A Class of Singular Control Problems and the Smooth Fit Principle

TL;DR: In particular, the authors analyzes a class of singular control problems for which value functions are not necessarily smooth and provides necessary and sufficient conditions for the well-known smooth fit principle, along with the regularity of the value functions.

Abstract: This paper analyzes a class of singular control problems for which value functions are not necessarily smooth. Necessary and sufficient conditions for the well-known smooth fit principle, along with the regularity of the value functions, are given. Explicit solutions for the optimal policy and for the value functions are provided. In particular, when payoff functions satisfy the usual Inada conditions, the boundaries between action and continuation regions are smooth and strictly monotonic, as postulated and exploited in the existing literature (see [A. K. Dixit and R. S. Pindyck, Investment under Uncertainty, Princeton University Press, Princeton, NJ, 1994]; [M. H. A. Davis et al., Adv. in Appl. Probab., 19 (1987), pp. 156-176]; [T. O. Kobila, Stochastics Stochastics Rep., 43 (1993), pp. 29-63]; [A. B. Abel and J. C. Eberly, J. Econom. Dynam. Control, 21 (1997), pp. 831-852]; [A. Oksendal, Finance Stoch., 4 (2000), pp. 223-250]; [J. A. Scheinkman and T. Zariphopoulou, J. Econom. Theory, 96 (2001), pp. 180-207]; [A. Merhi and M. Zervos, SIAM J. Control Optim., 46 (2007), pp. 839-876]; and [L. H. Alvarez, A General Theory of Optimal Capacity Accumulation under Price Uncertainty and Costly Reversibility, Working Paper, Helsinki Center of Economic Research, Helsinski, Finland, 2006]). Illustrative examples for both smooth and nonsmooth cases are discussed to emphasize the pitfall of solving singular control problems with a priori smoothness assumptions.

Summary

1 Introduction

• Consider the following problem in reversible investment/capacity planning that arises naturally in resource extraction and power generation.
• Facing the risk of market uncertainty, companies extract resources (such as oil or gas) and choose the capacity level in response to the random fluctuation of market price for the resources, subject to some capacity constraints, as well as the associated costs for capacity expansion and contraction.
• Both necessary and sufficient conditions on the differentiability of the value function and on the smooth fit principle are established.
• In fact, when the payoff function is not smooth, this paper is the first to rigorously characterize the action and no-action regions, and to explicitly construct both the optimal policy and the value function.

2.1 Problem

• This is a continuous time formulation of the aforementioned risk management problem.
• The goal of the company is to maximize its long-term profit with a payoff function that depends on both the resource extraction rate and the market price, with a form of H(Y )Xλ.
• And the control problem can be reduced to an equivalent yet simpler singular control problem .
• In fact, this equivalent control problem is more standard in the existing literature for both singular control and (ir)reversible investment.

3 Derivation of the Solution

• The derivation goes as follows: first, a collection of corresponding optimal switching problems is established and solved; then, the consistency of the optimal switching controls is proved; finally, the existence of the corresponding integrable optimal singular control (ξ̂+, ξ̂−) ∈.
• A′y is established and the corresponding value function are derived.
• This method is built on the general correspondence between singular controls and switching controls outlined in Guo and Tomecek (2007).

3.1.2 Solution of the Optimal Switching Problems

• The derivation of the value function proceeds analogously to the derivation for the case of h(z) >.
• Moreover, these equations depend on z only through ν(z) and κ(z), implying that there exist unique constants κ, ν such that κ(z) ≡ κ and ν(z) ≡ ν for all z.

3.2 Derivation of the Optimal Switching Controls

• The authors then define a collection of these switching controls and prove that this collection satisfies the consistency property from Guo and Tomecek (2007), which implies that it corresponds to an admissible singular control.
• Note that since the regime values κ̂n are alternating, it suffices to define the switching times τ̂n.

3.2.2 Consistency of the Switching Controls

• The collection of switching controls (α̂(x, z))z∈(a,b) is consistent.
• Then the consistency follows from the following lemmas.
• For simplicity, the authors omit the dependence on x from the notation.
• Note that after the first switch, each subsequent switch requires that Xx move from (0, F (z)] to [G(z),∞) or vice versa.
• In particular, this quantity is independent of z. Since log(Xx) is a Brownian motion with drift, its sample paths are almost surely uniformly continuous on [0, t].

3.3 Derivation of the Optimal Singular Control

• Clearly, the following proposition holds, from Merhi and Zervos (2007).
• Therefore, after verifying the Standing Assumptions A1, A2 and A3, one can invoke Guo and Tomecek (2007, Proposition 2.13, Theorem 3.13, Theorem 3.10) and conclude that there exists a corresponding integrable singular control (ξ̂+, ξ̂−) ∈.
• To prove Theorem 2.5, the authors first establish some Lemmas.
• (The second one follows by a similar argument, and the last one is immediate from the definition of C and the first two.).

