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On a nabla fractional boundary value problem with general boundary conditions

  • Received: 02 September 2019 Accepted: 23 October 2019 Published: 29 October 2019
  • MSC : 26D15, 34A08, 34B05, 39A10, 39A12

  • In this article, we consider a nabla fractional boundary value problem with general boundary conditions. Brackins & Peterson [5] gave an explicit expression for the corresponding Green's function. Here, we show that this Green's function is nonnegative and obtain an upper bound for its maximum value. Since the expression for the Green's function is complicated, derivation of its properties may not be straightforward. For this purpose, we use a few properties of fractional nabla Taylor monomials. Using the Green's function, we will then develop a Lyapunov-type inequality for the nabla fractional boundary value problem.

    Citation: Jagan Mohan Jonnalagadda. On a nabla fractional boundary value problem with general boundary conditions[J]. AIMS Mathematics, 2020, 5(1): 204-215. doi: 10.3934/math.2020012

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  • In this article, we consider a nabla fractional boundary value problem with general boundary conditions. Brackins & Peterson [5] gave an explicit expression for the corresponding Green's function. Here, we show that this Green's function is nonnegative and obtain an upper bound for its maximum value. Since the expression for the Green's function is complicated, derivation of its properties may not be straightforward. For this purpose, we use a few properties of fractional nabla Taylor monomials. Using the Green's function, we will then develop a Lyapunov-type inequality for the nabla fractional boundary value problem.


    The models involving entry-exit decisions apply to many situations, as stated in [1,2]. Such models may be described simply as follows. A firm decides when to invest in or when to abandon a project that can bring profit. There is plenty of literature on models concerning how to schedule the timing under the assumption that exiting the project does not take time, so we do not list them in detail, but refer to [1,2,3,4,5,6,7,8,9]. The ideas of entry-exit decisions apply to many concrete issues, for example, relationships of globalization and entrepreneurial entry-exit [10], when to invest or expand a start-up firm [11], when to enter or exit stock markets [12], and how to make a schedule for buying carbon emission rights [13].

    In practice, the exiting process may take a long time, so ignoring it is not reasonable. For example, Brexit took more than 3 years, from June 23, 2016 to January 31, 2020 (https://www.britannica.com/topic/Brexit). The models [14,15] take account of the time, but with equal output rates in the regular production and exit periods. It is an apparent feature that the output rate of the project is usually reduced during the exit period. In this paper, we introduce a parameter to describe the output rate during the exit period and completely discuss the effects of output reduction on entry-exit decisions, under the assumption that the commodity price of the project follows a geometric Brownian motion.

    We use the optimal stopping theory to carry out the study and obtain the explicit expressions of the optimal activating time and start time of the exit, which is one of the contributions of the paper. With these explicit expressions in hand, we can carefully analyze the effects of output reduction on entry-exit decisions, which is another contribution of the paper.

    If the optimal choice is never to exit the project, the output reduction has no effect on the optimal entry-exit decision.

    If the optimal exit is in a finite time, the situation becomes complicated. However, we obtain complete criterions, which determine the effects of output reduction on entry-exit decisions (see Section 4).

    We outline the structure of this paper. In Section 2, we describe the model in detail. In Section 3, we determine an optimal entry-exit decision. In Section 4, we discuss the effects of output reduction during exit period on entry-exit decisions. Some conclusions are drawn in Section 5.

    Assume that the price process P of one unit product follows

    dP(t)=μP(t)dt+σP(t)dB(t)andP(0)=p, (2.1)

    where μR, σ,p>0, and B is a one dimensional standard Brownian motion, which denotes uncertainty. In this paper, all times are stopping times w.r.t. the filtration generated by the Brownian motion B.

    Since involving the construction period leads to complicated calculations and distracts us from analyzing the effects of reduction on entry-exit decisions, and the effects of the construction period have been discussed in [3,15], we assume that there is no construction period (it may happen when the firm buys a project). The firm activates the project at time τI with the entry cost KI and completes the abandonment of the project during the time interval [τO,τO+δ], with the exit cost valued at KO at time τO+δ. Without loss of generality, we assume that the firm produces one unit product per unit time during the period [τI,τO] at the marginal cost C and α (0α1) unit products per unit time during the time interval of exiting the project [τO,τO+δ].

