Research article

The worst-case scenario: robust portfolio optimization with discrete distributions and transaction costs

  • Received: 14 March 2024 Revised: 27 May 2024 Accepted: 30 May 2024 Published: 28 June 2024
  • MSC : 91B05, 91G10

  • This research introduces min-max portfolio optimization models that incorporating transaction costs and focus on robust Entropic value-at-risk. This study offers a unified approach to handl the distribution of random parameters that affect the reward and risk aspects. Utilizing the duality theorem, the study transforms the optimization models into manageable forms, thereby accommodating the underlying random variables' discrete box and ellipsoidal distributions. The impact of transaction costs on optimal portfolio selection is examined through numerical examples under a robust return-risk framework. The results underscore the importance of the proposed model in safeguarding capital and reducing exposure to extreme risks, thus outperforming other strategies documented in the literature. This demonstrates the model's effectiveness in balancing maximizing returns and minimizing potential losses, making it a valuable tool for investors that seek to navigate uncertain financial markets.

    Citation: Ebenezer Fiifi Emire Atta Mills. The worst-case scenario: robust portfolio optimization with discrete distributions and transaction costs[J]. AIMS Mathematics, 2024, 9(8): 20919-20938. doi: 10.3934/math.20241018

    Related Papers:

    [1] Ibtesam Alshammari, Islam M. Taha . On fuzzy soft β-continuity and β-irresoluteness: some new results. AIMS Mathematics, 2024, 9(5): 11304-11319. doi: 10.3934/math.2024554
    [2] R. Mareay, Radwan Abu-Gdairi, M. Badr . Soft rough fuzzy sets based on covering. AIMS Mathematics, 2024, 9(5): 11180-11193. doi: 10.3934/math.2024548
    [3] Fahad Alsharari, Ahmed O. M. Abubaker, Islam M. Taha . On r-fuzzy soft γ-open sets and fuzzy soft γ-continuous functions with some applications. AIMS Mathematics, 2025, 10(3): 5285-5306. doi: 10.3934/math.2025244
    [4] Abdelghani Taouti, Waheed Ahmad Khan . Fuzzy subnear-semirings and fuzzy soft subnear-semirings. AIMS Mathematics, 2021, 6(3): 2268-2286. doi: 10.3934/math.2021137
    [5] Arife Atay . Disjoint union of fuzzy soft topological spaces. AIMS Mathematics, 2023, 8(5): 10547-10557. doi: 10.3934/math.2023535
    [6] Samirah Alzahrani, A. A. Nasef, N. Youns, A. I. EL-Maghrabi, M. S. Badr . Soft topological approaches via soft γ-open sets. AIMS Mathematics, 2022, 7(7): 12144-12153. doi: 10.3934/math.2022675
    [7] Rui Gao, Jianrong Wu . Filter with its applications in fuzzy soft topological spaces. AIMS Mathematics, 2021, 6(3): 2359-2368. doi: 10.3934/math.2021143
    [8] Warud Nakkhasen, Teerapan Jodnok, Ronnason Chinram . Intra-regular semihypergroups characterized by Fermatean fuzzy bi-hyperideals. AIMS Mathematics, 2024, 9(12): 35800-35822. doi: 10.3934/math.20241698
    [9] Fenhong Li, Liang Kong, Chao Li . Non-global nonlinear mixed skew Jordan Lie triple derivations on prime -rings. AIMS Mathematics, 2025, 10(4): 7795-7812. doi: 10.3934/math.2025357
    [10] Umar Ishtiaq, Fahad Jahangeer, Doha A. Kattan, Manuel De la Sen . Generalized common best proximity point results in fuzzy multiplicative metric spaces. AIMS Mathematics, 2023, 8(11): 25454-25476. doi: 10.3934/math.20231299
  • This research introduces min-max portfolio optimization models that incorporating transaction costs and focus on robust Entropic value-at-risk. This study offers a unified approach to handl the distribution of random parameters that affect the reward and risk aspects. Utilizing the duality theorem, the study transforms the optimization models into manageable forms, thereby accommodating the underlying random variables' discrete box and ellipsoidal distributions. The impact of transaction costs on optimal portfolio selection is examined through numerical examples under a robust return-risk framework. The results underscore the importance of the proposed model in safeguarding capital and reducing exposure to extreme risks, thus outperforming other strategies documented in the literature. This demonstrates the model's effectiveness in balancing maximizing returns and minimizing potential losses, making it a valuable tool for investors that seek to navigate uncertain financial markets.


    In this paper, we consider the existence of solutions and a generalized Lyapunov-type inequality to the following boundary value problem for differential equation of variable order

    {Dq(t)0+x(t)+f(t,x)=0,  0<t<T,x(0)=0,x(T)=0, (1.1)

    where 0<T<+, Dq(t)0+ denotes derivative of variable order([1,2,3,4]) defined by

    Dq(t)0+x(t)=d2dt2t0(ts)1q(s)Γ(2q(s))x(s)ds,t>0, (1.2)

    and

    I2q(t)0+x(t)=t0(ts)1q(s)Γ(2q(s))x(s)ds,t>0, (1.3)

    denotes integral of variable order 2q(t), 1<q(t)2, 0tT. f:(0,T]×RR is given continuous function satisfying some assumption conditions.

    The operators of variable order, which fall into a more complex operator category, are the derivatives and integrals whose order is the function of certain variables. The variable order fractional derivative is an extension of constant order fractional derivative. In recent years, the operator and differential equations of variable order have been applied in engineering more and more frequently, for the examples and details, see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17].

    The subject of fractional calculus has gained considerable popularity and importance due to its frequent appearance in different research areas and engineering, such as physics, chemistry, control of dynamical systems etc. Recently, many people paid attention to the existence and uniqueness of solutions to boundary value problems for fractional differential equations. Although the existing literature on solutions of boundary value problems of fractional order (constant order) is quite wide, few papers deal with the existence of solutions to boundary value problems of variable order. According to (1.2) and (1.3), it is obviously that when q(t) is a constant function, i.e. q(t)q (q is a finite positive constant), then Iq(t)0+,Dq(t)0+ are the usual Riemann-Liouville fractional integral and derivative [18].

    The following properties of fractional calculus operators Dq0+, Iq0+ play an important part in discussing the existence of solutions of fractional differential equations.

    Proposition 1.1. [18] The equality Iγ0+Iδ0+f(t)=Iγ+δ0+f(t), γ>0,δ>0 holds for fL(0,b),0<b<+.

    Proposition 1.2. [18] The equality Dγ0+Iγ0+f(t)=f(t), γ>0 holds for fL(0,b),0<b<+.

    Proposition 1.3. [18] Let 1<α2. Then the differential equation

    Dα0+f=0

    has solutions

    f(t)=c1tα1+c2tα2,c1,c2R.

    Proposition 1.4. [18] Let 1<α2, f(t)L(0,b), Dα0+fL(0,b). Then the following equality holds

    Iα0+Dα0+f(t)=f(t)+c1tα1+c2tα2,c1,c2R.

