Watermark Options

We consider a new family of derivatives whose payoffs become strictly positive when the price of their underlying asset falls relative to its historical maximum. We derive the solution to the discretionary stopping problems arising in the context of pricing their perpetual American versions by means of an explicit construction of their value functions. In particular, we fully characterise the free-boundary functions that provide the optimal stopping times of these genuinely two-dimensional problems as the unique solutions to highly non-linear first order ODEs that have the characteristics of a separatrix. The asymptotic growth of these free-boundary functions can take qualitatively different forms depending on parameter values, which is an interesting new feature.


Introduction
Put options are the most common financial derivatives that can be used by investors to hedge against asset price falls as well as by speculators betting on falling prices. In particular, out of the money put options can yield strictly positive payoffs only if the price of their underlying asset falls below a percentage of its initial value. In a related spirit, equity default swaps (EDSs) pay out if the price of their underlying asset drops by more than a given percentage of its initial value (EDSs were introduced by J. P. Morgan London in 2003 and their pricing was studied by Medova and Smith [MS06]). Further derivatives whose payoffs depend on other quantifications of asset price falls include the European barrier and binary options studied by Carr [C06] and Vecer [V06], as well as the perpetual lookback-American options with floating strike that were studied by Pedersen [P00] and Dai [D01].
In this paper, we consider a new class of derivatives whose payoffs depend on asset price falls relative to their underlying asset's historical maximum price. Typically, a hedge fund manager's performance fees are linked with the value of the fund exceeding a "high watermark", which is an earlier maximum. We have therefore named this new class of derivatives "watermark" options. Deriving formulas for the risk-neutral pricing of their European type versions is a cumbersome but standard exercise. On the other hand, the pricing of their American type versions is a substantially harder problem, as expected. Here, we derive the complete solution to the optimal stopping problems associated with the pricing of their perpetual American versions.
To fix ideas, we assume that the underlying asset price process X is modelled by the geometric Brownian motion given by for some constants µ and σ = 0, where W is a standard one-dimensional Brownian motion. Given a point s ≥ x, we denote by S the running maximum process defined by In this context, we consider the discretionary stopping problems whose value functions are defined by v(x, s) = sup u(x, s) = sup and for some constants a, b, r, K > 0, where T is the set of all stopping times. In practice, the inequality r ≥ µ should hold true. For instance, in a standard risk-neutral valuation context, r > 0 should stand for the short interest rate whereas r − µ ≥ 0 should be the underlying asset's dividend yield rate. Alternatively, we could view µ > 0 as the short rate and r −µ ≥ 0 as additional discounting to account for counter-party risk. Here, we solve the problems we consider without assuming that r ≥ µ because such an assumption would not present any simplifications (see also Remark 1).
Watermark options can be used for the same reasons as the existing options we have discussed above. In particular, they could be used to hedge against relative asset price falls as well as to speculate by betting on prices falling relatively to their historical maximum. For instance, they could be used by institutions that are constrained to hold investmentgrade assets only and wish to trade products that have risk characteristics akin to the ones of speculative-grade assets (see Medova and Smith [MS06] for further discussion that is relevant to such an application). Furthermore, these options can provide useful risk-management instruments, particularly, when faced with the possibility of an asset bubble burst. Indeed, the payoffs of watermark options increase as the running maximum process S increases and the price process X decreases. As a result, the more the asset price increases before dropping to a given level, the deeper they may be in the money.
Watermark options can also be of interest as hedging instruments to firms investing in real assets. To fix ideas, consider a firm that invests in a project producing a commodity whose price or demand is modelled by the process X. The firm's future revenue depends on the stochastic evolution of the economic indicator X, which can collapse for reasons such as extreme changes in the global economic environment (e.g., see the recent slump in commodity prices) and / or reasons associated with the emergence of disruptive new technologies (e.g., see the fate of DVDs or firms such as Blackbury or NOKIA). In principle, such a firm could diversify risk by going long to watermark options.
The applications discussed above justify the introduction of the watermark options as derivative structures. These options can also provide alternatives to existing derivatives that can better fit a range of investors' risk preferences. For instance, the version associated with (5) effectively identifies with the Russian option (see Remark 6). It is straightforward to check that, if s ≥ 1, then the price of the option is increasing as the parameter b increases, ceteris paribus. In this case, the watermark option is cheaper than the corresponding Russian option if b < a. On the other hand, increasing values of the strike price K result in ever lower option prices. We have not attempted any further analysis in this direction because this involves rather lengthy calculations and is beyond the scope of this paper.
