}}}) r({\varphi}) r(\lambda)
    6 0.7071
    7 0.4714 1.8586 1.8163 1.8545 1.8611 1.7819 1.9314 2.5877
    7 0.2828 1.9004 1.8805 1.8949 1.9072 1.9384 1.8458 2.6167
    8 0.1571 1.9153 1.9572 1.8973 1.9526 1.9742 1.9628 2.5709
    8 0.0882 1.9457 1.9694 1.9407 1.9644 1.9866 1.9764 2.6851

     | Show Table
    DownLoad: CSV
    Figure 5.3.  Example 3. Approximate solutions (from left to right and from up to down): magnitude of strain rate, pseudostress, velocity magnitude and arrows, postprocessed vorticity magnitude, postprocessed pressure, and temperature. Snapshots obtained from a simulation with a lowest-order approximation and 451,690 DOF.

    To close this section we conduct a benchmark test of a differentially heated, two-dimensional cavity of width 2 and height 1 (dimensionless units). The nonlinear viscosity used here is also explicitly dependent on depth, as in the models used in the context of mantle convection [12,40],

    \mu({\varphi}) = 2\exp(-9.7044 {\varphi}+4.1588(1-y)),

    and the boundary treatment proceeds as follows. For the thermal energy conservation, the boundary is split into \Gamma_D (top and bottom edges of the box) and \Gamma_N (vertical walls) where temperature and heat flux are prescribed, respectively. The boundary temperature on \Gamma_D is set to 0 on the top and 1 on the bottom edges, and it suffices with adequately defining \varphi_D in the formulation. On \Gamma_N we simply set zero thermal flux (the vertical walls are considered insulated). For the Navier-Stokes equations, and following [40], we impose free slip conditions defined as {\bf u}\cdot{{\mathit{\boldsymbol{\nu}}}} = {{\mathit{\boldsymbol{\sigma}}}}{{\mathit{\boldsymbol{\nu}}}}\cdot{\bf m} = 0 , where {\bf m} denotes the tangential vector. These conditions imply that the Galerkin term for boundary velocity of Section 2 is now modified as

    \kappa_4 \int_{\Gamma} ({\bf u}\cdot{{\mathit{\boldsymbol{\nu}}}})\,({\bf v}\cdot{{\mathit{\boldsymbol{\nu}}}}) = 0\qquad \forall {\bf v}\in {\bf H}^1(\Omega),

    and we also require the additional condition

    \kappa_5 \int_{\Gamma} ({{\mathit{\boldsymbol{\sigma}}}}{{\mathit{\boldsymbol{\nu}}}}\cdot{\bf m})\,({{\mathit{\boldsymbol{\tau}}}}{{\mathit{\boldsymbol{\nu}}}}\cdot{\bf m}) = 0\qquad \forall {{\mathit{\boldsymbol{\tau}}}} \in \mathbb{H}(\mathbf{div} ; {\Omega} ),

    where \kappa_5>0 can be taken, e.g., equal to \kappa_4 . The coupled Boussinesq equations can be scaled so that the thermal conductivity is inverse to the adimensional Rayleigh number Ra = 10^4 , and the buoyancy term is simply {\varphi}{\bf g} with {\bf g} = (0,1)^{{\tt t}} . In order to produce convection regimes we require extending the formulation to the transient case, adding to the thermal energy equation the term \partial_t \varphi and to the momentum equation the acceleration \partial_t {\bf u} (which for mantle convection could eventually be dropped as it scales with the inverse of a very large Prandtl number). These time derivatives are approximated with the backward Euler scheme, using a constant timestep of \Delta t = 0.1 and running the coupled Boussinesq equations until the final time t = 12 . The initial velocity is zero and the initial temperature is {\varphi}(0) = 1-y^2+0.01\cos(4\pi x)\sin(\pi y) . We employ a relatively coarse structured mesh with 32K triangles (but graded to be refined in the y- direction near the bottom and top boundaries), which results in a mixed-primal method (for the lowest-order case) having 208,986 DOF. For this test we observe that an average of five fixed-point iterations are required to reach the fixed tolerance of 10^{-7} . The Rayleigh number used here was sufficiently large to generate thermal blob-shaped forms (see the plumes in the temperature plot of Figure 5.4). The obtained convection modes are consistent with the flow patterns observed in [40] (see also [31,33]). They indicate that the velocity is driven by the temperature difference.