    These properties play a very important role in considering the existence of the solutions of differential equations for the Riemnn-Liouville fractional derivative, for details, please refer to [18]. However, from [1,2,16], for general functions h(t),g(t), we notice that the semigroup property doesn't hold, i.e., Ih(t)a+Ig(t)a+Ih(t)+g(t)a+. Thus, it brings us extreme difficulties, we can't get these properties like Propositions 1.1-1.4 for the variable order fractional operators (integral and derivative). Without these properties for variable order fractional derivative and integral, we can hardly consider the existence of solutions of differential equations for variable order derivative by means of nonlinear functional analysis (for instance, some fixed point theorems).

    Let's take Proposition 1.1 for example. To begin with the simplest case,

    Example 1.5. Let p(t)=t, q(t)=1, f(t)=1,0t3. Now, we calculate Ip(t)0+Iq(t)0+f(t)|t=1 and Ip(t)+q(t)0+f(t)|t=1 defined in (1.3).

    Ip(t)0+Iq(t)0+f(t)|t=1=10(1s)s1Γ(s)s0(sτ)11Γ(1)dτds=10(1s)s1sΓ(s)ds0.472.

    and

    Ip(t)+q(t)0+f(t)|t=1=10(1s)sΓ(s+1)ds=10(1s)ssΓ(s)ds0.686.

    Therefore,

    Ip(t)0+Iq(t)0+f(t)|t=1Ip(t)+q(t)0+f(t)|t=1.

    As a result, the Propositions 1.2 and Propositions 1.4 do not hold for Dp(t)0+ and Ip(t)0+, such as, for function fL(0,T),0<p(t)<1,0tT, we get

    Dp(t)0+Ip(t)0+f(t)=D1(I1p(t)0+Ip(t)0+f(t))D1I1p(t)+p(t)0+f(t)=f(t),t(0,T],

    since we know that I1p(t)0+Ip(t)0+f(t)I1p(t)+p(t)0+f(t) for general function f.

    Now, we can conclude that Propositions 1.1–1.4 do not hold for Dq(t)0+ and Iq(t)0+.

    So, one can not transform a differential equation of variable order into an equivalent interval equation without the Propositions 1.1–1.4. It is a difficulty for us in dealing with the boundary value problems of differential equations of variable order. Since the equations described by the variable order derivatives are highly complex, difficult to handle analytically, it is necessary and significant to investigate their solutions.

    In [16], by means of Banach Contraction Principle, Zhang considered the uniqueness result of solutions to initial value problem of differential equation of variable order

    {Dp(t)0+x(t)=f(t,x),0<tT,x(0)=0, (1.4)

    where 0<T<+, Dp(t)0+ denotes derivative of variable order p(t) ([1,2,3,4]) defined by

    Dp(t)0+x(t)=ddtt0(ts)p(t)Γ(1p(t))x(s)ds,t>0. (1.5)

    and 1Γ(1p(t))t0(ts)p(t)x(s)ds is integral of variable order 1p(t) for function x(t). And p:[0,T](0,1] is a piecewise constant function with partition P={[0,T1],(T1,T2],(T2,T3],, (TN1,T]} (N is a given natural number) of the finite interval [0,T], i.e.

    p(t)=Nk=1qkIk(t),t[0,T],

    where 0<qk1,k=1,2,,N are constants, and Ik is the indicator of the interval [Tk1,Tk],k=1,2,,N(here T0=0,TN=T), that is Ik=1 for t[Tk1,Tk], Ik=0 for elsewhere.

    In [17], the authors studied the Cauchy problem for variable order differential equations with a piecewise constant order function[19]. Inspired by these works, we will study the boundary value problem (1.1) for variable order differential equation with a piecewise constant order function q(t) in this paper.

    Lyapunov's inequality is an outstanding result in mathematics with many different applications, see [20,21,22,23,24,25] and references therein. The result, as proved by Lyapunov[20] in 1907, asserts that if h:[a,b]R is a continuous function, then a necessary condition for the boundary value problem

    {y(t)+h(t)y(t)=0,a<t<b,y(a)=y(b)=0, (1.6)

    to have a nontrivial solution is given by

    ba|h(s)|ds>4ba, (1.7)

    where <a<b<+.

    Lyapunov's inequality has taken many forms, including versions in the context of fractional (noninteger order) calculus, where the second-order derivative in (1.6) is substituted by a fractional operator of order α,

    {Dαa+y(t)+h(t)y(t)=0,a<t<b,y(a)=y(b)=0, (1.8)

    where Dαa+ is the Riemann-Liouville derivative of order α(1,2] and h:[a,b]R is a continuous function. If (1.8) has a nontrivial solution, then

    ba|h(s)|ds>Γ(α)(4ba)α1.

    A Lyapunov fractional inequality can also be obtained by considering the fractional derivative in in the sense of Caputo instead of Riemann-Liouville [22]. More recently, there are some results of Lyapunov type inequalities for fractional boundary value problems. see [23,24]. In [25], authors obtained a generalization of inequality to boundary value problem as following

    {Dαa+y(t)+h(t)f(y)=0,a<t<b,y(a)=y(b)=0, (1.9)

    where Dαa+ is the Riemann-Liouville derivative, 1<α2, and h:[a,b]R is a Lebesgue integrable function. Under some assumptions on the nonlinear term f, authors got a generalization of inequality to the boundary value problem (1.9).

    ba|h(s)|ds>4α1Γ(α)η(ba)α1f(η), (1.10)

    where η is maximum value of nontrivial solution to the boundary value problem (1.9).

    Motivated by [21,22,23,24,25] and the above results, we focus on a generalized Lyapunov-type inequality to the boundary value problem (1.1) under certain assumptions of nonlinear term.

    The paper is organized as following. In Section 2, we provide some necessary definitions associated with the boundary value problem (1.1). In Section 3, we establish the existence of solutions for the boundary value problem (1.1) by using the Schauder fixed point theorem. In Section 4, we investigative the generalized Lyapunov-type inequalities to the boundary value problem (1.1). In section 5, we give some examples are presented to illustrate the main results.

    For the convenience of the reader, we present here some necessary definitions that will be used to prove our main results.

    Definition 2.1. A generalized interval is a subset I of R which is either an interval (i.e. a set of the form [a,b],(a,b),[a,b) or (a,b]); a point {a}; or the empty set .

    Definition 2.2. If I is a generalized interval. A partition of I is a finite set P of generalized intervals contained in I, such that every x in I lies in exactly one of the generalized intervals J in P.

    Example 2.3. The set P={{1},(1,6),[6,7),{7},(7,8]} of generalized intervals is a partition of [1,8].

    Definition 2.4. Let I be a generalized interval, let f:IR be a function, and let P a partition of I. f is said to be piecewise constant with respect to P if for every JP, f is constant on J.