The parameters a, b > 0 and K ≥ 0 can be used to fine tune different risk characteristics. For instance, the choice of the relative value b/a can reflect the weight assigned to the underlying asset's historical best performance relative to the asset's current value. In particular, it is worth noting that larger (resp., smaller) values of b/a attenuate (resp., magnify) the payoff's volatility that is due to changes of the underlying asset's price. In the context of the problem with value function given by (5), the choice of a, b can be used to factor in a power utility of the payoff received by the option's holder. Indeed, if we set a =ãq and b =bq, then S b τ /X a τ = Sb τ /Xã τ q is the CRRA utility with risk-aversion parameter 1 − q of the payoff Sb τ /Xã τ received by the option's holder if the option is exercised at time τ . From a modelling point of view, the standard in the mathematical finance literature use of a geometric Brownian motion as an asset price process is an approximation that is largely justified by its tractability. In fact, such a process is not an appropriate model for an asset price that may be traded as a bubble. In view of the applications we have discussed above, the pricing of watermark options when the underlying asset's price process is modelled by diffusions associated with local volatility models that have been considered in the context of financial bubbles (e.g., see Cox and Hobson [CH05]) presents an interesting problem for future research.
The Russian options introduced and studied by Shepp and Shiryaev [SS93,SS94] are the special cases that arise if a = 0 and b = 1 in (5). In fact, the value function given by (5) identifies with the value function of a Russian option for any a, b > 0 (see Remark 6). The lookback-American options with floating strike that were studied by Pedersen [P00] and Dai [D01] are the special cases that arise for the choices a = b = 1 and a = b = K = 1 in (4), respectively (see also Remark 4). Other closely related problems that have been studied in the literature include the well-known perpetual American put options (a = 1, b = 0 in (4)), which were solved by McKean [McK65], the lookback-American options studied by Guo and Shepp [GS01] and Pedersen [P00] (a = 0, b = 1 in (6)), and the π-options introduced and studied by Guo and Zervos [GZ10] (a < 0 and b > 0 in (3)).
Beyond these references, optimal stopping problems involving a one-dimensional diffusion and its running maximum (or minimum) include Jacka [J91] [P14] solves an optimal stopping problem involving a one-dimensional diffusion, its running maximum as well as its running minimum. Optimal stopping problems with an infinite time horizon involving spectrally negative Lévy processes and their running maximum (or minimum) include Ott [O13, O14], Kyprianou and Ott [KO14], and references therein.
In Section 2, we solve the optimal stopping problem whose value function is given by (3) for a = 1 and b ∈ ]0, ∞[ \ {1}. To this end, we construct an appropriately smooth solution to the problem's variational inequality that satisfies the so-called transversality condition, which is a folklore method. In particular, we fully determine the free-boundary function separating the "waiting" region from the "stopping" region as the unique solution to a first-order ODE that has the characteristics of a separatrix. It turns out that this freeboundary function conforms with the maximality principle introduced by Peskir [P98]: it is the maximal solution to the ODE under consideration that does not intersect the diagonal part of the state space's boundary. The asymptotic growth of this free-boundary function is notably different in each of the cases 1 < b and 1 > b, which is a surprising result (see Remark 3).
In Section 3, we use an appropriate change of probability measure to solve the optimal stopping problem whose value function is given by (4) for a = 1 and b ∈ ]0, ∞[ \ {1} by reducing it to the problem studied in Section 2. We also outline how the optimal stopping problem defined by (1)-(3) for a = b = 1 reduces to the the problem given by (1), (2) and (4) for a = b = 1, which is the one arising in the pricing of a perpetual American lookback with floating strike option that has been solved by Pedersen [P00] and Dai [D01] (see Remark 4). We then explain how a simple re-parametrisation reduces the apparently more general optimal stopping problems defined by (1)-(4) for any a > 0, b > 0 to the corresponding cases with a = 1, b > 0 (see Remark 5). Finally, we show that the optimal stopping problem defined by (1), (2) and (5) reduces to the one arising in the context of pricing a perpetual Russian option that has been solved by Shepp and Shiryaev [SS93,SS94] (see Remark 6).