    Figure 4.  Example 4. Approximate velocity line integral contours and temperature profiles for the differentially heated cavity at times t = 4 , t = 8 , t = 12 , computed with the lowest-order scheme and a backward Euler time stepping.


    [1] Abu-Ghunmi D, Bino A, Tayeh M (2015) Idiosyncratic Risk and Corporate Governance: Evidence from Jordan. Emerg Mark Financ Tr 51: S40–S50. https://doi.org/10.1080/1540496X.2015.1026717 doi: 10.1080/1540496X.2015.1026717
    [2] Adams R (2005) Powerful CEOs and Their Impact on Corporate Performance. Rev Financ Stud 18: 1403–1432. https://doi.org/10.1093/rfs/hhi030 doi: 10.1093/rfs/hhi030
    [3] Aharony J, Liu C, Yawson A (2015) Corporate litigation and executive turnover. J Corp Financ 34: 268–292. https://doi.org/10.1016/j.jcorpfin.2015.07.009 doi: 10.1016/j.jcorpfin.2015.07.009
    [4] Alon I, Wang H, Shen J, et al. (2014) Chinese state-owned enterprises go global. J Bus Strat 35: 3–18. https://doi.org/10.1108/JBS-12-2013-0118 doi: 10.1108/JBS-12-2013-0118
    [5] Andreou PC, Antoniou C, Horton J, et al. (2016) Corporate Governance and Firm-specific Stock Price Crashes. Eur Financ Manage 22: 916–956. https://doi.org/10.1111/eufm.12084 doi: 10.1111/eufm.12084
    [6] Andreou PC, Louca C, Petrou AP (2017) CEO Age and Stock Price Crash Risk. Rev Financ 21: 1287–1325. https://doi.org/10.1093/rof/rfw056 doi: 10.1093/rof/rfw056
    [7] Ang A, Hodrick R, Xing Y, et al. (2008) High idiosyncratic volatility and low returns: International and further U.S. evidence. J Financ Econ 91: 1–23. https://doi.org/10.3386/w13739 doi: 10.3386/w13739
    [8] Angeletos GM (2007) Uninsured idiosyncratic investment risk and aggregate saving. Rev Econ Dyn 10: 1–30. https://doi.org/10.1016/j.red.2006.11.001 doi: 10.1016/j.red.2006.11.001
    [9] An Z, Li DH, Yu J (2015) Firm crash risk, information environment, and speed of leverage adjustment. J Corp Financ 31: 132–151. https://doi.org/10.1016/j.jcorpfin.2015.01.015 doi: 10.1016/j.jcorpfin.2015.01.015
    [10] Bali TG, Cakici N (2008) Idiosyncratic volatility and the cross section of expected returns. J Financ Quant Anal 43: 29–58. https://doi.org/10.1017/S002210900000274X doi: 10.1017/S002210900000274X
    [11] Bansal P, Clelland I (2004) Talking trash: Legitimacy, impression management, and unsystematic risk in the context of the natural environment. Acad Manage J 47: 93–103. https://doi.org/10.2307/20159562 doi: 10.2307/20159562
    [12] Bartram SM, Brown G, Stulz RM (2012) Why Are U.S. Stocks More Volatile? J Financ 67: 1329–1370. https://doi.org/10.1111/j.1540-6261.2012.01749.x doi: 10.1111/j.1540-6261.2012.01749.x
    [13] Bartram SM, Brown GW, Conrad J (2011) The Effects of Derivatives on Firm Risk and Value. J Financ Quant Anal 46: 967–999. https://doi.org/10.1017/S0022109011000275 doi: 10.1017/S0022109011000275
    [14] Beatty RP, Ritter JR (1986) Investment Banking, Reputation, and the Underpricing of Initial Public Offerings. J Financ Econ 15: 213–232. https://doi.org/10.1016/0304-405x(86)90055-3 doi: 10.1016/0304-405x(86)90055-3