    Example 2.5. The function f:[1,6]R defined by

    f(x)={3,  1x<3,4,  x=3,5,  3<x<6,2,  x=6,

    is piecewise constant with respect to the partition {[1,3],{3},(3,6),{6}} of [1,6].

    The following example illustrates that the semigroup property of the variable order fractional integral doesn't holds for the piecewise constant functions p(t) and q(t) defined in the same partition of finite interval [a,b].

    Example 2.6. Let p(t)={3,  0t1,2,  1<t4, q(t)={2,  0t1,3,  1<t4, and f(t)=1,0t4. We'll verify Ip(t)0+Iq(t)0+f(t)|t=2Ip(t)+q(t)0+f(t)|t=2, here, the variable order fractional integral is defined in (1.3). For 1t4, we have

    Ip(t)0+Iq(t)0+f(t)=10(ts)p(s)1Γ(p(s))s0(sτ)q(τ)1Γ(q(τ))dτds+t1(ts)p(s)1Γ(p(s))s0(sτ)q(τ)1Γ(q(τ))dτds=10(ts)2Γ(3)s0(sτ)Γ(2)dτds+t1(ts)Γ(2)[10(sτ)Γ(2)dτ+s1(sτ)2Γ(3)dτ]ds=10(ts)2s22Γ(3)ds+t1(ts)[s22(s1)22+(s1)36]ds=10(ts)2s22Γ(3)ds+16t1(ts)(s33s2+9s4)ds,

    thus, we have

    Ip(t)0+Iq(t)0+f(t)|t=2=10(2s)2s22Γ(3)ds+1621(2s)(s33s2+9s4)ds=215+1760=512
    Ip(t)+q(t)0+f(t)|t=2=20(2s)p(s)+q(s)1Γ(p(s)+q(s))ds=10(2s)3+21Γ(3+2)ds+21(2s)2+31Γ(2+3)ds=31120+1120=415.

    Therefore, we obtain

    Ip(t)0+Iq(t)0+f(t)|t=2Ip(t)+q(t)0+f(t)|t=2,

    which implies that the semigroup property of the variable order fractional integral doesn't hold for the piecewise constant functions p(t) and q(t) defined in the same partition [0,1],(1,4] of finite interval [0,4].

    We need the following assumptions:

    (H1) Let nN be an integer, P={[0,T1],(T1,T2],(T2,T3],,(Tn1,T]} be a partition of the interval [0,T], and let q(t):[0,T](1,2] be a piecewise constant function with respect to P, i.e.,

    q(t)=Nk=1qkIk(t)={q1,  0tT1,q2,  T1<tT2,,  ,qn,  Tn1<tTn=T, (3.1)

    where 1<qk2(k=1,2,,n) are constants, and Ik is the indicator of the interval [Tk1,Tk], k=1,2,,n (here T0=0,Tn=T), that is, Ik(t)=1 for t[Tk1,Tk] and Ik(t)=0 for elsewhere.

    (H2) Let trf:[0,T]×RR be a continuous function (0r<1), there exist constants c1>0,c2>0, 0<γ<1 such that

    tr|f(t,x(t))|c1+c2|x(t)|γ,0tT,x(t)R.

    In order to obtain our main results, we firstly carry on essential analysis to the boundary value problem (1.1).

    By (1.2), the equation of the boundary value problem (1.1) can be written as

    d2dt2t0(ts)1q(s)Γ(2q(s))x(s)ds+f(t,x)=0,  0<t<T, (3.2)

    According to (H1), Eq (3.2) in the interval (0,T1] can be written as

    Dq10+x(t)+f(t,x)=0,  0<tT1. (3.3)

    Equation (3.2) in the interval (T1,T2] can be written by

    d2dt2(T10(ts)1q1Γ(2q1)x(s)ds+tT1(ts)1q2Γ(2q2)x(s)ds)+f(t,x)=0, (3.4)

    and Eq (3.2) in the interval (T2,T3] can be written by

    d2dt2(T10(ts)1q1Γ(2q1)x(s)ds+T2T1(ts)1q2Γ(2q2)x(s)ds+tT2(ts)1q3Γ(2q3)x(s)ds)+f(t,x)=0. (3.5)

    In the same way, Eq (3.2) in the interval (Ti1,Ti],i=4,5,,n1 can be written by

    d2dt2(T10(ts)1q1Γ(2q1)x(s)ds++tTi1(ts)1qiΓ(2qi)x(s)ds)+f(t,x)=0. (3.6)

    As for the last interval (Tn1,T), similar to above argument, Eq (3.2) can be written by

    d2dt2(T10(ts)1q1Γ(2q1)x(s)ds++tTn1(ts)1qnΓ(2qn)x(s)ds)+f(t,x)=0. (3.7)

    Remark 3.1. From the arguments above, we find that, according to condition (H1), in the different interval, the equation of the boundary value problem (1.1) must be represented by different expression. For instance, in the interval (0,T1], the equation of the boundary value problem (1.1) is represented by (3.3); in the interval (T1,T2], the equation of the boundary value problem (1.1) is represented by (3.4); in the interval (T2,T3], the equation of the boundary value problem (1.1) is represented by (3.5), etc. But, as far as we know, in the different intervals, the equation of integer order or constant fractional order problems may be represented by the same expression. Based these facts, different than integer order or constant fractional order problems, in order to consider the existence results of solution to the boundary value problem (1.1), we need consider the relevant problem defined in the different interval, respectively.

    Now, based on arguments previous, we present definition of solution to the boundary value problem (1.1), which is fundamental in our work.

    Definition 3.2. We say the boundary value problem (1.1) has a solution, if there exist functions xi(t),i=1,2,,n such that x1C[0,T1] satisfying equation (3.3) and x1(0)=0=x1(T1); x2C[0,T2] satisfying equation (3.4) and x2(0)=0=x2(T2); x3C[0,T3] satisfying equation (3.5) and x3(0)=0=x3(T3); xiC[0,Ti] satisfying equation (3.6) and xi(0)=0=xi(Ti)(i=4,5,,n1); xnC[0,T] satisfying equation (3.7) and xn(0)=xn(T)=0.

    Theorem 3.3. Assume that conditions (H1) and (H2) hold, then the boundary value problem (1.1) has one solution.

    Proof. According the above analysis, the equation of the boundary value problem (1.1) can be written as Eq (3.2). Equation (3.2) in the interval (0,T1] can be written as

    Dq10+x(t)+f(t,x)=0,  0<tT1.

    Now, we consider the following two-point boundary value problem

    {Dq10+x(t)+f(t,x)=0,  0<t<T1,x(0)=0,x(T1)=0. (3.8)

    Let xC[0,T1] be solution of the boundary value problem (3.8). Now, applying the operator Iq10+ to both sides of the above equation. By Propositions 1.4, we have

    x(t)=d1tq11+d2tq121Γ(q1)t0(ts)q11f(s,x(s))ds,0<tT1.