    To answer the two questions, what time is optimal to activate the project and what time is optimal to start the abandonment procedure, we solve the optimization problem

    J(p)=supτIτOEp[τOτIexp(rt)(P(t)C)dt+ατO+δτOexp(rt)(P(t)C)dtexp(rτI)KIexp(r(τO+δ))KO]. (2.2)

    We call stopping times τI and τO the activating times and start times of the exit, respectively, and we call the function J the maximal expected present value of the project.

    If rμ, some straight calculations show us that

    Ep[+0exp(rt)(P(t)C)dt]=+.

    Thus, we obtain the following result.

    Theorem 3.1. Assume that rμ, then τI: =0 is an optimal activating time and τO: =+ is an optimal start time of the exit, i.e., the firm should never exit the project. In addition, the function J in (2.2) is given by J+.

    In the rest of this section, we assume r>μ.

    Taking τI=τO: =0 in (2.2), we have

    J(p)KI+α(1exp((μr)δ))rμpexp(rδ)KOαCr(1exp(rδ)),

    thus, in the remains of this section, we always assume that

    rKI+exp(rδ)rKO+α(1exp(rδ))C0

    to avoid arbitrage opportunities.

    Let λ1 and λ2 be the solutions to the equation

    rμλ12σ2λ(λ1)=0

    with λ1<λ2, then we have λ1<0 and λ2>1.

    Theorem 3.2. Assume that r>μ. The following are true:

    (ⅰ) If

    (1α)C+(αCrKO)exp(rδ)0,

    then (τI,τO) is a solution to (2.2), where

    τI=inf{t:t>0,P(t)pI}

    and τO=+. Here,

    pI=λ2λ21(rμ)(Cr+KI).

    In addition,

    J(p)={Bpλ2,ifp<pI,prμCrKI,ifppI,

    where

    B=pI1λ2λ2(rμ).

    (ⅱ) If

    (1α)C+(αCrKO)exp(rδ)>0

    and

    α(1exp((μr)δ))pOαC(1exp(rδ))exp(rδ)rKOrKI0, (3.1)

    where

    pO=λ1λ11rμ1α+αexp((μr)δ)((1α)Cr+exp(rδ)(αCrKO)),

    then (τI,τO) is a solution to (2.2), where

    τI=inf{t:t>0,P(t)pI}

    and

    τO=inf{t:t>τI,P(t)pO}.

    Here, pI is the largest solution of the algebraic equation

    A(λ2λ1)pλ1I+(λ21)rμpIλ2(Cr+KI)=0.

    In addition,

    J(p)={Bpλ2,ifp<pI,Apλ1+prμCrKI,ifppI,

    where

    A=1α+αexp((μr)δ)λ1(μr)pO1λ1

    and

    B=λ1λ12Apλ1λ2I+pI1λ2λ2(rμ).

    Remark 3.3. We propose condition (3.1) to eliminate the possibility that the firm enters the project at a trigger price lower than the optimal trigger price of the exit, i.e., the firm enters the project and then immediately decides to exit the project.

    In light of [14, Theorems 3.1 and 5.2], we have the following Lemma 3.4, which serves as preparation for the proof of Theorem 3.2.

    Lemma 3.4. If (τ1,τ2) is a solution to the optimization problem

    ˜J(p):=supτIτOEp[τOτIexp(rt)(P(t)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)], (3.2)

    it is also a solution to (2.2) and J(x)=˜J(x), where

    l1:=α(1exp((μr)δ))μr

    and

    l0:=αCr(1exp(rδ))+exp(rδ)KO.

    Proof. By the strong Markov property of the process {(s+t,P(t)),t0}, where sR, we have

    Ep[τOτIexp(rt)(P(t)C)dt+ατO+δτOexp(rt)(P(t)C)dtexp(rτI)KIexp(r(τO+δ))KO]=Ep[τOτIexp(rt)(P(t)C)dt+αexp(rτO)EP(τO)[δ0exp(rt)(P(t)C)dt] exp(rτI)KIexp(r(τO+δ))KO].

    Thus, we need to calculate

    αEP(τO)[δ0exp(rt)(P(t)C)dt]exp(rδ)KO=αδ0exp(rt)(exp(μt)P(τO)C)dtexp(rδ)KO=l1P(τO)l0.

    The proof is complete.