    [15] Benmelech E, Kandel E, Veronesi P (2010) Stock-Based Compensation and Ceo (Dis)Incentives. Q J Econ 125: 1769–1820. https://doi.org/10.1162/qjec.2010.125.4.1769 doi: 10.1162/qjec.2010.125.4.1769
    [16] Bernard Y, Godard L, Zouaoui M (2016) The Effect of CEOs' Turnover on the Corporate Sustainability Performance of French Firms. J Bus Ethics 150: 1049–1069. https://doi.org/10.1007/s10551-016-3178-7 doi: 10.1007/s10551-016-3178-7
    [17] Bernile G, Bhagwat V, Yonker S (2018) Board diversity, firm risk, and corporate policies. J Financ Econ 127: 588–612. https://doi.org/10.1016/j.jfineco.2017.12.009 doi: 10.1016/j.jfineco.2017.12.009
    [18] Brandt MW, Brav A, Graham JR, et al. (2010) The Idiosyncratic Volatility Puzzle: Time Trend or Speculative Episodes? Re Financ Stud 23: 863–899. https://doi.org/10.1093/rfs/hhp087 doi: 10.1093/rfs/hhp087
    [19] Brickley JA (2003) Empirical research on CEO turnover and firm-performance: a discussion. J Account Econ 36: 227–233. https://doi.org/10.1016/j.jacceco.2003.09.003 doi: 10.1016/j.jacceco.2003.09.003
    [20] Boyd JH, De Nicolo G (2005) The theory of bank risk taking and competition revisited. J Financ 60: 1329–1343. https://doi.org/10.1111/j.1540-6261.2005.00763.x doi: 10.1111/j.1540-6261.2005.00763.x
    [21] Brown G, Kapadia N (2007) Firm-specific risk and equity market development. J Financ Econ 84: 358–388. https://doi.org/10.1016/j.jfineco.2006.03.003 doi: 10.1016/j.jfineco.2006.03.003
    [22] Bushman R, Dai ZL, Wang X (2010) Risk and CEO turnover. J Financ Econ 96: 381–398. https://doi.org/10.1016/j.jfineco.2010.03.001 doi: 10.1016/j.jfineco.2010.03.001
    [23] Cai X, Jiang F, Kang JK (2019) Remote Board Meetings and Board Monitoring Effectiveness: Evidence from China. SSRN Electron J. https://doi.org/10.2139/ssrn.3450328 doi: 10.2139/ssrn.3450328
    [24] Callen JL, & Fang XH (2015) Religion and Stock Price Crash Risk. J Financ Quant Anal 50: 169–195. https://doi.org/10.1017/S0022109015000046 doi: 10.1017/S0022109015000046
    [25] Campbell JY, Lettau M, Malkiel BG, et al. (2001) Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk. J Financ 56: 1–43. https://doi.org/10.1111/0022-1082.00318 doi: 10.1111/0022-1082.00318
    [26] Campbell JY, Taksler GB (2003) Equity volatility and corporate bond yields. J Financ 58: 2321–2349. https://doi.org/10.1046/j.1540-6261.2003.00607.x doi: 10.1046/j.1540-6261.2003.00607.x
    [27] Cao XP, Pan XF, Qian MJ, et al. (2017) Political capital and CEO entrenchment: Evidence from CEO turnover in Chinese non-SOEs. J Corp Financ 42: 1–14. https://doi.org/10.1016/j.jcorpfin.2016.11.003 doi: 10.1016/j.jcorpfin.2016.11.003
    [28] Chakraborty A, Sheikh S, Subramanian N (2007) Termination risk and managerial risk taking. J Corp Financ 13: 170–188. https://doi.org/10.1016/j.jcorpfin.2006.04.001 doi: 10.1016/j.jcorpfin.2006.04.001
    [29] Chang EC, Wong SML (2009) Governance with multiple objectives: Evidence from top executive turnover in China. J Corp Financ 15: 230–244. https://doi.org/10.1016/j.jcorpfin.2008.10.003 doi: 10.1016/j.jcorpfin.2008.10.003