4 Regularity, Smooth Fit and Region Characterization

• The authors shall establish necessary and sufficient conditions for the smooth fit principle by exploiting both the structure of the payoff function and the explicit solution of the value function.
• This analysis leads to the proof of Theorem 2.6 on region characterization.

4.1 Regularity and Smooth Fit

• Therefore, d(x, ·) has only countably many discontinuities.
• This theorem follows naturally from the following Lemma and the Proposition.
• Thus fx must have at least two positive roots.

5 Examples and Discussions

• By now, it is clear from their analysis that without sufficient smoothness of the payoff function, the value function may be non-differentiable and the boundaries may be non-smooth or not strictly monotonic.
• Moreover, when the payoff function H is not continuously differentiable, the interior of C may not be simply connected.
• Note, however, the regions S0, S1 and C are mutually disjoint and simply connected by the monotonicity of F and G.
• The authors elaborate on these points with some concrete examples.

5.2 Discussion

• The above examples demonstrate how the regularity assumptions typically assumed by the traditional HJB approach may fail.
• Furthermore, Example 5.3 shows that in general, one may not have the smoothness of the boundary, as the boundaries F and G are not necessarily continuous or not even strictly increasing.

Figures

Figure 8: Interior of continuation region NOT connected

Figure 6: Value function C1, F,G are only C0: because H is not C2.

Figure 7: Value function NOT C1, F, G NOT continuous: because H is not C1.

Figure 4: Example from Guo-Tomecek (2006) of Case II–when boundaries are smooth

Figure 1: Illustration: Case I–when boundaries are smooth

Figure 3: Ilustration of Case II–when boundaries are smooth

Figure 2: Illustration: Case I–when boundaries are NOT smooth: h(z) = c/z for z < 1 and h(z) = −d/(2− z) for z > 1 with K0/d < K1/c.

Figure 5: Value function is C1, F,G are C1, but NOT strictly increasing: because H is NOT strictly concave.

Full text

UC Berkeley
Coleman Fung Risk Management Research Center Working
Papers 2006-2013
Title
A Class of Singular Control Problems and the Smooth Fit Principle
Permalink
https://escholarship.org/uc/item/8w46j0td
Authors
Guo, Xin
Tomecek, Pascal
Publication Date
2007-10-07
eScholarship.org Powered by the California Digital Library
University of California

University of California
Berkeley
Working Paper # 2007 -05
A Class of Singular Control Problems
and the Smooth Fit Principle
Xin Guo, UC Berkeley and Cornell University
Pascal Tomecek, Cornell University
October 7,2007

A Class of Singular Control Problems and the Smo oth Fit
Principle
∗
Xin Guo
†
UC Berkeley and Cornell
Pascal Tomecek
‡
Cornell University
October 7, 2007
Abstract
This paper analyzes a class of singular control problems for which value functions
are not necessarily smooth. Necessary and sufficient conditions for the well-known
smooth fit principle, along with the regularity of the value functions, are given. Explicit
solutions for the optimal policy and for the value functions are provided. In particular,
when payoff functions satisfy the usual Inada conditions, the boundaries between action
and no-action regions are smooth and strictly monotonic as postulated and exploited in
the existing literature (Dixit and Pindyck (1994); Davis, Dempster, Sethi, and Vermes
(1987); Kobila (1993); Abel and Eberly (1997); Øksendal (2000); Scheinkman and
Zariphopoulou (2001); Merhi and Zervos (2007); Alvarez (2006)). Illustrative examples
for both smooth and non-smooth cases are discussed, to highlight the pitfall of solving
singular control problems with a priori smoothness assumptions.
∗
This research is generously supported by grants from OpenLink Fund at the Coleman Fung Risk Man-
agement Research Center at UC Berkeley
†
Department of Industrial Engineering and Operations Research, UC Berkeley, CA 94720-1777, USA,
Phone (510) 642 3615, email: xinguo@ieor.berkeley.edu.
‡
Scho ol of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY 14853-3801,
USA, Phone (607) 255 1270, Fax (607) 255 9129, email: pascal@orie.cornell.edu.
1