    Remark 3.5. The proof of Lemma 3.4 has an economic meaning as follows. We first discount the benefit during the abandonment period to time τO, then discount this value to time zero.

    With the help of Lemma 3.4, we can prove Theorem 3.2.

    Proof of Theorem 3.2. (1) By Lemma 3.4, we solve problem (3.2),

    supτIτOEp[τOτIexp(rt)(P(t)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)]=supτIτOEp[exp(rτI)τOτI0exp(rt)(P(t+τI)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)]=supτIEp[exp(rτI)(G(P(τI))KI)]=:H(p), (3.3)

    where

    G(p):=supτOEp[τO0exp(rt)(P(t)C)dtexp(rτO)(l1P(τO)+l0)]. (3.4)

    (2) Assume

    (1α)C+(αCrKO)exp(rδ)0.

    We first solve problem (3.4) and then problem (3.3).

    For problem (3.4), noting

    Ep[0exp(rt)(P(t)C)dt]=prμCr,

    we see that

    Ep[0exp(rt)(P(t)C)dt]l1pl0,

    which implies

    τO=+

    and

    G(p)=prμCr.

    Set

    h(p):=prμCrKI.

    Since

    {p:p>0andrh(p)μph(p)0}={p:pC+rKI},

    the exercise region of problem (3.3) takes the form [pI,+) for some

    pIC+rKI.

    The function H satisfies

    rHμpH12σ2p2H=0

    on the continuation region (0,pI) and is Lipschitz continuous on (0,+) and C1 continuous at pI. Thus, we get

    H(p)=Bpλ2

    and B and pI solve

    {Bpλ2I=pIrμCrKI,λ2Bpλ21I=1rμ,

    by which we finish the proof of (ⅰ).

    (3) Assume

    (1α)C+(αCrKO)exp(rδ)>0

    and (3.1) hold. We again first solve problem (3.4) and then problem (3.3).

    Set

    g(p):=l1pl0.

    A straight calculation shows that

    {p:p>0andrgμpg(p)p+C0}=(0,(1α)C+(αCrKO)exp(rδ)1α(1exp((μr)δ))),

    which means the exercise region of problem (3.4) takes the form (0,pO] for some

    pO(1α)C+(αCrKO)exp(rδ)1α(1exp((μr)δ)).

    The function G satisfies

    rGμpG12σ2p2Gp+C=0

    on the continuation region (pO,+) and is Lipschitz continuous on (0,+) and C1 continuous at pO. Thus, we get

    G(p)=Apλ1+prμCr,

    and A and pO solve

    {Apλ1O+pOrμCr=l1pOl0,λ1Apλ11O+1rμ=l1,

    which implies coefficient A and the optimal exit trigger pricer of (ⅱ).

    To solve problem (3.4), we define

    h(p):=G(p)KI.

    In light of (3.1),

    {p:p>0andrh(p)μph(p)0}={p:pC+rKI},

    thus, the exercise region of problem (3.3) takes the form [pI,+) for some

    pIC+rKI.

    The function H satisfies

    rHμpH12σ2p2H=0

    on the continuation region (0,pI) and is Lipschitz continuous on (0,+) and C1 continuous at pI. Thus, we get

    H(p)=Bpλ2,

    and B and pI solve

    {Bpλ2I=Apλ1I+pIrμCrKI,λ2Bpλ21I=λ1Apλ11I+1rμ,

    by which we finish the proof of (ⅱ).

    We analyze the effects of output reduction during the exit period on entry-exit decisions.

    If rμ, the firm has an optimal time τI=0 to activate the project and should never exit the project. Thus, the reduction does not affect entry-exit decisions.

    If r>μ and

    (1α)C+(αCrKO)exp(rδ)0,

    the optimal time to activate the project is given by

    τI=inf{t:t>0,P(t)pI},

    where

    pI=λ2λ21(rμ)(Cr+KI).

    Thus, the reduction does not affect the optimal activating time. As same as the case of rμ, the firm should never exit the project and the reduction does not affect exit decisions.

    Assume that r>μ and

    (1α)C+(αCrKO)exp(rδ)>0.

    We first analyze the effects of reduction on the optimal start time of the exit. By (ⅱ) of Theorem 3.2, the optimal trigger price is an increasing function of α if

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    a decreasing function if

    exp(μδ)(CrKO)<Cexp(rδ)rKO,

    and a constant function if

    exp(μδ)(CrKO)=Cexp(rδ)rKO.

    We list some examples to illustrate the analysis. Taking

    r=0.2,μ=0.1,σ=0.3,δ=2,C=5,KI=20,andKO=10,

    we have

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    then the optimal trigger price is an increasing function of α. See Figure 1. If we replace μ=0.1 with μ=0.1, we have a decreasing function. See Figure 2.

    Figure 1.  α-pO-increasing.
    Figure 2.  α-pO-decreasing.

    We conclude that:

    (1) If

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    as the reduction increases (i.e., α decreases), the firm will postpone the exit.

    (2) If

    exp(μδ)(CrKO)<Cexp(rδ)rKO,

    as the reduction increases, the firm will advance the exit.

    (3) If

    exp(μδ)(CrKO)=Cexp(rδ)rKO,

    the reduction does not affect the exit.

    To analyze the effects of reduction on the optimal time to activate the project, we define two functions as follows:

    f(p):=A(λ2λ1)pλ1

    and

    g(p):=(λ21)rμp+λ2(Cr+KI)

    according to (ⅱ) of Theorem 3.2. Thus, the functions f and g intersect at two points, say, (p1,f(p1)) and (p2,f(p2)) with p1<p2, and the optimal trigger price pI of activating the project is given by pI=p2.

    To capture the behavior of pI as α varies, we only need to examine the behavior of the coefficient A as α varies. Setting

    M:=(Cexp(rδ)rKO)(1exp((μr)δ))+(1λ1)exp(rδ)(CrKOCexp(μδ)+exp((μr)δ)rKO)

    and

    N:=C(1exp(rδ))(1exp((μr)δ)),

    after some calculations, we find that:

    (1) If M/N0, A is increasing on [0,1] and pI is decreasing on [0,1].

    (2) If M/N1, A is decreasing on [0,1] and pI is increasing on [0,1].

    (3) If 0<M/N<1, A is decreasing on [0,M/N] and increasing on [M/N,1] and pI is increasing on [0,M/N] and decreasing on [M/N,1].

    Numerical examples help us understand the analysis above.

    Figure 3 demonstrates the decreasing of pI (r=0.2, μ=0.1, σ=0.3, δ=0.6, C=5, KI=20, and KO=10). Figure 4 demonstrates the increasing of pI (r=0.2, μ=0.1, σ=0.3, δ=2, C=5, KI=20, and KO=10), and Figure 5 demonstrates the non-monotonicity of pI (r=0.2, μ=0.1, σ=0.3, δ=0.6, C=5, KI=20, and KO=10).

    Figure 3.  α-pI-decreasing.
    Figure 4.  α-pI-increasing.
    Figure 5.  α-pI-non-monotonicity.

    In other words, here are the effects of the reduction on the optimal time of activating the project:

    (1) If M/N0, as the reduction increases (i.e., α decreases), the firm will postpone activating the project.

    (2) If M/N0, as the reduction increases, the firm will advance activating the project.

    (3) If 0<M/N<1, as the reduction increases, the firm will postpone activating the project then advance activating the project.

    There is an apparent phenomenon that firms usually reduce their output rate during stopping the regular production of a project. We intend to analyze the effects of reduction on the optimal entry-exit decision. Since the papers [3,15] have investigated the effects of the construction period on entry-exit decisions, we assume that the project has been constructed and the production immediately starts for concentrating on the study of the effects of reduction.

    We introduce the output rate into the models [14,15] and describe the problem using the optimal stopping theory, obtaining explicit solutions in Theorems 3.1 and 3.2. These explicit solutions help us in discovering the effects of reduction on the optimal entry-exit decision.

    We carefully examine the effects of reduction. According the analysis listed in Section 4, we come to the effects of reduction as follows. If the firm should never exit the project to obtain the maximal profit, the reduction does not affect the optimal entry-exit decision. However, if the firm exits the project in finite time, the effects show in a complicated manner. We study the effects analytically and numerically, and provide the relations of the parameters involved in the model that can determine the effects.

    The author declares he has not used Artificial Intelligence (AI) tools in the creation of this article.

    Many thanks are due to the editors and reviewers for their constructive suggestions and valuable comments. This work is partially supported by the Fundamental Research Funds for the Central Universities (Grant No. N2023034).

    No potential conflict of interest exists regarding the publication of this paper.



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