    By x(0)=0 and the assumption of function f, we could get d2=0. Let x(t) satisfying x(T1)=0, thus we can get d1=Iq10+f(T1,x)T1q11. Then, we have

    x(t)=Iq10+f(T1,x)T1q11tq11Iq10+f(t,x),0tT1 (3.9)

    Conversely, let xC[0,T1] be solution of integral Eq (3.9), then, by the continuity of function trf and Proposition 1.2, we can easily get that x is the solution of boundary value problem (3.8).

    Define operator T:C[0,T1]C[0,T1] by

    Tx(t)=Iq10+f(T1,x)T1q11tq11Iq10+f(t,x(t)),0tT1.

    It follows from the properties of fractional integrals and assumptions on function f that the operator T:C[0,T1]C[0,T1] defined above is well defined. By the standard arguments, we could verify that T:C[0,T1]C[0,T1] is a completely continuous operator.

    In the next analysis, we take

    M(r,q)=max{2(1r)Γ(q1),2(1r)Γ(q2),,2(1r)Γ(qn)}.

    Let Ω={xC[0,T1]:xR} be a bounded closed convex subset of C[0,T1], where

    R=max{2c1M(r,q)(T+1)2,(2c2M(r,q)(T+1)2)11γ}.

    For xΩ and by (H2), we have

    |Tx(t)|T1q11tq11Γ(q1)T10(T1s)q11|f(s,x(s))|ds+1Γ(q1)t0(ts)q11|f(s,x(s))|ds2Γ(q1)T10(T1s)q11|f(s,x(s))|ds2Γ(q1)T10(T1s)q11sr(c1+c2|x(s)|γ)ds2Tq111Γ(q1)T10sr(c1+c2Rγ)ds2Tq111T1r1(1r)Γ(q1)(c1+c2Rγ)M(r,q)Tq1r1(c1+c2Rγ)M(r,q)(T+1)2(c1+c2RRγ1)R2+R2=R,

    which means that T(Ω)Ω. Then the Schauder fixed point theorem assures that the operator T has one fixed point x1Ω, which is a solution of the boundary value problem (3.8).

    Also, we have obtained that Eq (3.2) in the interval (T1,T2] can be written by (3.4). In order to consider the existence result of solution to (3.4), we rewrite (3.4) as following

    d2dt2T10(ts)1q2Γ(2q2)x(s)ds+d2dt2tT1(ts)1q2Γ(2q2)x(s)ds=f(t,x).  T1<tT2,

    For 0sT1, we take x(s)0, then, by the above equation, we get

    Dq2T1x(t)+f(t,x)=0,T1<t<T2.

    Now, we consider the following boundary value problem

    {Dq2T1+x(t)+f(t,x)=0,  T1<t<T2,x(T1)=0,x(T2)=0, (3.10)

    Let xC[T1,T2] be solution of the boundary value problem (3.10). Now, applying operator Iq2T1+ on both sides of equation to boundary value problem (3.10) and by Propositions 1.4, we have

    x(t)=d1(tT1)q21+d2(tT1)q221Γ(q2)tT1(ts)q21f(s,x(s))ds,T1<tT2.

    By x(T1)=0,x(T2)=0, we have d2=0 and d1=Iq2T1+f(T2,x)(T2T1)1q2. Then, we have

    x(t)=Iq20+f(T2,x)(T2T1)1q2(tT1)q211Γ(q2)tT1(ts)q21f(s,x(s))ds,T1tT2.

    Conversely, let xC[T1,T2] be solution of integral equation above, then, by the continuity assumption of function trf and Proposition (1.2), we can get that x is solution solution of the boundary value problem (3.10).

    Define operator T:C[T1,T2]C[T1,T2] by

    Tx(t)=Iq20+f(T2,x)(T2T1)1q2(tT1)q211Γ(q2)tT1(ts)q21f(s,x(s))ds.

    It follows from the continuity of function trf that operator T:C[T1,T2]C[T1,T2] is well defined. By the standard arguments, we know that T:C[T1,T2]C[T1,T2] is a completely continuous operator.

    For xΩ and by (H2), we get

    |Tx(t)|(T2T1)1q2(tT1)q21Γ(q2)T2T1(T2s)q21|f(s,x(s))|ds+1Γ(q2)tT1(ts)q21|f(s,x(s))|ds2Γ(q2)T2T1(T2s)q21|f(s,x(s))|ds2Γ(q2)T2T1(T2s)q21sr(c1+c2|x(s)|γ)ds2Tq212Γ(q2)T2T1sr(c1+c2Rγ)ds=2Tq212(T1r2T1r1)(1r)Γ(q2)(c1+c2Rγ)2Tq2r2(1r)Γ(q2)(c1+c2Rγ)M(r,q)(T+1)2(c1+c2RRγ1)R2+R2=R,

    which means that T(Ω)Ω. Then the Schauder fixed point theorem assures that operator T has one fixed point ˜x2Ω, which is one solution of the following integral equation, that is,

    ˜x2(t)=Iq20+f(T2,˜x2)(T2T1)1q2(tT1)q211Γ(q2)tT1(ts)q21f(s,˜x2(s))ds,T1tT2. (3.11)

    Applying operator Dq2T1+ on both sides of (3.11), by Proposition 1.2, we can obtain that

    Dq2T1+˜x2(t)+f(t,˜x2)=0,T1<tT2,

    that is, ˜x2(t) satisfies the following equation

    d2dt21Γ(2q2)tT1(ts)1q2˜x2(s)ds+f(t,˜x2)=0,T1<tT2. (3.12)

    We let

    x2(t)={0,  0tT1,˜x2(t),T1<tT2 (3.13)

    hence, from (3.12), we know that x2C[0,T2] defined by (3.13) satisfies equation

    d2dt2(T10(ts)1q1Γ(2q1)x2(s)ds+tT1(ts)1q2Γ(2q2)x2(s)ds)+f(t,x2)=0,

    which means that x2C[0,T2] is one solution of (3.4) with x2(0)=0,x2(T2)=˜x2(T2)=0.

    Again, we have known that Eq (3.2) in the interval (T2,T3] can be written by (3.5). In order to consider the existence result of solution to Eq (3.5), for 0sT2, we take x(s)0, then, by (3.5), we get

    Dq3T2x(t)+f(t,x)=0,T2<t<T3.

    Now, we consider the following boundary value problem

    {Dq3T2+x(t)+f(t,x)=0,  T2<t<T3,x(T2)=0,x(T3)=0. (3.14)

    By the standard way, we know that the boundary value problem (3.14) exists one solution ˜x3Ω. Since ˜x3 satisfies equation

    Dq3T2+˜x3(t)+f(t,˜x3)=0,T2<tT3,

    that is, ˜x3(t) satisfies the following equation

    d2dt21Γ(2q3)tT2(ts)1q3˜x3(s)ds+f(t,˜x3)=0,T2<tT3. (3.15)

    We let

    x3(t)={0,  0tT2,˜x3(t),T2<tT3, (3.16)

    hence, from (3.15), we know that x3C[0,T3] defined by (3.16) satisfies equation

    d2dt2(T10(ts)1q1Γ(2q1)x3(s)ds+T2T1(ts)1q2Γ(2q2)x3(s)ds+tT2(ts)1q3Γ(2q3)x3(s)ds)+f(t,x3)=0,

    which means that x3C[0,T3] is one solution of (3.5) with x3(0)=0,x(T3)=˜x3(T3)=0.

