The Duality Principle for Multidimensional Optional Semimartingales

Preprint

Article

The Duality Principle for Multidimensional Optional Semimartingales

Altmetrics

Downloads

Views

Comments

A peer-reviewed article of this preprint also exists.

Mahdieh Aminian Shahrokhabadi^*

Alexander Melnikov,Andrey Pak

Mahdieh Aminian Shahrokhabadi^*

Alexander Melnikov,Andrey Pak

This version is not peer-reviewed

Submitted:

28 December 2023

Posted:

28 December 2023

You are already at the latest version

Alerts

Abstract

In option pricing, we often deal with options whose payoffs depend on multiple factors such as foreign exchange rates, stocks, etc. Usually, it leads to a knowledge of the joint distributions and complicated integration procedures. The paper develops an alternative approach that converts the option pricing problem to a dual one and presents a solution to the problem in the optional semimartingale setting. The paper contains several examples which illustrate results in terms of the parameters of models and options.

Keywords:

Subject: Computer Science and Mathematics - Applied Mathematics

1. Introduction

In this study, our objective is to build upon the exploration of the duality principle in option pricing for multidimensional optional semimartingales, as introduced in Eberlein et al. (2009). The options under consideration have payoffs dependent on various factors, such as foreign exchange rates, interest rates, and multiple stocks, including examples like swap options and quanto options.

The pricing of these complex options often necessitates a comprehensive understanding of joint probabilities and involves intricate integration procedures. A suggested alternative approach, as outlined in Eberlein et al. (2009), involves transforming the original problem into its dual option pricing problem, rather than directly solving it. The subsequent resolution of this dual problem provides valuable insights into the valuation of options characterized by intricate dependencies and multiple influencing factors.

As stated earlier, the model we employ to describe the evolution of asset price processes is in the context of multidimensional optional semimartingale models. The family of optional semimartingales constitutes a diverse class of stochastic processes that includes cadlag semimartingales as a subset. Generally, such processes lack cadlag modifications, i.e., they are not inherently right-continuous with finite left-limits. Recently, numerous articles have explored optional processes and their relevance in financial and energy markets. For a comprehensive discussion on their application in the energy market, particularly addressing spike-related issues, please refer to Abdelghani et al. (2022).

Applying the outcomes derived from our study, we can establish duality relationships among swap options, quanto options, and standard call and put options within the framework of optional semimartingale models. This revelation carries notable implications, primarily manifesting in a substantial reduction in the computational complexity associated with the valuation of these financial instruments.

By leveraging our findings, we unveil a practical and efficient means of interrelating swap and quanto options with their standard call and put counterparts in optional semimartingale models. This not only enhances the understanding of the intricate connections between these financial instruments but also provides a tangible advantage in terms of computational efficiency when determining their respective valuations.

Extensive literature has delved into the duality principle of option pricing for multidimensional models. Notable contributions include studies by Margrabe (1978); Geman et al. (1995), and Gerber and Shiu (1996) for the Black-Scholes model, Eberlein and Papapantoleon (2005) for examining time-inhomogeneous Levy processes, Fajardo and Mordecki (2006) for Levy processes, and Schröder (2015) is focusing on cadlag semimartingales. In Eberlein et al. (2009) work, Eberlein, Papapantoleon, and Shiryaev establish the predictable characteristics triplet for one-dimensional semimartingales under the dual measure, employing the Esscher change of measure. On the contrary, limited attention has been given to optional semimartingales.

The paper is organized as follows. In Section 2, we will establish crucial definitions and notations that serve as the foundation for understanding the subsequent sections. Additionally, we will review pertinent results regarding the canonical decomposition of optional semimartingales. This includes an exploration of their quadruplet of predictable characteristics, and their the Laplace cumulant process. Section 3 contains details and lemmas tailored for Exponentially Optional Semimartingale models. We included the main result of this work in Section 4, where we present results concerning multidimensional optional semimartingales and the treatment of linear transformations applied to multidimensional semimartingales. In Section 5, we delve into the duality relation for optional processes, specifically examining the European, Margrabe, and quanto options. Alongside this, in Section 6, we provide explicit examples elucidating the application of the duality principles examined in the previous section.