2 The solution to the main optimal stopping problem We now solve the optimal stopping problem defined by (1)-(3) for a = 1 and b = p ∈ ]0, ∞[ \ {1}, namely, the problem defined by (1), (2) and To fix ideas, we assume in what follows that a filtered probability space (Ω, F , (F t ), P) satisfying the usual conditions and carrying a standard one-dimensional (F t )-Brownian motion W has been fixed. We denote by T the set of all (F t )-stopping times. The solution to the optimal stopping problem that we consider involves the general solution to the ODE which is given by for some A, B ∈ R, where the constants m < 0 < n are the solutions to the quadratic equation given by We make the following assumption.
Remark 1 We can check that, given any r > 0, the equivalences m + 1 < 0 ⇔ r + µ > σ 2 and 1 < n ⇔ µ < r hold true. It follows that Assumption 1 holds true for a range of parameter values such that r ≥ µ, which is associated with the applications we have discussed in the introduction, as well as such that r < µ.
We prove the following result in the Appendix.
We will solve the problem we study in this section by constructing a classical solution w to the variational inequality with boundary condition that identifies with the value function v. Given such a solution, we denote by S and W the so-called stopping and waiting regions, which are defined by In particular, we will show that the first hitting time τ S of S, defined by is an optimal stopping time.
To construct the required solution to (12)-(13), we first note that it is not optimal to stop whenever the state process (X, S) takes values in the set On the other hand, the equivalences imply that the set should be a subset of the continuation region W as well. Furthermore, since ∂ ∂s , which is part of the state space's boundary, should also be a subset of W because the boundary condition (13) cannot hold otherwise.
In view of these observations, we look for a strictly increasing function H : where Γ = (n + 1)(m + 1) such that and (see Figure 1).
To proceed further, we recall the fact that the function w(·, s) should satisfy the ODE (7) in the interior of the waiting region W. Since the general solution to (7) is given by (8), we therefore look for functions A and B such that To determine the free-boundary H and the functions A, B, we first note that the boundary condition (13) requires thatȦ (s)s n +Ḃ(s)s m = 0.
In view of the regularity of the optimal stopping problem's reward function, we expect that the so-called "principle of smooth fit" should hold true. Accordingly, we require that w(·, s) should be C 1 along the free-boundary point H(s), for s > 0. This requirement yields the system of equations which is equivalent to Differentiating these expressions with respect to s and substituting the results forȦ andḂ in (20), we can see that H should satisfy the ODĖ where In view of (16), we need to determine the solution to (23) in the domain that is such that H(0) = 0. To this end, we cannot just solve (23) with the initial condition H(0) = 0 because H(0, 0) is not well-defined. Therefore, we need to consider all solutions to (23) in D H and identify the unique one that coincides with the actual free-boundary function H. It turns out that this solution is a separatrix. Indeed, it divides D H into two open domains D u H and D l H such that the solution to (23) that passes though any point in D u H hits the boundary of D H at some finiteŝ, while, the solution to (23) that passes though any point in D l H has asymptotic growth as s → ∞ such that the corresponding solution to the variational inequality (12) does not satisfy the transversality condition that is captured by the limits on the right-hand side of (35) (see also Remark 2 and Figures 2-3).
To identify this separatrix, we fix any δ > 0 and we consider the solution to (23) that is such that where s † (δ) ≥ δ is the intersection of the half-line {(x, s) ∈ R 2 | x = δ and s > 0} with the boundary of D H , which is the unique solution to the equation The following result, which we prove in the Appendix, is primarily concerned with identifying s * > s † such that the solution to (23) that passes through (δ, s * ), namely, satisfies (26), coincides with the separatrix. Using purely analytical techniques, we have not managed to show that this point s * is unique, namely, that there exists a separatrix rather than a funnel. For this reason, we establish the result for an interval [s • , s • ] of possible values for s * such that the corresponding solution to (23) has the required properties. The fact that s • = s • follows immediately from Theorem 4, our main result, thanks to the uniqueness of the optimal stopping problem's value function.
Lemma 2 Suppose that the problem data satisfy Assumption 1. Given any δ > 0, there where s † (δ) ≥ δ is the unique solution to (27) Figure 3).  (26). Any solution to (23) that is as in (a) does not identify with the actual free-boundary function H because the corresponding solution to the variational inequality (12) does not satisfy the boundary condition (13). On the other hand, any solution to (23) that is as in (b) does not identify with the actual free-boundary function H because we can show that its asymptotic growth as s → ∞ is such that the corresponding solution w to the variational inequality (12) does not satisfy (32) and the transversality condition, which is captured by the limits on the right-hand side of (35), is not satisfied. To keep the paper at a reasonable length, we do not expand on any of these issues that are not really needed for our main results thanks to the uniqueness of the value function.