    By the similar way, in order to consider the existence of solution to Eq (3.6) defined on [Ti1,Ti] of (3.2), we can investigate the following two-point boundary value problem

    {DqiTi1+x(t)+f(t,x)=0,  Ti1<t<Ti,x(Ti1)=0,x(Ti)=0. (3.17)

    By the same arguments previous, we obtain that the Eq (3.6) defined on [Ti1,Ti] of (3.2) has solution

    xi(t)={0,  0tTi1,˜xi(t),Ti1<tTi, (3.18)

    where ˜xiΩ with ˜xi(Ti1)=0=˜xi(Ti), i=4,5,,n1.

    Similar to the above argument, in order to consider the existence result of solution to Eq (3.7), we may consider the following boundary value problem

    {DqnTn1+x(t)+f(t,x)=0,  Tn1<t<Tn=T,x(Tn1)=0,x(T)=0. (3.19)

    So by the same considering, for Tn1tT we get

    x(t)=(TTn1)1qn(tTn1)qn1IqnTn1+f(T,x)IqnTn1+f(t,x).

    Define operator T:C[Tn1,T]C[Tn1,T] by

    Tx(t)=(TTn1)1qn(tTn1)qn1IqnTn1+f(T,x)1Γ(qn)tTn1(ts)qn1f(s,x(s))ds,

    Tn1tT. It follows from the continuity assumption of function trf that operator T:C[Tn1,T]C[Tn1,T] is well defined. By the standard arguments, we note that T:C[Tn1,T]C[Tn1,T] is a completely continuous operator.

    For xΩ and by (H2), we get

    |Tx(t)|(TTn1)1qn(tTn1)qn1Γ(qn)TTn1(Ts)qn1|f(s,x(s))|ds+1Γ(qn)tTn1(ts)qn1|f(s,x(s))|ds2Γ(qn)TTn1(Ts)qn1|f(s,x(s))|ds2Γ(qn)TTn1(Ts)qn1sr(c1+c2|x(s)|γ)ds2Tqn1Γ(qn)TTn1sr(c1+c2Rγ)ds2Tqn1(T1rT1rn1)(1r)Γ(qn)(c1+c2Rγ)2(T+1)2(1r)Γ(qn)(c1+c2Rγ)M(r,q)(T+1)2(c1+c2RRγ1)R2+R2=R,

    which means that T(Ω)Ω. Then the Schauder fixed point theorem assures that operator T has one fixed point ˜xnΩ, which is one solution of the following integral equation, that is,

    ˜xn(t)=(TTn1)1qn(tTn1)qn1IqnTn1+f(T,˜xn)1Γ(qn)tTn1(ts)qn1f(s,˜xn(s))ds,Tn1tT. (3.20)

    Applying operator DqnTn1+ on both sides of (3.20), by Proposition 1.2, we can obtain that

    DqnTn1+˜xn(t)+f(t,˜xn)=0,Tn1<tT,

    that is, ˜xT(t) satisfies the following equation

    d2dt21Γ(2qn)tTn1(ts)1qn˜xn(s)ds+f(t,˜xn)=0,Tn1<tT. (3.21)

    We let

    xn(t)={0,  0tTn1,˜xn(t),Tn1<tT, (3.22)

    hence, from (3.21), we know that xnC[0,T] defined by (3.22) satisfies equation

    d2dt2(T10(ts)1q1Γ(2q1)xn(s)ds++tTn1(ts)1qnΓ(2qn)xn(s)ds)+f(t,xn)=0.

    for Tn1<t<T, which means that xnC[0,T] is one solution of (3.7) with xn(0)=0,xn(T)=˜xn(T)=0.

    As a result, we know that the boundary value problem (1.1) has a solution. Thus we complete the proof.

    Remark 3.4. For condition (H2), if γ1, then using similar way, we can obtain the existence result of solution to the boundary value problem (1.1) provided that we impose some additional conditions on c1,c2.

    In this section, we investigate the generalized Lyapunov-type inequalities for the boundary value problem (1.1).

    Now, we explore characters of Green functions to the boundary value problems (3.8), (3.10), (3.14), , (3.17) and (3.19).

    Proposition 4.1. Assume that trf:[0,T]×RR, (0r<1) is continuous function, q(t):[0,T](1,2] satisfies (H1), then the Green functions

    Gi(t,s)={1Γ(qi)[(TiTi1)1qi(tTi1)qi1(Tis)qi1(ts)qi1],Ti1stTi,1Γ(qi)(TiTi1)1qi(tTi1)qi1(Tis)qi1,Ti1tsTi, (4.1)

    of the boundary value problems (3.8), (3.10), (3.14), , (3.17) and (3.19) satisfy the following properties:

    (1) Gi(t,s)0 for all Ti1t,sTi;

    (2) maxt[Ti1,Ti]Gi(t,s)=Gi(s,s), s[Ti1,Ti];

    (3) Gi(s,s) has one unique maximum given by

    maxs[Ti1,Ti]Gi(s,s)=1Γ(qi)(TiTi14)qi1,

    where i=1,2,,n, T0=0,Tn=T.

    Proof. From the proof of Theorem 3.1, we know that Green functions of the boundary value problems (3.8), (3.10), (3.14), , (3.17) and (3.19) are given by (4.1).

    Using a similar way, we can verify these three results. In fact, let

    g(t,s)=(TiTi1)1qi(tTi1)qi1(Tis)qi1(ts)qi1,Ti1stTi.

    We see that

    gt(t,s)=(qi1)[(TiTi1)1qi(tTi1)qi2(Tis)qi1(ts)qi2](qi1)[(TiTi1)1qi(ts)qi2(TiTi1)qi1(ts)qi2]=0,

    which means that g(t,s) is nonincreasing with respect to t, so g(t,s)g(Ti,s)=0 for Ti1stTi. Thus, together this with the expression of Gi(t,s), we get that Gi(t,s)0 for all Ti1t,sTi, i=1,2,,n, T0=0,Tn=T.

    Since g(t,s) is nonincreasing with respect to t, it holds that g(t,s)g(s,s) for Ti1stTi. On the other hand, for Ti1tsTi, we have

    (TiTi1)1qi(tTi1)qi1(Tis)qi1(TiTi1)1qi(sTi1)qi1(Tis)qi1.

    These assure that maxt[Ti1,Ti]Gi(t,s)=Gi(s,s), s[Ti1,Ti], i=1,2,,n, T0=0,Tn=T.