2. Canonical Decomposition of Optional Semimartingales

Let’s begin by defining key terms and notations crucial for understanding the upcoming sections. Specifically, we need to clarify the concept of an unusual probability space. An unusual probability space is represented as

(Ω, F, F = {(F_{t})}_{t \geq 0}, P)

, wherein the natural filtration

F_{t}

is characterized by the absence of both right- and left-continuity. The completeness of the space arises from the fact that

F

incorporates all its P-null sets, and P serves as a completed probability measure. We define

F_{+} = (F_{t^{+}})

, with

F_{t^{+}} = ⋂_{t \leq s} F_{s}

. Now, considering

F^{*} : = F_{+}^{P}

as the standard filtration—both right-continuous and complete—the construction of

F_{+}^{P}

involves enriching

F_{+}

with P-null sets.

Let

R^{d}

represent the d-dimensional Euclidean space. In this space, the scalar product between two vectors u and v, both elements of

R^{d}

, is denoted as

〈 u, v 〉 = u^{'} v

, where

u^{'}

denotes the transpose of the vector (or matrix) u.

Moving forward, the next step involves defining optional processes, which serve as the foundational processes under consideration in this paper. All definitions in this regard are drawn from Abdelghani and Melnikov (2020). To formulate the definition of optional processes, it is essential to establish the concept of an optional

σ

-algebra. In this context, we designate the

σ

-algebra

O (F)

on the interval

[0, \infty) \times Ω

as optional if it is generated by all right-continuous

F_{t}

-adapted processes with left-hand limits.

Definition 1.

A random process

X = (X_{t}), t \in [0, \infty)

, is said to be optional if it is

O (F)

-measurable. Optional processes are progressively measurable, and thus clearly measurable. In general, optional processes have right and left limits but are not necessarily continuous on the right, left, or otherwise.

Additionally, when it comes to optional stochastic processes, we can introduce the following definitions. The left process, denoted as

X_{-} = {(X_{t_{-}})}_{t \geq 0}

, where

X_{0_{-}} = X_{0}

and

X_{t_{-}} = \lim_{s ↑ t} X_{s}

. Similarly, we have the right processes

X_{+} = {(X_{t_{+}})}_{t \geq 0}

. Also, the regular differential process

Δ X = {(Δ X_{t})}_{t \geq 0}

where

Δ X_{t} = X_{t} - X_{t -}

and the forward differential process

Δ^{+} X = {(Δ^{+} X_{t})}_{t \geq 0}

where

Δ^{+} X_{t} = X_{t +} - X_{t}

It is also crucial to understand the classification of stopping times, a topic that is thoroughly discussed in Chapter 2 of Abdelghani and Melnikov (2020). A solid understanding of these definitions is paramount for delving into the subsequent sections. Here, we would like to briefly define stopping (or Markov) times. Then we will restate two important categories of this random time which is important in the rest of the paper.

Definition 2.

Let

(F_{t})

be a filtration on

(Ω, F, P)

. A random variable T on Ω with values in

R_{+} \cup {+ \infty}

is called a stopping time or optional time of the filtration

(F_{t})

if, for all

t \in R_{+}

, the event

{T \leq t}

belongs to

F_{t}

and belongs to the trivial filtration. The set of all stopping times is denoted by

T

Furthermore, Stopping times are also called Markov time. The simplest examples of them are

1_{T \leq t}

and every positive constant.

Definition 3.

T is called a wide sense stopping time if for all t,

{T < t} \in F_{t}

(1)

Also, we can say that T as a wide sense stopping time is optional in the wide sense if and only if it is optional to the family

F_{+}

. Another characterization of wide sense stopping times is, for all

t > 0

T \land t

F_{t}

-measurable.