    [30] Chen CL, Kim JB, Yao L (2017) Earnings smoothing: Does it exacerbate or constrain stock price crash risk? J Corp Financ 42: 36–54. https://doi.org/10.1016/j.jcorpfin.2016.11.004 doi: 10.1016/j.jcorpfin.2016.11.004
    [31] Chen H, Miao JJ, Wang N (2010) Entrepreneurial Finance and Nondiversifiable Risk. Rev Financ Stud 23: 4348–4388. https://doi.org/10.1093/rfs/hhq122 doi: 10.1093/rfs/hhq122
    [32] Chen Z, Petkova R (2010) Does Idiosyncratic Volatility Proxy for Risk Exposure? SSRN Electron J 25: 2745–2787. https://doi.org/10.2139/ssrn.1572895 doi: 10.2139/ssrn.1572895
    [33] Child J, Tse DK (2001) China's transition and its implications for international business. J Int Bus Stud 32: 5–21. https://doi.org/10.1057/palgrave.jibs.8490935 doi: 10.1057/palgrave.jibs.8490935
    [34] Chung HH, Wynn JP (2008) Managerial legal liability coverage and earnings conservatism. J Account Econ 46: 135–153. https://doi.org/10.1016/j.jacceco.2008.03.002 doi: 10.1016/j.jacceco.2008.03.002
    [35] Chyz JA, Gaertner FB (2017) Can Paying "Too Much" or "Too Little" Tax Contribute to Forced CEO Turnover? Account Rev 93: 103–130. https://doi.org/10.2308/accr-51767 doi: 10.2308/accr-51767
    [36] Clayton MC, Hartzell JC, Rosenberg J (2005) The impact of CEO turnover on equity volatility. J Bus 78: 1779–1808. https://doi.org/10.1086/431442 doi: 10.1086/431442
    [37] Datta S, Iskandar-Datta M, Singh V (2017) The impact of idiosyncratic risk on accrual management. Int J Managerial Financ 13: 70–90. https://doi.org/10.1108/ijmf-01-2016-0013 doi: 10.1108/ijmf-01-2016-0013
    [38] Demerjian P, Lev B, McVay S (2012) Quantifying Managerial Ability: A New Measure and Validity Tests. Manage Scienc 58: 1229–1248. https://doi.org/10.1287/mnsc.1110.1487 doi: 10.1287/mnsc.1110.1487
    [39] Deng SY, Intintoli VJ, Zhang A (2019) CEO Turnover, Information Uncertainty, and Debt Contracting. Q J Financ 9. https://doi.org/10.1142/S2010139219500010 doi: 10.1142/S2010139219500010
    [40] Dewatripont M, Maskin E (1995) Credit and Efficiency in Centralized and Decentralized Economies. Rev Econ Stud 62: 541–555. https://doi.org/10.2307/2298076 doi: 10.2307/2298076
    [41] Dumitrescu A, Zakriya M (2021) Stakeholders and the stock price crash risk: What matters in corporate social performance? J Corp Financ 67. https://doi.org/10.1016/j.jcorpfin.2020.101871 doi: 10.1016/j.jcorpfin.2020.101871
    [42] Farrell KA, Whidbee DA (2002) Monitoring by the financial press and forced CEO turnover. J Bank Financ 26: 2249–2276. https://doi.org/10.1016/S0378-4266(01)00183-2 doi: 10.1016/S0378-4266(01)00183-2
    [43] Fee CE, Hadlock CJ, Huang J, et al. (2018) Robust Models of CEO Turnover: New Evidence on Relative Performance Evaluation. Rev Corp Financ Stud 7: 70–100. https://doi.org/10.1093/rcfs/cfx018 doi: 10.1093/rcfs/cfx018
    [44] Fee CE, Hadlock CJ, Pierce JR (2013) Managers with and without Style: Evidence Using Exogenous Variation. Rev Financ Stud 26: 567–601. https://doi.org/10.1093/rfs/hhs131 doi: 10.1093/rfs/hhs131
    [45] Ferreira MA, Laux PA (2007) Corporate governance, idiosyncratic risk, and information flow. J Financ 62: 951–989. https://doi.org/10.1111/j.1540-6261.2007.01228.x doi: 10.1111/j.1540-6261.2007.01228.x