    Next, we verify (3) of Proposition 4.1. Obviously, the maximum points of Gi(s,s) are not Ti1 and Ti, i=1,2,,n, T0=0,Tn=T. For s(Ti1,Ti), i=1,2,,n, T0=0,Tn=T, we have that

    dGi(s,s)ds=1Γ(qi)(TiTi1)1qi(qi1)[(sTi1)qi2(Tis)qi1(sTi1)qi1(Tis)qi2]=1Γ(qi)(TiTi1)1qi(qi1)(sTi1)qi2(Tis)qi2[Ti+Ti12s],

    which implies that the maximum points of Gi(s,s) is s=Ti1+Ti2, i=1,2,,n, T0=0,Tn=T. Hence, for i=1,2,,n,T0=0,Tn=T,

    maxs[Ti1,Ti]Gi(s,s)=G(Ti1+Ti2,Ti1+Ti2)=1Γ(qi)(TiTi14)qi1.

    Thus, we complete this proof.

    Theorem 4.2. Let (H1) holds and trf:[0,T]×RR, (0r<1) be a continuous function. Assume that there exists nonnegative continuous function h(t) defined on [0,T] such that

    tr|f(t,x)|h(t)|x(t)|,0tT,x(t)R

    If the boundary value problem (1.1) has a nontrivial solution x, then

    T0srh(s)ds>ni=1Γ(qi)(4TiTi1)qi1. (4.2)

    Proof. Let x be a nontrivial solution of the boundary value problem (1.1). Using Definition 3.2 and the proof of Theorem 3.3, we know that

    x(t)={x1(t),0tT1x2(t)={0,0tT1,˜x1(t),T1<tT2,x3(t)={0,0tT2,˜x2(t),T2<tT3,xi(t)={0,0tTi1,˜xi(t),Ti1<tTi,xn(t)={0,0tTn1,˜xn(t),Tn1<tT, (4.3)

    where x1C[0,T1] is nontrivial solution of the boundary value problem (3.8) with a1=0, ˜x2C[T1,T2] is nontrivial solution of the boundary value problem (3.10) with a2=0, ˜x3C[T2,T3] is nontrivial solution of the boundary value problem (3.14) with a3=0, ˜xiC[Ti1,Ti] is nontrivial solution of the boundary value problem (3.17) with ai=0, ˜xnC[Tn1,T] is nontrivial solution of the boundary value problem (3.19). From (4.3) and Proposition 4.1, we have

    x1C[0,T1]=max0tT1|x1(t)|max0tT1T10G1(t,s)|f(s,x1(s))|dsT10G1(s,s)srh(s)|x1(s)|ds<x1C[0,T1]Γ(q1)(T14)q11T10srh(s)ds, 

    which implies that

    T10srh(s)ds>Γ(q1)(4T1)q11. (4.4)
    x2C[0,T2]=maxT1tT2|˜x2(t)|=maxT1tT2|T2T1G2(t,s)f(s,˜x2(s))ds|T2T1G2(s,s)srh(s)|˜x2(s)|ds<˜x2C[T1,T2]Γ(q2)(T2T14)q21T2T1srh(s)ds,=x2C[0,T2]Γ(q2)(T2T14)q21T2T1srh(s)ds,

    which implies that

    T2T1srh(s)ds>Γ(q2)(4T2T1)q21. (4.5)

    Similar, for i=3,4,,n (Tn=T), we have

    xiC[0,Ti]=maxTi1tTi|˜xi(t)|=maxTi1tTi|TiTi1Gi(t,s)f(s,˜xi(s))ds|TiTi1Gi(s,s)srh(s)|˜xi(s)|ds<˜xiC[Ti1,Ti]Γ(qi)(TiTi14)qi1TiTi1srh(s)ds,=xiC[0,Ti]Γ(qi)(TiTi14)qi1TiTi1srh(s)ds,

    which implies that

    TiTi1srh(s)ds>Γ(qi)(4TiTi1)qi1.

    So, we get

    T0srh(s)ds>ni=1Γ(qi)(4TiTi1)qi1.

    We complete the proof.

    Remark 4.3. We notice that if r=0 and q(t)=q, q is a constant, i.e., BVP (1.1) is a fractional differential equation with constant order, then by similar arguments as done in [22], we get

    T0h(s)ds>Γ(q)(4T)q1.

    So, the inequalities (4.2) is a generalized Lyapunov-type inequality for the boundary value problem (1.1).

    Example 5.1. Let us consider the following nonlinear boundary value problem

    {Dq(t)0+x(t)+t0.5|x|121+x2=0,0<t<2,u(0)=u(2)=0, (5.1)

    where

    q(t)={1.2,  0t1,1.6,  1<t2.

    We see that q(t) satisfies condition (H1); t0.5f(t,x(t))=|x(t)|121+x(t)2:[0,2]×RR is continuous. Moreover, we have

    t0.5|f(t,x(t))|=|x(t)|121+x(t)2|x(t)|12.

    Let r=0.5, c1=c2=1 and γ=12. We could verify that f(t,x)=t0.5|x|121+x2 satisfies condition (H2). This suggests that the boundary value problem (5.1) has a solution by the conclusion of Theorem 3.3.

    Example 5.2. Let us consider the following linear boundary value problem

    {Dq(t)0+x(t)+t0.4=0,0<t<3,u(0)=0,u(3)=0, (5.2)

    where

    q(t)={1.2,  0t1,1.5,  1<t2,1.8,  2<t3.

    We see that q(t) satisfies condition (H1); f(t,x(t))=t0.4:[0,3]×RR is continuous. Moreover, |f(t,x(t))|=t0.430.4, thus we could take suitable constants to verify f(t,x)=t0.4 satisfies condition (H2). Then Theorem 3.3 assures the boundary value problem (5.2) has a solution.

    In fact, we know that equation of (5.1) can been divided into three expressions as following

    D1.20+x(t)+t0.4=0,  0<t1. (5.3)

    For 1<t2,

    d2dt2(10(ts)0.2Γ(0.8)x(s)ds+t1(ts)0.5Γ(0.5)x(s)ds)+t0.4=0. (5.4)

    For 2<t3,

    d2dt2(10(ts)0.2Γ(0.8)x(s)ds+21(ts)0.5Γ(0.5)x(s)ds+t2(ts)0.8Γ(0.2)x(s)ds)+t0.4=0. (5.5)

    By [18], we can easily obtain that the following boundary value problems

    {D1.20+x(t)+t0.4=0,  0<t1,x(0)=0,x(1)=0
    {D1.51+x(t)=d2dt2t1(ts)0.5Γ(0.5)x(s)ds+t0.4=0,  1<t<2,x(1)=0,x(2)=0
    {D1.82+x(t)=d2dt2t2(ts)0.8Γ(0.2)x(s)ds+t0.4=0,  2<t<3,x(2)=0,x(3)=0

    respectively have solutions

    x1(t)=Γ(1.4)Γ(2.6)(t0.2t1.6)C[0,1];˜x2(t)=Γ(1.4)Γ(2.9)((t1)0.5(t1)1.9)C[1,2];˜x3(t)=Γ(1.4)Γ(3.2)((t2)0.8(t2)2.2)C[2,3].