Remark 3 The asymptotic growth of H(s) as s → ∞ takes qualitatively different forms in each of the cases p < 1 and p > 1 (recall that the parameter p stands for the ratio b/a, where the parameters a, b are as in the introduction (see also Remark 5)). Indeed, if we denote by H(·; p) the free-boundary function to indicate its dependence on the parameter p, then where c > 0 is the constant appearing in (I) or (II) of Lemma 2, according to the case. Furthermore, c is proportional to (resp., independent of) K −1 if p < 1 (resp., p > 1).
We now consider a solution H to the ODE (23) that is as in the previous lemma. In the following result, which we prove in the Appendix, we show that the function w defined by is such that and satisfies (12)-(13).
Lemma 3 Suppose that the problem data satisfy Assumption 1. Also, consider any s * ∈ s • (δ), s • (δ) , where s • (δ) ≤ s • (δ) are as in Lemma 2, for some δ > 0, and let H(·) = H(·; s * ) be the corresponding solution to the ODE (23) that satisfies (26). The function w defined by (29) is strictly positive, it satisfies the variational inequality (12) outside the set (x, s) ∈ R 2 | s > 0 and x = H(s) as well as the boundary condition (13), and is such that (30)-(31) hold true. Furthermore, given any s > 0, there exists a constant C = C(s) > 0 such that where We can now prove our main result.
Theorem 4 Consider the optimal stopping problem defined by (1), (2) and (6), and suppose that the problem data satisfy Assumption 1. The optimal stopping problem's value function v identifies with the solution w to the variational inequality (12) with boundary condition (13) described in Lemma 3, and the first hitting time τ S of the stopping region S, which is defined as in (14), is optimal. In particular, Proof. Fix any initial condition (x, s) ∈ S ∪ W. Using Itô's formula, the fact that S increases only in the set {X t = S t } and the boundary condition (13), we can see that It follows that Given a stopping time τ ∈ T and a localising sequence of bounded stopping times (τ j ) for the local martingale M, these calculations imply that In view of the fact that w satisfies the variational inequality (12) and Fatou's lemma, we can see that follows.
To prove the reverse inequality and establish the optimality of τ S , we note that, given any constant T > 0, (33) with τ = τ S ∧ T and the definition (14) of τ S imply that In view of (32), Lemma 6 in the Appendix, the fact that S is an increasing process, and the dominated and monotone convergence theorems, we can see that Combining this result with (34), we obtain the identity v = w and the optimality of τ S . Finally, given any δ > 0, the identity s • (δ) = s • (δ) follows from the uniqueness of the value function v.

Ramifications and connections with the perpetual American lookback with floating strike and Russian options
We now solve the optimal stopping problem defined by (1), (2) and (4) for a = 1 and b = p ∈ ]0, ∞[ \ {1}, namely, the problem given by (1), (2) and by means of an appropriate change of probability measure that reduces it to the one we solved in Section 2. To this end, we denotẽ and we make the following assumption that mirrors Assumption 1.
Theorem 5 Consider the optimal stopping problem defined by (1), (2) and (36) and suppose that the problem data satisfy Assumption 2. The problem's value function is given by u(x, s) = xv(x, s) for all s > 0 and x ∈ ]0, s] and the first hitting time τ S of the stopping region S is optimal, where v is the value function of the optimal stopping problem defined by (1), (2) and (6), given by Theorem 4, and S is defined by (18) withμ,r defined by (37) and the associatedm,ñ in place of µ, r and m, n.