    [46] Firth M, Fung PMY, Rui OM (2006) Firm performance, governance structure, and top management turnover in a transitional economy. J Manage Stud 43: 1289–1330. https://doi.org/10.1111/j.1467-6486.2006.00621.x doi: 10.1111/j.1467-6486.2006.00621.x
    [47] Fisman RJ, Khurana R, Rhodes-Kropf M, et al. (2014) Governance and CEO Turnover: Do Something or Do the Right Thing? Manage Sci 60: 319–337. https://doi.org/10.1287/mnsc.2013.1759 doi: 10.1287/mnsc.2013.1759
    [48] Fu FJ (2009) Idiosyncratic risk and the cross-section of expected stock returns. J Financ Econ 91: 24–37. https://doi.org/10.1016/j.jfineco.2008.02.003 doi: 10.1016/j.jfineco.2008.02.003
    [49] Gerlach R, Obaydin I, Zurbruegg R (2015) The impact of leverage on the idiosyncratic risk and return relationship of REITs around the financial crisis. Int Rev Econ Financ 38: 207–219. https://doi.org/10.1016/j.iref.2015.02.029 doi: 10.1016/j.iref.2015.02.029
    [50] Goel AM, Thakor AV (2008) Overconfidence, CEO Selection, and Corporate Governance. J Financ 63: 2737–2784. https://doi.org/10.1111/j.1540-6261.2008.01412.x doi: 10.1111/j.1540-6261.2008.01412.x
    [51] Gormley TA, Matsa DA (2016) Playing it safe? Managerial preferences, risk, and agency conflicts. J Financ Econ 122: 431–455 https://doi.org/10.1016/j.jfineco.2016.08.002 doi: 10.1016/j.jfineco.2016.08.002
    [52] Goyal A, Santa-Clara P (2003) Idiosyncratic risk matters! J Financ 58: 975–1007. https://doi.org/10.1111/1540-6261.00555 doi: 10.1111/1540-6261.00555
    [53] Guo LX, Masulis RW (2015) Board Structure and Monitoring: New Evidence from CEO Turnovers. Rev Financ Stud 28: 2770–2811. https://doi.org/10.1093/rfs/hhv038 doi: 10.1093/rfs/hhv038
    [54] Habib A, Hasan MM, Jiang HY (2018) Stock price crash risk: review of the empirical literature. Account Financ 58: 211–251. https://doi.org/10.1111/acfi.12278 doi: 10.1111/acfi.12278
    [55] Haß LH, Vergauwe S, Zhang Z (2017) State-ownership and bank loan contracting: evidence from corporate fraud. Eur J Financ 25: 550–567. https://doi.org/10.1080/1351847x.2017.1328454 doi: 10.1080/1351847x.2017.1328454
    [56] Huang CW, Ho PH, Lin CY, Yen JF (2014) Firm age, idiosyncratic risk, and long-run SEO underperformance. Int Rev Econ Financ 34: 246–266. https://doi.org/10.1016/j.iref.2014.08.009 doi: 10.1016/j.iref.2014.08.009
    [57] Huson MR, Malatesta PH, Parrino R (2004) Managerial succession and firm performance. J Financ Econ 74: 237–275. https://doi.org/10.1016/j.jfineco.2003.08.002 doi: 10.1016/j.jfineco.2003.08.002
    [58] Hutton AP, Marcus AJ, Tehranian H (2009) Opaque financial reports, R2, and crash risk. J Financ Econ 94: 67–86. https://doi.org/10.1016/j.jfineco.2008.10.003 doi: 10.1016/j.jfineco.2008.10.003
    [59] Irvine PJ, Pontiff J (2009) Idiosyncratic Return Volatility, Cash Flows, and Product Market Competition. Rev Financ Stud 22: 1149–1177. https://doi.org/10.1093/rfs/hhn039 doi: 10.1093/rfs/hhn039
    [60] Jensen MC, Meckling WH (1976) Theory of the firm: Managerial behavior, agency costs and ownership structure. J Financ Econ 3: 305–360. https://doi.org/10.1016/0304-405x(76)90026-x doi: 10.1016/0304-405x(76)90026-x
    [61] Jenter D, Kanaan F (2015) CEO Turnover and Relative Performance Evaluation. J Financ 70: 2155–2184. https://doi.org/10.1111/jofi.12282 doi: 10.1111/jofi.12282
    [62] Jenter D, Lewellen K (2021) Performance-Induced CEO Turnover. Rev Financ Stud 34: 569–617. https://doi.org/10.1093/rfs/hhaa069 doi: 10.1093/rfs/hhaa069
    [63] Jin L, Myers S (2006) R2 around the world: New theory and new tests. J Financ Econ 79: 257–292. https://doi.org/10.1016/j.jfineco.2004.11.003 doi: 10.1016/j.jfineco.2004.11.003
    [64] John K, Litov L, Yeung B (2008) Corporate governance and risk-taking. J Financ 63: 1679–1728. https://doi.org/10.1111/j.1540-6261.2008.01372.x doi: 10.1111/j.1540-6261.2008.01372.x
    [65] Kato T, Long C (2006) CEO turnover, firm performance, and enterprise reform in China: Evidence from micro data. J Comp Econ 34: 796-817. https://doi.org/10.1016/j.jce.2006.08.002 doi: 10.1016/j.jce.2006.08.002
    [66] Kim, J. B., Li, Y. H., & Zhang, L. D. (2011a). CFOs versus CEOs: Equity incentives and crashes. Journal of Financial Economics, 101(3), 713-730. doi: 10.1016/j.jfineco.2011.03.013 doi: 10.1016/j.jfineco.2011.03.013
    [67] Kim JB, Li YH, Zhang LD (2011b) Corporate tax avoidance and stock price crash risk: Firm-level analysis. J Financ Econ 100: 639–662. https://doi.org/10.1016/j.jfineco.2010.07.007 doi: 10.1016/j.jfineco.2010.07.007
    [68] Kim JB, Wang Z, Zhang LD (2016) CEO Overconfidence and Stock Price Crash Risk. Contemp Account Res 33: 1720–1749. https://doi.org/10.1111/1911-3846.12217 doi: 10.1111/1911-3846.12217
    [69] Kim JB, Zhang L (2016) Accounting Conservatism and Stock Price Crash Risk: Firm-level Evidence. Contemp Account Res 33: 412–441. https://doi.org/10.1111/1911-3846.12112 doi: 10.1111/1911-3846.12112
    [70] Kravet T, Shevlin T (2010) Accounting restatements and information risk. Rev Account Stud 15: 264–294. https://doi.org/10.1007/s11142-009-9103-x doi: 10.1007/s11142-009-9103-x
    [71] Kreps DM, Wilson R (1982) Reputation and Imperfect Information. J Econ Theory 27: 253–279. https://doi.org/10.1016/0022-0531(82)90030-8 doi: 10.1016/0022-0531(82)90030-8
    [72] Lee W, Wang L (2016) Do political connections affect stock price crash risk? Firm-level evidence from China. Rev Quant Financ Account 48: 643–676. https://doi.org/10.1007/s11156-016-0563-3 doi: 10.1007/s11156-016-0563-3
    [73] Li HB, Meng LS, Wang Q, et al. (2008) Political connections, financing and firm performance: Evidence from Chinese private firms. J Dev Econ 87: 283–299. https://doi.org/10.1016/j.jdeveco.2007.03.001 doi: 10.1016/j.jdeveco.2007.03.001
    [74] Li XR, Chan KC (2016) Communist party control and stock price crash risk: Evidence from China. Econ Lett 141: 5–7. https://doi.org/10.1016/j.econlet.2016.01.018 doi: 10.1016/j.econlet.2016.01.018
    [75] Li XR, Wang SS, Wang X (2017) Trust and stock price crash risk: Evidence from China. J Bank Financ 76: 74–91. https://doi.org/10.1016/j.jbankfin.2016.12.003 doi: 10.1016/j.jbankfin.2016.12.003
    [76] Li YW, Zeng YQ (2019) The impact of top executive gender on asset prices: Evidence from stock price crash risk. J Corp Financ 58: 528–550. https://doi.org/10.1016/j.jcorpfin.2019.07.005 doi: 10.1016/j.jcorpfin.2019.07.005
    [77] Tan M, Liu B (2016) CEO's managerial power, board committee memberships and idiosyncratic volatility. In Rev Financ Anal 48: 21–30. https://doi.org/10.1016/j.irfa.2016.09.003 doi: 10.1016/j.irfa.2016.09.003
    [78] Luo JH, Gong MN, Lin YL, et al. (2016) Political connections and stock price crash risk: Evidence from China. Econ Lett 147: 90–92. https://doi.org/10.1016/j.econlet.2016.08.024 doi: 10.1016/j.econlet.2016.08.024
    [79] Xu Y, Malkiel BG (2002) Investigating the Behavior of Idiosyncratic Volatility. J Bus 76: 613–644. https://doi.org/10.1086/377033 doi: 10.1086/377033
    [80] Masulis RW, Mobbs S (2011) Are All Inside Directors the Same? Evidence from the External Directorship Market. J Financ 66: 823–872. https://doi.org/10.1111/j.1540-6261.2011.01653.x doi: 10.1111/j.1540-6261.2011.01653.x
    [81] Milgrom P, Roberts J (1986) Price and Advertising Signals of Product Quality. J Polit Econ 94: 796–821. https://doi.org/10.1086/261408 doi: 10.1086/261408
    [82] Mishra DR (2020) CEO Private Firm Experience and Idiosyncratic Risk. https://doi.org/10.2139/ssrn.3720156
    [83] Morck R, Yeung B, Yu W (2000) The information content of stock markets: why do emerging markets have synchronous stock price movements? J Financ Econ 58: 215–260. https://doi.org/10.1016/s0304-405x(00)00071-4 doi: 10.1016/s0304-405x(00)00071-4
    [84] Nguyen P (2011) Corporate governance and risk-taking: Evidence from Japanese firms. Pac-Basin Financ J 19: 278–297. https://doi.org/10.1016/j.pacfin.2010.12.002 doi: 10.1016/j.pacfin.2010.12.002
    [85] Panousi V, Papanikolaou D (2012) Investment, Idiosyncratic Risk, and Ownership. J Financ 67: 1113–1148. https://doi.org/10.1111/j.1540-6261.2012.01743.x doi: 10.1111/j.1540-6261.2012.01743.x
    [86] Patton AJ, Verardo M (2012) Does Beta Move with News? Firm-Specific Information Flows and Learning About Profitability. SSRN Electron J. https://doi.org/10.2139/ssrn.1361813 doi: 10.2139/ssrn.1361813
    [87] Peters FS, Wagner AF (2014) The Executive Turnover Risk Premium. J Financ 69: 1529–1563. https://doi.org/10.1111/jofi.12166 doi: 10.1111/jofi.12166
    [88] Pi L, Lowe J (2009) Can a powerful CEO avoid involuntary replacement?—An empirical study from China. Asia Pac J Manage 28: 775–805. https://doi.org/10.1007/s10490-009-9178-8 doi: 10.1007/s10490-009-9178-8
    [89] Piotroski JD, Wong TJ, Zhang TY (2015) Political Incentives to Suppress Negative Information: Evidence from Chinese Listed Firms. J Account Res 53: 405–459. https://doi.org/10.1111/1475-679x.12071 doi: 10.1111/1475-679x.12071
    [90] Preuss S, Königsgruber R (2021) How do corporate political connections influence financial reporting? A synthesis of the literature. J Account Public Policy 40. https://doi.org/10.1016/j.jaccpubpol.2020.106802 doi: 10.1016/j.jaccpubpol.2020.106802
    [91] Qian Y, Roland G (1999) Federalism and the Soft Budget Constraint. SSRN Electron J. https://doi.org/10.2139/ssrn.149988 doi: 10.2139/ssrn.149988