    It is known by calculation that

    x1(t),0t1,x2(t)={0,0t1,˜x2(t),1<t2,x3(t)={0,0t2,˜x3(t),2<t3, (5.6)

    are the solutions of (5.3)–(5.5), respectively. By Definition 3.2 and (5.6), we know that

    x(t)={x1(t)=Γ(1.4)Γ(2.6)(t0.2t1.6),0t1,x2(t)={0,0t1,Γ(1.4)Γ(2.9)((t1)0.5(t1)1.9),1<t2,x3(t)={0,0t2,Γ(1.4)Γ(3.2)((t2)0.8(t2)2.2),2<t3

    is one solution of the boundary value problem (5.2).

    In this paper, we consider a two-points boundary value problem of differential equations of variable order, which is a piecewise constant function. Based the essential difference about the variable order fractional calculus (derivative and integral) and the integer order and the constant fractional order calculus (derivative and integral), we carry on essential analysis to the boundary value problem (1.1). According to our analysis, we give the definition of solution to the boundary value problem (1.1). The existence result of solution to the boundary value problem (1.1) is derived. We present a Lyapunov-type inequality for the boundary value problems (1.1). Since the variable order fractional calculus (derivative and integral) and the integer order and the constant fractional order calculus (derivative and integral) has the essential difference, it is interesting and challenging about the existence, uniqueness of solutions, Lyapunov-type inequality, etc, to the boundary value problems of differential equations of variable order.

    This research is supported by the Natural Science Foundation of China (11671181). The authors are thankful to the referees for their careful reading of the manuscript and insightful comments.

    The author declares no conflicts of interest in this paper.