Proof. We are going to establish this result by means of an appropriate change of probability measure. We therefore consider a canonical underlying probability space because the problem we solve is over an infinite time horizon. To this end, we assume that Ω = C(R + ), the space of continuous functions mapping R + into R, and we denote by W the coordinate process on this space, which is given by W t (ω) = ω(t). Also, we denote by (F t ) the right-continuous regularisation of the natural filtration of W , which is defined by F t = ε>0 σ W s , s ∈ [0, t + ε] , and we set F = t≥0 F t . In particular, we note that the right-continuity of (F t ) implies that the first hitting time of any open or closed set by an R d -valued continuous (F t )-adapted process is an (F t )-stopping time (e.g., see Protter [P05, Theorems I.3 and I.4]). Furthermore, we denote by P (resp.,P) the probability measure on (Ω, F ) under which, the process W (resp., the processW defined byW t = −σt + W t ) is a standard (F t )-Brownian motion starting from 0. The measures P andP are locally equivalent, and their density process is given by dP dP where Z is the exponential martingale defined by Given any (F t )-stopping time τ , we use the monotone convergence theorem, the fact that E[Z T | F τ ]1 {τ ≤T } = Z τ 1 {τ ≤T } and the tower property of conditional expectation to calculate and the conclusions of the theorem follow from the fact that dX t =μX t dt + σX t dW t and Theorem 4.
Remark 4 Using a change of probability measure argument such as the one in the proof of the theorem above, we can see that, if p = 1, then the value function defined by (6) admits the expression where X is given by dX t = (µ − σ 2 )X t dt + σX t dW t , X 0 = x > 0, and expectations are computed under an appropriate probability measure P under which W is a standard Brownian motion. This observation reveals that the optimal stopping problem defined by (1), (2) and (6) for p = 1 reduces to the one arising in the pricing of a perpetual American lookback with floating strike option, which has been solved by Pedersen [P00] and Dai [D01].
Remark 5 IfX is the geometric Brownian motion given by then dX a t = 1 2σ 2 a(a − 1) +μa X a t dt +σaX a t dW t ,X a 0 =x a > 0, and, given anyŝ ≥x, In view of these observations, we can see that the solution to the optimal stopping problem defined by (39), (40) and v(x,ŝ) = sup which identifies with the problem (1)-(3) discussed in the introduction, can be immediately derived from the solution to the problem given by (1), (2) and (6). Similarly, we can see that the solution to the optimal stopping problem defined by (39), (40) and which identifies with the problem (1), (2) and (4) discussed in the introduction, can be obtained from the solution to the problem given by (1), (2) and (36). In particular, v(x,ŝ) = v(x a ,ŝ a ) andû(x,ŝ) = u(x a ,ŝ a ), for µ = 1 2σ 2 a(a − 1) +μa, σ =σa and p = b a .
Therefore, having restricted attention to the problems given by (1), (2) and (6) or (36) has not involved any loss of generality.
Remark 6 Consider the geometric Brownian motion X given by (1) and its running maximum S given by (2). IfX is the geometric Brownian motion defined by andS is its running maximum given bỹ S t = max s, max It follows that the value function υ defined by (5) admits the expression υ(x, s) =υ( Using a change of probability measure argument such as the one in the proof of Theorem 5, we can see thatυ where expectations are computed under an appropriate probability measure P under which the dynamics ofX are given by for a standard Brownian motion W . It follows that the optimal stopping problem defined by (1), (2) and (5) reduces to the one arising in the context of pricing a perpetual Russian option, which has been solved by Shepp and Shiryaev [SS93,SS94].

Appendix: Proof of results in Section 2
We need the following result, the proof of which can be found, e.g., in Merhi and Zervos [MZ07, Lemma 1].
Lemma 6 Given any constants T > 0 and ζ ∈ ]0, n[, there exist constants ε 1 , ε 2 > 0 such that Proof of Lemma 1. Suppose first that m + 1 > 0. Since m < 0 is a solution to the quadratic equation (9), this inequality implies that On the other hand, the inequality p − 1 > n and the fact that n > 0 is a solution to the quadratic equation (9) imply that In view of this inequality, we can see that and the proof is complete.
Proof of Lemma 2. Throughout the proof, we fix any δ > 0 and we denote by s † = s † (δ) the unique solution to (27 Given a point s * > s † and the solution H(·; s * ) to (23)-(26) discussed above, we define and with the usual conventions that inf ∅ = ∞ and sup ∅ = −∞, where c > 0 is as in the statement of the lemma, depending on the case. We also denote and we note that In particular, we can see that these equivalences and (42) imply that (m + 1)(n + 1) − nmKh < 0 for all (h, s) ∈ D 1 h , (48) (m + 1)(n + 1)s p−1 − nmKh < 0 for all (h, s) ∈ D 2 h , while, (43) implies the equivalence In view of definitions (44)-(46), the required claims will follow if we prove that To prove that these results are true, we need to differentiate between the two cases of the lemma: although the main ideas are the same the calculations involved are remarkably different (compare Figures 4 and 5).