    [92] Raheja CG (2005) Determinants of board size and composition: A theory of corporate boards. J Financ Quant Anal 40: 283–306. https://doi.org/10.1017/S0022109000002313 doi: 10.1017/S0022109000002313
    [93] Rajgopal S, Venkatachalam M (2011) Financial reporting quality and idiosyncratic return volatility. J Account Econ 51: 1–20. https://doi.org/10.1016/j.jacceco.2010.06.001 doi: 10.1016/j.jacceco.2010.06.001
    [94] Robin AJ, Zhang H (2015) Do Industry-Specialist Auditors Influence Stock Price Crash Risk? J Pract Theory 34: 47–79. https://doi.org/10.2308/ajpt-50950 doi: 10.2308/ajpt-50950
    [95] Sassen R, Hinze AK, Hardeck I (2016) Impact of ESG factors on firm risk in Europe. J Bus Econ 86: 867–904. https://doi.org/10.1007/s11573-016-0819-3 doi: 10.1007/s11573-016-0819-3
    [96] Suchman MC (1995) Managing Legitimacy—Strategic and Institutional Approaches. Acad Manage Rev 20: 571–610. https://doi.org/10.2307/258788 doi: 10.2307/258788
    [97] Tran QT, Nguyen XM, Nguyen TH (2016) CEO duality, state shareholder and CEO turnover: Evidence from Vietnamese stock market. Bus Econ Horiz 12: 113–120. https://doi.org/10.15208/beh.2016.09 doi: 10.15208/beh.2016.09
    [98] Wang Q, Wong TJ, Xia LJ (2008) State ownership, the institutional environment, and auditor choice: Evidence from China. J Account Econ 46: 112–134. https://doi.org/10.1016/j.jacceco.2008.04.001 doi: 10.1016/j.jacceco.2008.04.001
    [99] Westphal JD, Deephouse DL (2011) Avoiding Bad Press: Interpersonal Influence in Relations Between CEOs and Journalists and the Consequences for Press Reporting About Firms and Their Leadership. Organ Sci 22: 1061–1086. https://doi.org/10.1287/orsc.1100.0563 doi: 10.1287/orsc.1100.0563
    [100] Wiersema MF, Zhang Y (2013) Executive turnover in the stock option backdating wave: The impact of social context. Strat Manage J 34: 590–609. https://doi.org/10.1002/smj.2030 doi: 10.1002/smj.2030
    [101] Wooldridge JM (2002) Econometric analysis of cross section and panel data.
    [102] Xu NH, Li XR, Yuan QB, et al. (2014) Excess perks and stock price crash risk: Evidence from China. J Corp Financ 25: 419–434. https://doi.org/10.1016/j.jcorpfin.2014.01.006 doi: 10.1016/j.jcorpfin.2014.01.006
    [103] Xu YX, Malkiel BG (2003) Investigating the behavior of idiosyncratic volatility. J Bus 76: 613–644. https://doi.org/10.1086/377033 doi: 10.1086/377033
    [104] Wiersema MF, Zhang Y (2011) CEO Dismissal: The Role of Investment Analysts. Strat Manage J 32: 1161–1182. https://doi.org/10.1002/smj.932 doi: 10.1002/smj.932
    [105] Zhou M, Jiang K, Chen Z (2022) Temperature and corporate risk taking in China. Financ Res Lett 48. https://doi.org/10.1016/j.frl.2022.102862 doi: 10.1016/j.frl.2022.102862
    [106] Zhang X (2016) Income Smoothing, Idiosyncratic Risk & CEO Turnover. J Math Financ 06: 1–13. https://doi.org/10.4236/jmf.2016.61001 doi: 10.4236/jmf.2016.61001
    [107] Zwiebel J (1995) Corporate Conservatism and Relative Compensation. J Polit Econ 103: 1–25. DOI:10.1086/261973 doi: 10.1086/261973
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