1 Introduction
Consider the following problem in reversible investment/capacity planning that arises nat-
urally in resource extraction and power generation. Facing the risk of market uncertainty,
companies extract resources (such as oil or gas) and choose the capacity level in response
to the random fluctuation of market price for the resources, subject to some capacity con-
straints, as well as the associated costs for capacity expansion and contraction. The goal of
the company is to maximize its long-term profit, subject to these constraints and the rate
of resource extraction.
This kind of capacity planning with price uncertainty and partial (or no) reversibility
originated from the economics literature and has since attracted the interest of the ap-
plied mathematics community. (See Dixit and Pindyck (1994); Brekke and Øksendal (1994);
Davis, Dempster, Sethi, and Vermes (1987); Kobila (1993); Abel and Eberly (1997); Baldurs-
son and Karatzas (1997); Øksendal (2000); Scheinkman and Zariphopoulou (2001); Wang
(2003); Chiarolla and Haussmann (2005); Guo and Pham (2005) and the references therein.)
Mathematical analysis of such control problems has evolved considerably from the initial
heuristics to the more sophisticated and standard stochastic control approach, and from
the very special case study to general payoff functions. (See Harrison and Taksar (1983);
Karatzas (1985); Karatzas and Shreve (1985); El Karoui and Karatzas (1988, 1989); Ma
(1992); Davis and Zervos (1994, 1998); Boetius and Kohlmann (1998); Alvarez (2000, 2001 );
Bank (2005); Boetius (2005)). Most recently, Merhi and Zervos (2007) analyzed this problem
in great generality and provided explicit solutions for the special case where the payoff is of
Cobb-Douglas type. Their method is to directly solve the HJB equations, assuming certain
regularity conditions for both the value function and the boundaries between the action and
no-action regions. Guo and Tomecek (2007) later established sufficient conditions for the
smoothness of the value function by connecting the singular control problem with a collection
of optimal switching problems.
However, for many singular control problems in reversible investment and in areas such
as queuing and wireless communications (Martins, Shreve, and Soner (1996); Assaf (1997);
Harrison and Van Mieghem (1997); Ata, Harrison, and Shepp (2005)), there is no regularity
for either the value function or the boundaries. Therefore, two important mathematical issues
remain: 1) necessary conditions for regularity properties; and 2) characterization for the
value function and for the action and no-action regions when these regularity conditions fail.
Understanding these issues is especially important in cases where only numerical solutions
are available, and for which the assumption on the degree of the smoothness is wrong (see
also discussions in Section 5.2).
This paper addresses these two issues via the study of a class of singular control problems.
Both necessary and sufficient conditions on the differentiability of the value function and on
the smooth fit principle are established. Moreover, these conditions lead to a derivative-
based characterization of the investment, disinvestment and continuation regions even for
non-smooth value functions. In fact, when the payoff function is not smooth, this paper is the
2

first to rigorously characterize the action and no-action regions, and to explicitly construct
both the optimal policy and the value function. To be consistent with the literature in
(ir)reversible investment, the running payoff function in this paper depends on the resource
extraction rate and the market price in the form of H(Y )X
λ
. It is worth noting that H(·)
is any concave function of the capacity, and may be neither monotonic nor differentiable.
This includes the special cases investigated by Guo and Pham (2005); Merhi and Zervos
(2007); Guo and Tomecek (2007). In particular, when H satisifies the well-known Inada
conditions (i.e., continuously differentiable, strictly increasing, strictly concave, with H(0) =
0, H
0
(0
+
) = ∞, H
0
(∞) = 0), our results show that the boundaries between regions are
indeed continuous and strictly increasing as postulated and exploited in previous works:
Dixit and Pindyck (1994); Davis, Dempster, Sethi, and Vermes (1987); Kobila (1993); Abel
and Eberly (1997); Øksendal (2000); Scheinkman and Zariphopoulou (2001); Merhi and
Zervos (2007); Alvarez (2006). Also note that our method can be applied to more general
(diffusion) processes for the price dynamics, other than the geometric Brownian motion
assumed for explicitness in this paper. Finally, the construction between the functional form
of the boundaries and the payoff function itself is also novel, as the value function and the
boundaries may be neither smooth nor strictly monotonic as in the existing literature.
The most relevant and recent work to this paper is Alvarez (2006), which provides a
great deal of economic insight into the problem. However, Alvarez (2006) only handles
payoff functions satisfying the Inada conditions. In contrast, our solution is independent of
the regularity of the payoff and value functions.
Outline. The control problem is formally stated, with its value function and optimal p olicy
described in Section 2; details of the derivation are in Section 3. The main result of this
paper regarding the regularity of the value function is in Section 4. Examples are provided
in Section 5, including cases for which the value function is not differentiable, the optimal
controlled process not continuous, the boundaries of the action regions not smooth, and the
interior of the continuation region not simply connected.
2 Mathematical Problem and Solution
2.1 Problem
Let (Ω, F, F, P) be a filtered probability space and assume a given bounded interval [a, b] ⊂
(−∞, ∞). Consider the following problem:
Problem A.
V
H
(x, y) := sup
(ξ
+
,ξ
−
)∈A
00
y
J
H
(x, y; ξ
+
, ξ
−
),
3

Frequently asked questions

Q1. What are the contributions in "A class of singular control problems and the smooth fit principle" ?

This paper analyzes a class of singular control problems for which value functions are not necessarily smooth. Explicit solutions for the optimal policy and for the value functions are provided. Illustrative examples for both smooth and non-smooth cases are discussed, to highlight the pitfall of solving singular control problems with a priori smoothness assumptions. ∗This research is generously supported by grants from OpenLink Fund at the Coleman Fung Risk Management Research Center at UC Berkeley †Department of Industrial Engineering and Operations Research, UC Berkeley, CA 94720-1777, USA, Phone ( 510 ) 642 3615, email: xinguo @ ieor.