In the arguments that we develop, the inequalities m + 1 < 0 < n, 0 < p < 1 and c = m + 1 mK ∈ ]0, Γ[, which are relevant to the case we now consider, are worth keeping in mind. In view of the inequalities m(n + 1) − nmKh < (m + 1)(n + 1) − nmKh we can see that where the function s is defined by In particular, this limit and (57) imply that where s {−1} is the inverse function of s. In view of these observations, we can see that as well as that It follows that (51)-(54) are all true.

1/(n−m)
∈ ]0, 1[ that are relevant to the case we now consider. In view of (49) and the inequalities we can see that where the function s is defined by To proceed further, we define Γ 1 = (n + 1)(m + 1) nmK and Γ 2 = 1 K , we note that Γ = Γ 1 ∧ Γ 2 , and we observe that If Γ = Γ 2 , then we can use the inequality (m + 1)(n + 1) − nm < 0, which holds true in this case (see also (17)), to verify that Proof of Lemma 3. We first note that the strict positivity of w follows immediately from its definition in (29) and Lemma 2.(III). To establish (32), we fix any s > 0 and we note that Lemma 2 implies that there exists a point s ≥ s such that for all u ≥ s.
Combining this observation with the fact that H is continuous, we can see that there exists a constant C 1 (s) > 0 such that for all u ≥ s and x ∈ [H(u), u].
In view of these calculations, the definition (29) of w, and the inequalities m + 1 < 0 < n, we can see that there exists a constant C = C(s) > 0 such that (32) holds true. For future reference, we note that the second of the estimates above implies that, given any s > 0, By construction, we will prove that the positive function w is a solution to the variational inequality (12) with boundary condition (13) that satisfies (30)-(31) if we show that and Proof of (71). In view of the assumption that m + 1 < 0 and the fact that m < 0 < n are the solutions to the quadratic equation (9) given by (10), we can see that Combining this inequality and identities with the fact that H(s) < Γs p ≤ (n+1)(m+1) nmK s p for all s > 0 (see (16)-(17)), we calculate and It follows that f (x, s) < 0 for all s > 0 and x ∈ [0, H(s)], and (71) has been established.
A probabilistic representation of g. Before addressing the proof of (72), we first show that, given any stopping time τ ≤ τ S , where τ S is defined by (14), To this end, we assume that (x, s) ∈ W in what follows without loss of generality. Since the function w(·, s) satisfies the ODE (7) in the waiting region W, we can see that Using Itô's formula, (13), the definition of g in (72) and this calculation, we obtain

It follows that
g(x, s) = E e −r(τ ∧T ∧τ j ) g(X τ ∧T ∧τ j , S τ ∧T ∧τ j ) where (τ j ) is a localising sequence of stopping times for the stochastic integral. Combining (32), (70) and the positivity of w with the definition of g in (72) and the fact that S is an increasing process, we can see that On the other hand, (70), the definition of f in (71), (74) and the fact that S is an increasing process imply that there exists a constant C 2 = C 2 (s) > 0 such that These estimates, the fact that γ ∈ ]0, n[, the assumption that p − 1 < n and Lemma 6 imply that In view of these observations and the fact that S is an increasing process, we can pass to the limits as j → ∞ and T → ∞ in (78) using the dominated and the monotone convergence theorems to obtain (76). Proof of (72). We first note that (73) and the definition (65) of Γ 1 imply that Combining these inequalities with (74) and (75), we can see that To proceed further, we use the identity In particular, (72) holds true if (see also (17) and (65)).
To establish (72) if the problem data is such that (84) is not true, we argue by contradiction. In view of (80)-(81) and (83), we therefore assume that K < Γ −1 1 and that there exist strictly positives 1 <s 2 such thats p− Given such anε > 0 fixed, we consider the solution to (1) with initial condition X 0 =s 2 −ε and the running maximum process S given by (2) with initial condition S 0 =s 2 −ε. Also, we definē S t =s 2 ∨ S t , for t ≥ 0, and τ = inf t ≥ 0 | X t = H(s 2 ) ∨ H(S t ) .
Using (76) Figure 3. Furthermore, the intersection of R 2 with the boundary of the domain D 2 h in which we consider solutions to (61) is designated by green.