    [1] A. Ahmadi-Javid, Entropic value-at-risk: A new coherent risk measure, J. Optim. Theory Appl., 155 (2012), 1105–1123. https://dx.doi.org/10.1007/s10957-011-9968-2 doi: 10.1007/s10957-011-9968-2
    [2] Y. Amihud, H. Mendelson, Liquidity and stock returns, Financ. Anal. J., 42 (1986), 43–48. http://dx.doi.org/10.2469/faj.v42.n3.43 doi: 10.2469/faj.v42.n3.43
    [3] P. Artzner, F. Delbaen, J.-M. Eber, D. Heath, Thinking coherently, Risk, 10 (1997), 68–71.
    [4] P. Artzner, F. Delbaen, J.-M. Eber, D. Heath, Coherent measures of risk, Math. Financ., 9 (1999), 203–228. http://dx.doi.org/10.1111/1467-9965.00068
    [5] A. Axelrod, L. Carlone, G. Chowdhary, S. Karaman, Data-driven prediction of EVAR with confidence in time-varying datasets, 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA, 2016, 5833–5838. http://dx.doi.org/10.1109/CDC.2016.7799166
    [6] D. P. Baron, On the utility theoretic foundations of mean-variance analysis, J. Financ., 32 (1977), 1683–1697. http://dx.doi.org/10.1111/j.1540-6261.1977.tb03363.x doi: 10.1111/j.1540-6261.1977.tb03363.x
    [7] A. Ben-Tal, D. Bertsimas, D. B. Brown, A soft robust model for optimization under ambiguity, Oper. Res., 58 (2010), 1220–1234. http://dx.doi.org/10.1287/opre.1100.0821 doi: 10.1287/opre.1100.0821
    [8] D. Bertsimas, D. B. Brown, Constructing uncertainty sets for robust linear optimization, Oper. Res., 57 (2009), 1483–1495. http://dx.doi.org/10.1287/opre.1080.0646 doi: 10.1287/opre.1080.0646
    [9] M. Borkovec, I. Domowitz, B. Kiernan, V. Serbin, Portfolio optimization and the cost of trading, J. Invest., 19 (2010), 63–76. http://dx.doi.org/10.3905/joi.2010.19.2.063 doi: 10.3905/joi.2010.19.2.063
    [10] J. P. Bouchaud, J. Bonart, J. Donier, M. Gould, Trades, quotes and prices: financial markets under the microscope, Cambridge: Cambridge University Press, 2018. http://dx.doi.org/https://doi.org/10.1017/9781316659335
    [11] S. P. Boyd, L. Vandenberghe, Convex optimization, Cambridge: Cambridge University Press, 2004.
    [12] S. Caçador, J. M. Dias, P. Godinho, Portfolio selection under uncertainty: a new methodology for computing relative-robust solutions, Int. T. Oper. Res., 28 (2021), 1296–1329. http://dx.doi.org/10.1111/itor.12674 doi: 10.1111/itor.12674
    [13] D. Cajas, Entropic portfolio optimization: a disciplined convex programming framework, SSRN Electronic Journal, 2021 (2021), 3792520.
    [14] A. H. Chen, F. J. Fabozzi, D. S. Huang, Portfolio revision under mean-variance and mean-cvar with transaction costs, Rev. Quant. Finan. Acc., 39 (2012), 509–526. http://dx.doi.org/10.1007/s11156-012-0292-1 doi: 10.1007/s11156-012-0292-1
    [15] S. Chennaf, J. B. Amor, Entropic value at risk to find the optimal uncertain random portfolio, Soft Comput., 27 (2023), 15185–15197. http://dx.doi.org/10.1007/s00500-023-08547-5 doi: 10.1007/s00500-023-08547-5
    [16] J. Dufitinema, S. Pynnönen, T. Sottinen, Maximum likelihood estimators from discrete data modeled by mixed fractional brownian motion with application to the nordic stock markets, Commun. Stat. Simul. C., 51 (2022), 5264–5287. http://dx.doi.org/10.1080/03610918.2020.1764581 doi: 10.1080/03610918.2020.1764581
    [17] P. Embrechts, S. I. Resnick, G. Samorodnitsky, Extreme value theory as a risk management tool, N. Am. Actuar. J., 3 (1999), 30–41. http://dx.doi.org/10.1080/10920277.1999.10595797 doi: 10.1080/10920277.1999.10595797
    [18] D. Goldfarb, G. Iyengar, Robust portfolio selection problems, Math. Oper. Res., 28 (2003), 1–38. http://dx.doi.org/10.1287/moor.28.1.1.14260 doi: 10.1287/moor.28.1.1.14260
    [19] F. Hooshmand, Z. Anoushirvani, S. A. MirHassani, Model and efficient algorithm for the portfolio selection problem with real-world constraints under value-at-risk measure, Int. T. Oper. Res., 30 (2023), 2665–2690. http://dx.doi.org/10.1111/itor.13239 doi: 10.1111/itor.13239
    [20] R. P. Huang, Z. S. Xu, S. J. Qu, X. G. Yang, M. Goh, Robust portfolio selection with distributional uncertainty and integer constraints, J. Oper. Res. Soc. China, 11 (2023), 1–27. http://dx.doi.org/10.1007/s40305-023-00466-4 doi: 10.1007/s40305-023-00466-4
    [21] G. Kara, A. Özmen, G.-W. Weber, Stability advances in robust portfolio optimization under parallelepiped uncertainty, Cent. Eur. J. Oper. Res., 27 (2019), 241–261. http://dx.doi.org/10.1007/s10100-017-0508-5 doi: 10.1007/s10100-017-0508-5
    [22] J. Kriens, J. T. van Lieshout, Notes on the Markowitz portfolio selection method, Stat. Neerl., 42 (1988), 181–191. http://dx.doi.org/10.1111/j.1467-9574.1988.tb01232.x doi: 10.1111/j.1467-9574.1988.tb01232.x
    [23] W. Liu, L. Yang, B. Yu, Distributionally robust optimization based on Kernel density estimation and mean-entropic value-at-risk, INFORMS Journal on Optimization, 5 (2022), 68–91. http://dx.doi.org/10.1287/ijoo.2022.0076 doi: 10.1287/ijoo.2022.0076
    [24] G. M. Luo, Mixed complementarity problems for robust optimization equilibrium in bimatrix game, Appl. Math., 57 (2012), 503–520. http://dx.doi.org/10.1007/s10492-012-0029-4 doi: 10.1007/s10492-012-0029-4
    [25] H. Markowitz, Portfolio selection, J. Financ., 7 (1952), 77–91. http://dx.doi.org/10.1111/j.1540-6261.1952.tb01525.x
    [26] E. J. Menvouta, S. Serneels, T. Verdonck, Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets, Journal of Finance and Data Science, 9 (2023), 100097. http://dx.doi.org/10.1016/j.jfds.2023.100097 doi: 10.1016/j.jfds.2023.100097
    [27] J. E. Mitchell, S. Braun, Rebalancing an investment portfolio in the presence of convex transaction costs, submitted for publication.
    [28] J. E. Mitchell, S. Braun, Rebalancing an investment portfolio in the presence of convex transaction costs, including market impact costs, Optim. Method. Softw., 28 (2013), 523–542. http://dx.doi.org/10.1080/10556788.2012.717940 doi: 10.1080/10556788.2012.717940
    [29] K. Muthuraman, S. Kumar, Multidimensional portfolio optimization with proportional transaction costs, Math. Financ., 16 (2006), 301–335. http://dx.doi.org/10.1111/j.1467-9965.2006.00273.x doi: 10.1111/j.1467-9965.2006.00273.x
    [30] G. C. Pflug, Some remarks on the value-at-risk and the conditional value-at-risk, In: Probabilistic constrained optimization, Boston: Springer, 2000,272–281. http://dx.doi.org/10.1007/978-1-4757-3150-7_15
    [31] R. T. Rockafellar, S. Uryasev, Optimization of conditional value-at-risk, J. Risk, 3 (2000), 21–41.
    [32] R. Sehgal, A. Mehra, Robust reward–risk ratio portfolio optimization, Int. T. Oper. Res., 28 (2021), 2169–2190. http://dx.doi.org/10.1111/itor.12652 doi: 10.1111/itor.12652
    [33] R. L. Sun, T. F. Ma, S. Z. Liu, Portfolio selection based on semivariance and distance correlation under minimum variance framework, Stat. Neerl., 73 (2019), 373–394. http://dx.doi.org/10.1111/stan.12174 doi: 10.1111/stan.12174
    [34] X. J. Tong, F. L. Wu, L. Q. Qi, Worst-case cvar based portfolio optimization models with applications to scenario planning, Optim. Method. Softw., 24 (2009), 933–958. http://dx.doi.org/10.1080/10556780902865942 doi: 10.1080/10556780902865942
    [35] X. J. Tong, F. L. Wu, Robust reward–risk ratio optimization with application in allocation of generation asset, Optimization, 63 (2014), 1761–1779. http://dx.doi.org/10.1080/02331934.2012.672419 doi: 10.1080/02331934.2012.672419
    [36] X. T. Wang, Z. Li, L. Zhuang, Risk preference, option pricing and portfolio hedging with proportional transaction costs, Chaos Soliton. Fract., 95 (2017), 111–130. https://dx.doi.org/10.1016/j.chaos.2016.12.010 doi: 10.1016/j.chaos.2016.12.010
    [37] L. J. Xu, Y. J. Zhou, New robust reward-risk ratio models with CVaR and standard deviation, J. Math., 2022 (2022), 8304411. http://dx.doi.org/10.1155/2022/8304411 doi: 10.1155/2022/8304411
    [38] C. L. Zheng, Y. Chen, Portfolio selection based on relative entropy coherent risk measure, Systems Engineering-Theory & Practice, 34 (2014), 648–655.
    [39] Y. J. Zhou, L. Yang, L. J. Xu, B. Yu, Inseparable robust reward–risk optimization models with distribution uncertainty, Japan J. Indust. Appl. Math., 33 (2016), 767–780. http://dx.doi.org/10.1007/s13160-016-0230-z doi: 10.1007/s13160-016-0230-z
    [40] S. S. Zhu, D. Li, S. Y. Wang, Robust portfolio selection under downside risk measures, Quant. Financ., 9 (2009), 869–885. http://dx.doi.org/10.1080/14697680902852746 doi: 10.1080/14697680902852746
    [41] S. S. Zhu, M. Fukushima, Worst-case conditional value-at-risk with application to robust portfolio management, Oper. Res., 57 (2009), 1155–1168. http://dx.doi.org/10.1287/opre.1080.0684 doi: 10.1287/opre.1080.0684
    [42] J. X. Zhu, Optimal financing and dividend distribution with transaction costs in the case of restricted dividend rates, ASTIN Bull., 47 (2017), 239–268. http://dx.doi.org/10.1017/asb.2016.29 doi: 10.1017/asb.2016.29
  • This article has been cited by:

    1. Ibtesam Alshammari, Islam M. Taha, On fuzzy soft β-continuity and β-irresoluteness: some new results, 2024, 9, 2473-6988, 11304, 10.3934/math.2024554
    2. D. I. Taher, R. Abu-Gdairi, M. K. El-Bably, M. A. El-Gayar, Decision-making in diagnosing heart failure problems using basic rough sets, 2024, 9, 2473-6988, 21816, 10.3934/math.20241061
    3. Fahad Alsharari, Ahmed O. M. Abubaker, Islam M. Taha, On r-fuzzy soft γ-open sets and fuzzy soft γ-continuous functions with some applications, 2025, 10, 2473-6988, 5285, 10.3934/math.2025244
    4. Fahad Alsharari, Hind Y. Saleh, Islam M. Taha, Some Characterizations of k-Fuzzy γ-Open Sets and Fuzzy γ-Continuity with Further Selected Topics, 2025, 17, 2073-8994, 678, 10.3390/sym17050678
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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

Metrics

Article views(1408) PDF downloads(55) Cited by(0)

Figures and Tables

Figures(3)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog