Abstract
We consider an irreversible capacity expansion model in which additional investment has a strictly negative effect on the value of an underlying stochastic economic indicator. The associated optimisation problem takes the form of a singular stochastic control problem that admits an explicit solution. A special characteristic of this stochastic control problem is that changes of the state process due to control action depend on the state process itself in a proportional way.
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1 Introduction
A standard capacity expansion model, which is a special case of the model studied by Kobila [34], can be described as follows. We model market uncertainty by means of the geometric Brownian motion given by
for some constants b and \(\sigma \ne 0\), where W is a standard one-dimensional Brownian motion. The random variable \(X_t^0\) can represent an economic indicator such as the price of or the demand for one unit of a given investment project’s output at time t. The firm behind the project can invest additional capital at proportional costs at any time, but cannot disinvest from the project. We denote by y the project’s initial capital at time 0 and by \(\zeta _t\) the total additional capital invested by time t. We assume that there is no capital depreciation, so the total capital invested at time t is
The investor’s objective is to maximise the total expected discounted payoff resulting from the project’s management, which is given by the performance index
over all capacity expansion strategies \(\zeta \). The discounting rate \(r>0\) and the cost of each additional unit of capital \(K>0\) are constants, while h is an appropriate running payoff function.
Under suitable assumptions on the problem data, the solution to this stochastic control problem is characterised by a threshold given by a strictly increasing free-boundary function \(G^0: {\mathbb R}_+ \rightarrow {\mathbb R}_+\). In the special case that arises when \(h(x,y) = x^\alpha y^\beta \), for some \(\alpha > 0\) and \(\beta \in ]0,1[\), namely, when h is a so-called Cobb-Douglas production function,
where \(m < 0\) is an appropriate constant. If the initial condition (x, y) is strictly below the graph of the function \(G^0\) in the x-y plane, then it is optimal to invest so that the joint process \((X^0,Y)\) has a jump at time 0 that positions it in the graph of \(G^0\). Otherwise, it is optimal to take minimal action so that the state process \((X^0,Y)\) does not fall below the graph of \(G^0\), which amounts to reflecting it in \(G^0\) in the positive y-direction.
Irreversible capacity expansion models have attracted considerable interest and can be traced back to Manne [38] (see Van Mieghem [47] for a survey). More relevant to this paper models have been studied by several authors in the economics literature: see Dixit and Pindyck [17, Chapter 11] and references therein. Related models that have been studied in the mathematics literature include Davis, Dempster, Sethi and Vermes [13], Arntzen [4], Øksendal [42], Wang [48], Chiarolla and Haussmann [11], Bank [6], Alvarez [2, 3], Løkka and Zervos [35], Steg [45], Chiarolla and Ferrari [9], De Angelis, Federico and Ferrari [15], and references therein. Furthermore, capacity expansion models with costly reversibility were introduced by Abel and Eberly [1], and were further studied by Guo and Pham [22], Merhi and Zervos [40], Guo and Tomecek [23, 24], Guo, Kaminsky, Tomecek and Yuen [21], Løkka and Zervos [36], De Angelis and Ferrari [16], and Federico and Pham [19].
In the model that we have briefly discussed above, additional investment does not influence the underlying economic indicator, which is unrealistic if one considers supply and demand issues. The nature of the optimal strategy is such that, if \(b < {\frac{1}{2}}\sigma ^2\), then \(\lim _{t \rightarrow \infty } X_t^0 = 0\) and the investment’s maximal optimal capacity level remains finite for realistic choices of the problem data. On the other hand, if \(b \ge {\frac{1}{2}}\sigma ^2\), then \(\limsup _{t \rightarrow \infty } X_t^0 = \infty \) and the optimal capacity level typically converges to \(\infty \) as \(t \rightarrow \infty \).
The model that we study here assumes that additional investment has a strictly negative effect on the value of the underlying economic indicator process X. We assume that increasing the project’s capacity by a very small amount \(\Delta \zeta _t = \varepsilon \) at time t affects the process X linearly, namely,
for some constant \(c>0\), where we have taken X to be càglàd. Furthermore, we assume that increasing the project’s capacity by an amount \(\Delta \zeta _t > 0\) at time t has the same effect on the process X as increasing the project’s capacity N times infinitesimally close to each other by an amount \(\Delta \zeta _t / N\) for every choice of N, which gives rise to the identities
These considerations suggest the modelling of market uncertainty by the solution to the SDE
where
in which expression, \(\zeta ^\mathrm {c}\) denotes the continuous part of the increasing process \(\zeta \). At this point, it is worth noting that Guo and Zervos [25] have considered the same state dynamics in the optimal execution problem that they study. The objective is to maximise over all admissible capacity expansion strategies \(\zeta \) the performance criterion
where \(r, K > 0\) are constants and the running payoff function h satisfies Assumption 1 in the next section.
The solution to this problem is again characterised by a threshold defined by a strictly increasing free-boundary function G. Informally, the optimal strategy can be described as the one in the problem defined by (1)–(3). However, reflection in the free-boundary G is oblong rather than in the positive y-direction (see Figs. 1, 2, 3). Furthermore, the negative effect that additional investment has on the underlying economic indicator X results in a maximal optimal capacity level that is bounded in cases of special interest, such as the ones arising, e.g., when the running payoff function h is a Cobb-Douglas production function (see Example 2).
From a stochastic control theoretic perspective, the problem that we solve has the features of singular stochastic control, which was introduced by Bather and Chernoff [7] who considered a simplified model of spaceship control. In their seminal paper, Beneš, Shepp and Witsenhausen [8] were the first to solve rigorously an example of a finite-fuel singular control problem. Since then, the area has attracted considerable interest in the literature. Apart from references that we have discussed in the context of capacity expansion models, Bahlali et al. [5] Chiarolla and Haussmann [10], Chow, Menaldi and Robin [12], Davis and Zervos [14], Fleming and Soner [20, Chapter VIII], Haussmann and Suo [27, 28], Harrison and Taksar [26], Jack, Johnson and Zervos [29], Jacka [30, 31], Karatzas [32], Ma [37], Menaldi and Robin [39], Øksendal [42], Shreve et al. [43], Soner and Shreve [44], Sun [46] and Zhu [49], provide an alphabetically ordered list of further contributions.
In the references discussed above, the controlled process affects the state dynamics in a purely additive way: the change of the state process due to control action does not depend on the state process itself. Singular stochastic control models in which changes of the state process due to control action may depend on the state process were introduced and studied by Dufour and Miller [18] and Motta and Sartori [41]. To the best of our knowledge, problems with state dynamics such as the ones given by (4)–(5) have not been considered in the literature before. Furthermore, the problem that we solve is the very first one in the singular stochastic control literature that involves control action that does not affect the state dynamics in a purely additive way and admits an explicit solution (see also Remark 1 in the next section).
2 Problem Formulation and Assumptions
We fix a probability space \((\Omega , {\mathcal F},{\mathbb {P}})\) equipped with a filtration \(({\mathcal F}_t)\) satisfying the usual conditions of right continuity and augmentation by \({\mathbb {P}}\)-negligible sets, and carrying a standard one-dimensional \(({\mathcal F}_t)\)-Brownian motion W. We denote by \(\mathcal Z\) the family of all càglàd \(({\mathcal F}_t)\)-adapted increasing process \(\zeta \) such that \(\zeta _0 = 0\).
The state space of the control problem that we study is defined by
where \(\bar{y} \in ]0, \infty ]\) is the maximal capital that can be invested in the project, namely, the maximum capacity level that can be achieved. Given a capacity expansion processes \(\zeta \in {\mathcal Z}\), we consider the capacity process Y defined by (2) and the economic indicator process X given by (4)–(5). Using Itô’s formula, we can verify that
where \(X^0\) is the geometric Brownian motion defined by (1).
Definition 1
The set \(\mathcal A\) of all admissible capacity expansion strategies is the family of all processes \(\zeta \in {\mathcal Z}\) such that
\( \Box \)
The objective of the control problem is to maximise the performance index \(J_{x,y}\) defined by (6) over all admissible strategies \(\zeta \in {\mathcal A}\), for each initial condition \((x,y) \in {\mathcal S}\). Accordingly, we define the problem’s value function v by
Remark 1
In view of (7), we can see that the stochastic optimisation problem we solve is equivalent to maximising
over all admissible strategies \(\zeta \in {\mathcal A}\), where the dynamics of the state process \((X^0, Y)\) are given by (1)–(2). At first glance, this observation puts us in the context of the standard singular stochastic control theory because control action affects the dynamics of \((X^0, Y)\) in a purely additive way. However, such a reformulation is of limited theoretical value because the problem’s initial condition y enters non-trivially in the description of the performance criterion, which is a situation that is typically associated with time-inconsistent control problems. \(\Box \)
Our analysis involves the general solution to the second order Euler’s ODE
which is given by
for some \(A, B \in {\mathbb R}\), where the constants \(m<0<n\) are the solutions to the quadratic equation
given by
Our analysis also involves the function H defined by
This function has a natural economic interpretation. Indeed, increasing capacity by a small amount \(\varepsilon > 0\) causes the joint process (X, Y) to jump from a value (x, y) to the value \((x - cx \varepsilon , y + \varepsilon )\). Noting that
we can see that H(x, y) represents the project’s marginal running payoff rate in excess of the marginal cost of capital rate. In view of standard economics theory, this interpretation suggests that (a) the function \(H(\cdot , y)\) should be increasing for all \(y \ge 0\) because higher values of the underlying economic indicator X, which models the price of or the demand for one unit of the project’s output, should reflect higher values of marginal running payoff, and (b) the function \(H(x, \cdot )\) should be decreasing for all \(x>0\) because the project’s payoff rate should be concave in the volume of its output due to the balancing of supply and demand. These observations suggest requirements (17)–(19) in the following assumption. In fact, the conditions reflected by (17)–(19) are much weaker than the ones suggested by the above considerations. However, the relaxations involved present no added complications in our analysis whatsoever. The underlying economics theory also suggests that the running payoff function h should be increasing in the value of the underlying economic indicator X for each fixed value of the project’s capacity, which is captured by condition (14). The rest of the conditions appearing in the following assumption, which is admittedly rather long to state, are of a purely technical nature. It is worth noting that (15) is equivalent to the probabilistic condition
(see (77)–(78) in Appendix 2).
Assumption 1
The constants r, K are strictly positive, the function h is \(C^3\),
There exists a point \(x_0 \ge 0\) and a continuous strictly increasing function \(y^\dagger : ]x_0, \infty [ \rightarrow {\mathbb R}_+\) such that
where
Also, there exist a decreasing function \(\Psi : ]y_0, y_\infty [ \rightarrow ]0,\infty [\) such that \(\lim _{y \downarrow 0} \Psi (y) < \infty \) if \(x_0 > 0\) as well as constants \(C_0>0\) and \(\vartheta \in ]0,n[\) such that
\(\Box \)
We denote by \(x^\dagger \) the inverse of the function \(y^\dagger \) that is defined by
Example 1
Suppose that \(\bar{y} = \infty \) and h is a so-called Cobb-Douglas function, given by
where \(\alpha \in ]0,n[\) and \(\beta \in ]0,1]\) are constants. In this case, we can check that
If we define
then we can see that the calculations
imply that there exists a unique function \(y^\dagger : ]x_0, \infty [ \rightarrow {\mathbb R}_+\) such that (16)–(17) hold true. Furthermore, differentiating the identity \(H \bigl ( x, y^\dagger (x) \bigr ) = 0\) with respect to x, we can see that
so \(y^\dagger \) is indeed strictly increasing. Also, it is straightforward to check that (19)–(18) and (20)–(21) are all satisfied for \(\vartheta = n - \alpha \) and
\(\square \)
3 The Solution to the Control Problem
We solve the stochastic control problem that we consider by constructing an appropriate classical solution \(w: {\mathcal S} \rightarrow {\mathbb R}\) to the Hamilton-Jacobi-Bellman (HJB) equation
where \(w_y (x,0) = \lim _{y \downarrow 0} w_y (x,y)\). To obtain qualitative understanding of this equation, we consider the following heuristic arguments. At time 0, the project’s management has two options. The first one is to wait for a short time \(\Delta t\) and then continue optimally. Bellman’s principle of optimality implies that this option, which is not necessarily optimal, is associated with the inequality
Applying Itô’s formula to the second term in the expectation, and dividing by \(\Delta t\) before letting \(\Delta t \downarrow 0\), we obtain
The second option is to increase capacity by \(\varepsilon > 0\), and then continue optimally. This action is associated with the inequality
Rearranging terms and letting \(\varepsilon \downarrow 0\), we obtain
Furthermore, the Markovian character of the problem implies that one of these options should be optimal and one of (25), (26) should hold with equality at any point in the state space \(\mathcal S\). It follows that the problem’s value function v should identify with an appropriate solution w to the HJB equation (24).
To construct the solution w to (24) that identifies with the value function v, we first consider the existence of a strictly increasing function \(G: ]y_0, y_\infty [ \rightarrow ]0, \infty [\) that partitions the state space \(\mathcal S\) into two regions, the “waiting” region \(\mathcal W\) and the “investment” region \(\mathcal I\) defined by
In view of the interpretation of the function H defined by (13) as the project’s marginal running payoff rate in excess of the marginal cost of capital rate, which we have discussed in the previous section, we can see that increasing capacity cannot be optimal whenever the state process takes values \((x,y) \in {\mathcal S}\) such that \(H(x,y) < 0\). This observation, (17) in Assumption 1 and (22) suggest that the inequality
should hold true. Figures 1, 2, and 3 depict possible configurations of the waiting and the investment regions.
Inside the region \(\mathcal {W}\), the heuristic arguments that we have briefly discussed above suggest that w should satisfy the differential equation
In light of the theory that we review in Appendix 2 and the intuitive idea that the value function should remain bounded as \(x \downarrow 0\), every relevant solution to this ODE is given by
for some function A, where n is given by (12) and \(R(\cdot , y)\) is defined by (79) for \(k = h(\cdot , y)\), namely,
On the other hand, w should satisfy
which implies that
To determine A and G, we postulate that w is \(C^{2,1}\), in particular, along the free-boundary G. Such a requirement and (28)–(31) yield the system of equations
In view of the definition (29) of R, the associated expression (84) for the function \(x \mapsto xR_x (x,y)\) and (83), we can see that this system is equivalent to
where H is defined by (13) and
We can also check that the solution to (35) is given by
if the integrals converge.
The following result, the proof of which we develop in Appendix 1, is concerned with the solution to the system of equations (34)–(35).
Lemma 1
Suppose that Assumption 1 holds true. The equation \(q(x,y)=0\) for \(x>0\) defines uniquely a strictly increasing \(C^1\) function \(G: ]y_0, y_\infty [ \rightarrow ]0,\infty [\), which satisfies
where \(x^\dagger \) is defined by (22). Furthermore, the function A given by (37) is well-defined and real-valued, and there exists a constant \(C_1 > 0\) such that
where the decreasing function \(\Psi \) and the constant \(\vartheta > 0\) are as in (21), and
where \(g^{-1}\) is the inverse of the strictly increasing function g that is defined by
Remark 2
The last limit in (38) implies that, under the optimal strategy, if \(\bar{y} < \infty \), then the maximal capacity level \(\bar{y}\) is never reached. This result is due to the assumption that the function \(y^\dagger \) appearing in Assumption 1 is such that \(y^\dagger (\chi ) < \lim _{x \rightarrow \infty } y^\dagger (x) \equiv y_\infty \le \bar{y}\) for all \(\chi \in ]x_0, \infty [\). Our analysis could be trivially modified to allow for the possibility that \(\bar{y} < \infty \) and \(\lim _{y \uparrow \bar{y}} G(y) < \infty \), which would give rise to the situation where the maximal capacity level \(\bar{y}\) is reached in finite time with strictly positive probability. Such a relaxation would simply involve allowing for the strictly increasing function \(y^\dagger \) to be such that \(\lim _{x \rightarrow \infty } y^\dagger (x) \equiv y_\infty > \bar{y}\). However, we have opted against such a relaxation because this would complicate the notation and the proof of Lemma 1 substantially. \(\square \)
Example 2
Suppose that h is a Cobb-Douglas function given by (23) in Example 1. In this case, we can check that
Figures 2 and 3 illustrate this example. \(\square \)
To complete the construction of the solution w to the HJB equation (24) that identifies with the problem’s value function v, we note that there exists a mapping \(z : {\mathcal I} \rightarrow {\mathbb R}_+\) such that
Indeed, this claim follows immediately from the calculations
in which, we have used (38) and the fact that G is increasing. We prove the following result in Appendix 1.
Lemma 2
Suppose that Assumption 1 holds true. The function w defined by
where A is defined by (37) and z is given by (43), is a \(C^{2,1}\) solution to the HJB equation (24). Furthermore, the function \(w(\cdot ,y)\) is increasing and there exists a constant \(C_2 > 0\) such that
where the decreasing function \(\Psi \) is as in (20)–(21).
We can now establish the main result of the paper.
Theorem 1
Suppose that Assumption 1 holds true. The value function v of the control problem formulated in Sect. 2 identifies with the solution w to the HJB equation (24) given by (44) in Lemma 2 and the optimal capacity expansion strategy \(\zeta ^\star \) is given by
where
g is defined by (41), and \(X^0\) is the geometric Brownian motion given by (1).
Proof
Fix any initial condition \((x,y) \in {\mathcal S}\) and any admissible strategy \(\zeta \in {\mathcal A}\). In view of Itô-Tanaka-Meyer’s formula and the left-continuity of the processes X, Y, we can see that
where
Combining this calculation with the observation that
we obtain
Since w satisfies the HJB equation (24), it follows that
In view of the integration by parts formula and (2), we can see that
This identity, the admissibility condition (8) in Definition 1 and the monotone convergence theorem imply that
which implies that
The lower bound in (20), the estimate (45) and (52) imply that
The admissibility condition (8) and (53) imply that the random variable on the right-hand side of these inequalities has finite expectation. Combining this observation with (51), we can see that \({\mathbb {E}}\left[ \inf _{T \ge 0} M_T \right] > - \infty \). Therefore, the stochastic integral M is a supermartingale and \({\mathbb {E}}\left[ M_T \right] \le 0\) for all \(T>0\). Furthermore, Fatou’s lemma implies that
Taking expectations in (51) and passing to the limit, we obtain
The inequality \(J_{x,y} (\zeta ) \le w(x,y)\) now follows because the estimate (45) implies that
Thus, we have proved that \(v(x,y) \le w(x,y)\).
To prove the reverse inequality and establish the optimality of the process \(\zeta ^\star \) given by (47), we first consider the possibility that \([y_\infty , \bar{y}] \cap {\mathbb R}_+ \ne \emptyset \) and \(y \in [y_\infty , \bar{y}]\). In this case, \(\zeta _t^\star = 0\) for all \(t \ge 0\), and
which establish the required claims.
In the rest of the proof, we assume that \(y < y_\infty \). In this case,
for all \(t>0\), and, apart from a possible initial jump of size \((g^{-1} (e^{cy}x) - y)^+\) at time 0, the process \((e^{cy} X^0 , Y^\star )\) is reflecting in the free-boundary g in the positive direction. In particular,
In view of (7) and the definition (41) of g, we can see that
where \(X^\star \) is the solution to (4) given by (7). It follows that the process \((X^\star , Y^\star )\) satisfies
Since the function g is strictly increasing, \(\zeta _0^\star > 0\) if and only if \(xe^{cy} > g(y) \mathop {=}\limits ^{(41)} e^{cy} G(y)\). Therefore,
Furthermore, given any \((x,y) \in {\mathcal I}\), we note that
which implies that \(\zeta _0^\star = z(x,y)\), where the function z is given by (43). It follows that
In light of (56)–(58) and the construction of the solution w to the HJB equation (24), we can see that (50) implies that
for all \(T>0\), where the local martingale \(M^\star \) is defined as in (49).
To show that \(\zeta ^\star \) is indeed admissible, we use (40) and (55) to calculate
Combining these inequalities with the first estimate in (76), we can see that
It follows that
which proves that \(\zeta ^\star \in {\mathcal A}\).
To proceed further, we note that the inequality in (56), the fact that \(w(\cdot , y)\) is increasing and the bound given by (46) imply that, given any \(t>0\),
the last inequality following because \(\Psi \) is decreasing. Also, (20) and (56) imply that
The estimate (40) and (55) imply that
It follows that there exists a constant \(C_3 = C_3 (y)\) such that
for all \(t>0\). These inequalities and the estimates (76) imply that
and
In view of (59) and (61), we can see that \({\mathbb {E}}\left[ \sup _{T>0} M_T^\star \right] < \infty \). Therefore, the stochastic integral \(M^\star \) is a submartingale and \({\mathbb {E}}\left[ M_T^\star \right] \ge 0\) for all \(T>0\). Furthermore, Fatou’s lemma implies that
In view of these observations and (62), we can take expectations in (59) and pass to the limit to obtain
This result and the inequality \(v(x,y) \le w(x,y)\) that we have proved above, imply that \(v(x,y) = w(x,y)\) and that \(\zeta ^\star \) is optimal. \(\square \)
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We thank an anonymous referee for constructive suggestions that improved the paper.
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Appendices
Appendix 1: Proof of Lemmas 1 and 2
Proof of Lemma 1
Given any \(y \in ]y_0, y_\infty [\), we observe that
where \(x^\dagger \) is defined by (22) in Assumption 1. Also, we note that (17) and (18) in Assumption 1 imply that there exist constants \(\varepsilon _1 = \varepsilon _1 (y)\) and \(x_1 = x_1 (y) > x^\dagger (y)\) such that \(H(x,y) \ge \varepsilon _1\) for all \(x \ge x_1\). Given such a choice of constants, we calculate
because \(m<0\). Combining these observations with the fact that \(q(0,y) = 0\), we can see that the equation \(q(x,y) = 0\) for \(x>0\) has a unique solution \(G(y) > x^\dagger (y)\) for all \(y \in ]y_0, y_\infty [\), and that G satisfies (38).
To see that the function \(G: ]y_0, y_\infty [ \rightarrow ]0,\infty [\) is \(C^1\) and strictly increasing, we differentiate the identity \(q \bigl ( G(y),y \bigr ) = 0\) with respect to y to obtain
the inequality following from (19) in Assumption 1.
To establish (40), we note that
and
where we have used (38) and the facts that G is increasing and \(n-\vartheta > 0\). Combining these inequalities with the fact that G and g are continuous increasing functions with the same domain \(]y_0, y_\infty [\), we can see that there exists a constant \(C_1 > 0\) such that
For \(x>x_0\) and \(y = g^{-1} (x)\), this inequality implies the estimate in (40).
In view of (21) and the fact that the functions G, \(-\Psi \) are increasing, we can see that, given any \(y \in ]y_0, y_\infty [\),
which implies (39). Finally, the strict positivity of A follows from (17) and the inequality in (38). \(\square \)
Proof of Lemma 2
In view of its construction, we will prove that w is \(C^{2,1}\) if we show that \(w_y\), \(w_x\) and \(w_{xx}\) are continuous along the free-boundary G. To this end, we consider any \((x,y) \in {\mathcal I}\), we recall the definition (44) of w and the definition (43) of z, and we use (30)–(31) to calculate
and
These calculations imply the required continuity results because \(\lim _{n \rightarrow \infty } z(x_n,y_n) = 0\) for every convergent sequence \((x_n,y_n)\) in \(\mathcal I\) such that \(\lim _{n \rightarrow \infty } x_n = \lim _{n \rightarrow \infty } G(y_n)\).
To prove (45)–(46), we note that the bounds of h in (20), the definition (29) of R and the identity \(\sigma ^2 mn = -r\) imply that
The lower of these bounds and the positivity of A (see (39)) imply that
In light of (14) and (82) in Appendix 2, we can see that \(R(\cdot , y)\) is increasing for all \(y \in [0,\bar{y}] \cap {\mathbb R}\). Combining this observation with the inequalities \(A>0\) and \(n>0\), we deduce that \(w_x (x, y) \ge 0\) for all \((x,y) \in {\mathcal W}\). This result, (43) and (65) imply that \(w(\cdot , y)\) is increasing for all \(y \in [0,\bar{y}] \cap {\mathbb R}\), which, combined with (68), implies (45). Also, (46) follows immediately from (39) and the upper bound in (67).
It remains to show that w satisfies the HJB equation (24). By the construction and the \(C^{2,1}\) continuity of w, we will achieve this if we show that
To see (69), we consider any \((x,y) \in {\mathcal I}\) and we use (44), (65)–(66) and the fact that w satisfies the ODE (27) inside \(\mathcal W\) to calculate
These calculations, (17), (38), (43) and the continuity of z imply (69).
To prove (70), we first consider the possibility that \(y_\infty < \bar{y}\). In this case, we use the fact that \(w=R\) inside \({\mathcal W} \cap \bigl ( {\mathbb R}_+ \times [y_\infty , \bar{y}] \bigr )\), the definition (29) of R, the associated expression (84) for the function \(x \mapsto xR_x (x,y)\) and (83) to calculate
the inequality following thanks to (17) in Assumption 1.
To proceed further, we note that, inside \({\mathcal W} \cap \bigl ( {\mathbb R}_+ \times ]y_0, y_\infty [ \bigr )\), the definition (44) of w, (32), (34), calculations similar to the ones in (71) and the definition (13) of H imply that
In light of (17), (38) and the fact that \(m < 0 < n\), we can see that
which, combined with the identity \(\varrho \bigl ( G(y), y \bigr ) = 0\), implies that
Also, we can use the inequality
which follows from (17) in Assumption 1 and (34), to calculate
the inequality following from (17) and the fact that \(n>0\).
Finally, we can use the fact that m, n are the solutions to the quadratic equation (11) and straightforward calculations to obtain
This inequality and the maximum principle imply that the function \(\varrho \) has no positive maximum inside \(]0, x^\dagger (y)[\), which, combined with (73)–(74), implies that \(\varrho (x,y) \le 0\) for all \(y \in ]y_0, y_\infty [\) and \(x \in ]0, G(y)]\), and (70) follows. \(\square \)
Appendix 2: A Second Order Linear ODE
In this section, we review certain results regarding the solvability of a second order linear ODE on which our analysis has been based. All of the claims that we do not prove here are standard and can be found in several references (e.g., with the exception of (76), which is proved in Merhi and Zervos [40, Lemma 1], all results can be found in Knudsen et al. [33]).
Given a constant \(\lambda \),
where \(X^0\) is the geometric Brownian motion given by (1) and \(m < 0 < n\) are the constants defined by (12). Furthermore, for all \(\lambda \in ]0,n[\), there exist constants \(\varepsilon , C > 0\) such that
for all \(x>0\).
A Borel measurable function \(k : ]0,\infty [ \rightarrow {\mathbb R}\) satisfies
if and only if
In the presence of these equivalent integrability conditions, the function R defined by
is a special solution to the non-homogeneous ODE
that admits the probabilistic expression
Furthermore,
In our analysis we have used the following result.
Lemma 3
Consider any \(C^1\) function \(k: ]0,\infty [ \rightarrow {\mathbb R}\) satisfying the equivalent integrability conditions (77)–(78) and suppose that there exists \(\varepsilon > 0\) such that
Then
in which expression, both integrals are well-defined and real-valued.
Proof
We first note that the integrability condition (78) implies that the limits
exist in \({\mathbb R}\) and that
To see the latter claim, suppose that \(\liminf _{z \downarrow 0} z^{-m} |k(z)| > 0\). In such a case, there exist constants \(\varepsilon , z_1 > 0\) such that \(z^{-m} |k(z)| \ge \varepsilon \) for all \(z \le z_1\). Therefore,
which contradicts (78). We can argue similarly by contradiction to prove the second limit in (85).
Using the integration by parts formula, we calculate
The assumptions that we have made on \(k'\) and the monotone convergence theorem imply that the limit \(\lim _{z \downarrow 0} \int _z^x s^{-m} k'(s) \, ds\) exists. Therefore, we can pass to the limit as \(z \downarrow 0\) in (86) to obtain
Similarly, we can see that
The required result follows immediately from these calculations and the expression
\(\square \)
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Al Motairi, H., Zervos, M. Irreversible Capital Accumulation with Economic Impact. Appl Math Optim 75, 525–551 (2017). https://doi.org/10.1007/s00245-016-9341-9
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DOI: https://doi.org/10.1007/s00245-016-9341-9