We also require another classification of these stopping times, specifically, the concept of totally inaccessible stopping times, which will prove beneficial later on.

Definition 4.

T > 0

is totally inaccessible if for every sequence

{(T_{n})}_{n = 1, 2, \dots}

of stopping times that increases to T the event

{\lim_{n ↑ \infty} T_{n} = T, T < \infty}

has zero probability

Understanding the definition of a predictable process and

σ

-algebra is also valuable. We designate the

σ

-algebra

P (F)

Ω \times [0, \infty)

as predictable if it is generated by all left-continuous

F_{t}

-adapted processes with right-hand limits or by sets

{(ω, t) : S (ω) < t \leq T (ω)}

, where

S, T

vary across all Markov times.

Definition 5.

A random process

Y = (Y_{t})

t \in [0, \infty)

, whose trajectories have limits from the right is said to be predictable if

P (F)

-measurable. Furthermore, it is termed strongly predictable if

Y_{t} \in P (F)

and

Y_{t}^{+} \in O (F)

holds for all t.

Predictable processes are also optional, i.e.,

P (F) \subseteq O (F)

. Moreover, the definition of strongly predictable processes implies that for every stopping time T, the random variables

X_{T^{+}} 1_{T < \infty}

and

X_{T^{-}} 1_{T < \infty}

are

F_{T^{+}}

-measurable and

F_{T^{-}}

-measurable, respectively. The set of strongly predictable processes is denoted by

P_{s} (F)

P_{s}

Let’s revisit the definition of optional martingales, highlighting the difference from the definition of regular martingales on

F^{*}

satisfying the usual conditions. For an adapted process

X = (X_{t})

with

t \in R_{+}

, it qualifies as a martingale (or alternatively, a supermartingale or submartingale) concerning

F^{*}

if, for every

t \in R_{+}

X_{t}

is integrable, and the conditional expectation

X_{s} = E [X_{t} | F_{s}^{*}]

(or

X_{s} \geq E [X_{t} | F_{s}^{*}]

X_{s} \leq E [X_{t} | F_{s}^{*}]

) holds almost surely for all

s \leq t

. Referring to the existence and uniqueness of optional martingales in Chapter 5 of Abdelghani and Melnikov (2020), we outline the definitions of optional martingale and optional local martingale here.

Definition 6.

We define

M = {(M_{t})}_{t \in R_{+}}

, as an optional martingale (or alternatively, an optional supermartingale or an optional submartingale) if;

(a): M is an optional process (i.e., $M \in O (F)$ ),
(b): The random variable $M_{T} \cdot 1_{{T < \infty}}$ is integrable for any $T \in T$ ,
(c): There exists an integrable random variable $\hat{M}$ such that $M_{T} = E [\hat{M} | F_{T}]$ (or, $M_{T} \geq E [\hat{M} | F_{T}]$ , $M_{T} \leq E [\hat{M} | F_{T}]$ ) almost surely on ${T < \infty}$ for any $T \in T$ .

Definition 7.

A process

M = {(M_{t})}_{t \geq 0}

is called an optional local martingale if there exists a sequence

(R_{n}, M^{(n)}), n \in N,

where

R_{n}

are wide sense stopping times,

R_{n} ↑ \infty

a.s. and

M^{(n)}

is an optional martingale, such that

M 1_{[0, R_{n}]} = M^{(n)} 1_{[0, R_{n}]}

and the random variable

M_{R_{n +}}

is integrable for any

n \in N

Consider

M^{O} (P)

(

M_{l o c}^{O} (P)

), representing the set of optional (local) martingales in relation to the probability measure P. Let

V

denote the collection of all

F

-adapted processes with a finite variation where the variation for an optional process

A = (A_{t})

, is given by

Var {(A)}_{t} = \sum_{0 \leq s \leq t} | A_{s^{+}} - A_{s} | + \int_{0^{+}}^{t} | d A^{r} | < \infty

(2)

where

A^{r}

is a right-continuous finite-variation process, and the series is absolutely convergent. This discussion lays the groundwork for the exploration of optional semimartingales and their canonical decomposition, which is the next crucial step in this study.

Definition 8.

The stochastic process X is called an optional semimartingale denoted by the set

S

, if

X = X_{0} + M + A,

where

M \in M_{l o c}^{O}, A \in V, A_{0} = M_{0} = 0

and

X_{0}

is an

F_{0}

-measurable finite random variable.

If this decomposition holds with a strongly predictable process A of locally integrable variation, then the optional semimartingale X attains the special designation of a special optional semimartingale, denoted by the set

S_{s}

The canonical and component representation of semimartingales is also essential to our analysis of optional semimartingales. The canonical and component representation of optional semimartingales can be seen as a natural consequence of the decomposition

X = X_{0} + X^{c} + X^{d} + X^{g}

where

X^{c}

is a continuous optional semimartingale with the decomposition

X^{c} = a + m

, where a is continuous and strongly predictable with locally integrable variation, and m is a continuous local martingale. The discrete optional semimartingale parts,

X^{d} = a^{d} + m^{d}

and

X^{g} = a^{g} + m^{g}

, are expressible in terms of some underlying measures of right and left jumps, respectively.

Let’s proceed to define the characteristics of an optional semimartingale. Consider a d-dimensional optional semimartingale denoted as

Y = {(Y_{t})}_{0 \leq t \leq T}

, where

Y = {(Y_{1}, \dots, Y_{d})}^{'}

with the decomposition

Y_{i} = A_{i} + M_{i}

, where

M_{i} \in M_{loc}^{O}

A_{i} \in V

M_{i, 0} = 0

i = 1, \dots, d

. Let

{(T_{n})}_{n \geq 1}

and

{(U_{n})}_{n \geq 1}

be sequences of totally inaccessible stopping times and totally inaccessible wide sense stopping times, respectively. For

(E, E)

, consider the Lusin space where

E = (R^{d} ∖ {0}) \cup {δ^{d}} \cup {δ^{g}}

such that

δ^{d}

, and

δ^{g}

are some supplementary points or are the set of processes with finite variation on any segment

[0, t]

, P-a.s.;

E = B (E)

. On the

σ

-algebra

\tilde{E} = B (E) \times E

, define integer random measures on

(R_{+} \times E, \tilde{E})

\begin{matrix} μ^{r} (Γ) & = \sum_{n \geq 1} 1_{Γ} (T_{n}, β_{T_{n}}^{r}), & μ^{g} (Γ) & = \sum_{n \geq 1} 1_{Γ} (U_{n}, β_{U_{n}}^{g}) \end{matrix}

where

1_{Γ} (\cdot)

is an indicator function of a set

Γ \in \tilde{E},

β_{t}^{r} = Δ Y_{t}

Δ Y_{t} \neq 0

and

β_{t}^{r} = δ

Δ Y_{t} = 0, β_{t}^{g} = Δ^{+} Y_{t}

Δ^{+} Y_{t} \neq 0, β_{t}^{g} = δ

Δ^{+} Y_{t} = 0, t > 0 .

Under the unusual conditions on probability space

(Ω, F, F = {(F_{t})}_{t \geq 0}, P)

, any optional semimartingale Y can be decomposed as follows:

\begin{matrix} Y_{t} = Y_{0} + B_{t} + C_{t} + \int_{] 0, t] \times E} h (x) d (μ^{r} - ν^{r}) + \int_{[0, t [\times E} h (x) d (μ^{g} - ν^{g}) \\ + \int_{] 0, t] \times E} (x - h (x)) d μ^{r} + \int_{[0, t [\times E} (x - h (x)) d μ^{g} \end{matrix}

or in short notation

Y = Y_{0} + B + C + \sum_{j = r, g} [h (x) * (μ^{j} - ν^{j}) + (x - h (x)) * μ^{j}],

(3)

Here,

Y_{0}

F_{0}

-measurable random variable,

B \in V, B_{0} = 0,

and

C \in M_{l o c}^{O}, C_{0} = 0,

and it is continuous. The

ν^{j}

represent the compensators of

μ^{j}

(refer to chapter 7 of Abdelghani and Melnikov (2020) for a detailed definition of compensators). The function

h (x)

is a truncation function, meaning it is a bounded function with compact support that behaves as

h (x) = x

around the origin. As an example, one could choose

h (x) = x 1_{{| x | \leq 1}}

. Additionally,

W * μ

denotes the integral process, and

W * (μ - ν)

denotes the stochastic integral with respect to the compensated random measure

(μ - ν)

The collection of the processes

B, C

and the measures

ν^{r}, ν^{g}

is called the (local) characteristics of the semimartingale Y with respect to the probability measure P. Let us call them a quadruplet and denote them by

Q (Y | P) = (B, C, ν^{r}, ν^{g}) .

(4)

In addition, there exists an increasing predictable process A, predictable processes b, c and two transition kernels

F^{r}

and

F^{g}

from

(Ω \times [0, T], P)

into

(E, E)

such that

\begin{matrix} B & = b \cdot A, B_{t} = \int_{0}^{t} b_{s} d A_{s} \\ C & = c \cdot A, C_{t} = \int_{0}^{t} b_{s} d A_{s} \\ ν^{j} & = F^{j} \otimes A, ν^{j} ([0, t] \times E) = \int_{0}^{t} \int_{E} F_{s}^{j} (d x) d A_{s}, j = r, g \end{matrix}

Every optional semimartingale Y with characteristics

Q (Y | P) = (B, C, ν^{r}, ν^{g})

can be associated with an optional Laplace cumulant process defined by

G (u) = 〈 u, B 〉 + \frac{1}{2} 〈 u, C u 〉 + (e^{〈 u, x 〉} - 1 - 〈 u, h (x) 〉) * ν^{r} + (e^{〈 u, x 〉} - 1 - 〈 u, h (x) 〉) * ν^{g}

(5)

Furthermore, expressing the optional Laplace cumulant process can be achieved using the process A, represented as

G (u) = g (u) \cdot A

with

g (u) = 〈 u, b 〉 + \frac{1}{2} 〈 u, c u 〉 + (e^{〈 u, x 〉} - 1 - 〈 u, h (x) 〉) * F^{r} + (e^{〈 u, x 〉} - 1 - 〈 u, h (x) 〉) * F^{g}

(6)

Another significant distinction of the calculus of optional semimartingales on the unusual probability space, in comparison to regular semimartingales on the usual probability space, is their integration. To conclude this section and emphasize this difference, let us briefly discuss the results presented in Chapter 7 of Abdelghani and Melnikov (2020) concerning integration with respect to optional semimartingales.

The optional stochastic integral with respect to optional semimartingales X is defined in terms of the stochastic integrals with respect to its components A and m as

\begin{matrix} Y_{t} & = \int_{0}^{t} h_{s} d X_{s} = h \circ X_{t} = \int_{0}^{t} h_{s} d A_{s} + \int_{0}^{t} h_{s} d m_{s} \\ = h \circ A_{t} + h \circ m_{t} = h \circ A_{t}^{r} + h \circ A_{t}^{g} + h \circ m_{t}^{r} + h \circ m_{t}^{g}, \end{matrix}

(7)

Where

h \circ A_{t}^{r} = \int_{0^{+}}^{t} h_{s^{-}} d A_{s}^{r}, h \circ A_{t}^{g} = \int_{0}^{t^{-}} h_{s} d A_{s^{+}}^{g},

h \circ m_{t}^{r} = \int_{0^{+}}^{t} h_{s^{-}} d m_{s}^{r}, h \circ m_{t}^{g} = \int_{0}^{t^{-}} h_{s} d m_{s^{+}}^{g},

Moreover, given that

X = X^{r} + X^{g} = X^{c} + X^{d} + X^{g}

, we can express the integral with respect to X as

h \circ X = h \circ X^{r} + h \circ X^{g} = h \circ X^{c} + h \circ X^{d} + h \circ X^{g},

where

X^{r} = A^{r} + m^{r} = A^{c} + m^{c} + A^{d} + m^{d}

and

X^{g} = A^{g} + m^{g}

3. Exponentially Optional semimartingale models

In this section, we delve into specific details and lemmas tailored for Exponentially Optional Semimartingale models. The forthcoming discussions will cover various results, including equivalent statements for exponentially special optional semimartingales, a martingale version of the Lévy–Khintchine formula applicable to optional semimartingales, multiplicative decomposition for optional semimartingale, and a corollary addressing the uniqueness of the exponential optional compensator.

To start this section, we present definitions for a special optional semimartingale and an exponential optional compensator. This initial step aims to show differences between the findings in this section and those in the references Jacod and Shiryaev (1987) and Eberlein et al. (2009).

Definition 9.

An optional semimartingale X is called exponentially special optional semimartingales if

\exp (X - X_{0})

is a special optional semimartingale.

Definition 10.

Let X be an optional semimartingale. A strongly predictable process

V \in V

is called exponential optional compensator of X if

\exp (X - X_{0} - V) \in M_{l o c}^{O} (P) .

In the following lemma, our objective is to reformulate the outcome presented in Corollary II.2.42 of Jacod and Shiryaev (1987) for exponentially special optional semimartingales. This restatement serves as an extension of the original result by Jacob and Shiryaev.

Lemma 1.

Suppose X is an exponentially special optional semimartingale. The following statements are equivalent:

a) X is an optional semimartingale, and it admits the local quadruplet of characteristics

Q (X | P) = (B, C, ν^{r}, ν^{g})

b) For each

z \in R

, the process

e^{z X} - (e^{z X_{-}}) \circ G (z) \in M_{l o c}^{O} .

Proof.

(a) \Rightarrow (b) :

We consider the following decomposition of

X :

X = M + B + \sum_{s \leq t} [Δ X_{s} - h (Δ X_{s})] + \sum_{s < t} [Δ^{+} X_{s} - h (Δ^{+} X_{s})] .

Applying the change of variables formula to

f (x) = e^{z x}

and X, we get

\begin{matrix} e^{z X} - e^{z X_{0}} = & u e^{z X_{-}} \circ B + z e^{z X_{-}} \circ M \\ - \frac{1}{2} z^{2} e^{z X_{-}} \cdot C \\ + e^{z X_{-}} (e^{z x} - 1 - z h (x)) * μ^{r} \\ + e^{z X} (e^{z x} - 1 - z h (x)) * μ^{g} . \end{matrix}

(8)

Let us consider the right-hand side of (8); the second term is an optional local martingale; other terms are optional processes with a finite variation. However, since

e^{z X}

is a special optional semimartingale,

e^{z X_{-}} (e^{z x} - 1 - z h (x)) * μ^{r} + e^{z X} (e^{z x} - 1 - z h (x)) * μ^{g}

is actually a process of locally integrable variation. Thus,

e^{z X_{-}} (e^{z x} - 1 - z h (x)) * (μ^{r} - ν^{r}) + e^{z X} (e^{z x} - 1 - z h (x)) * (μ^{g} - ν^{g}) \in M_{l o c}^{O},

and the result follows.

(b) \Rightarrow (a) :

By hypothesis,

e^{z X}

is an optional semimartingale for each

z \in R .

Then, since

\log (x) \in C^{2}, X

is an optional semimartingale.

Let

(B^{*}, C^{*}, ν^{r *}, ν^{g *})

be a good version of the characteristics of

X .

For each

z \in R

we associate to

(B^{*}, C^{*}, ν^{r *}, ν^{g *})

a process

G^{*} (z) .

We have proved the implication

(a) \Rightarrow (b),

thus

e^{z X} - e^{z X} \circ G^{*} (z) \in M_{l o c}^{O}

. Then the hypothesis and the uniqueness of the canonical decomposition of the special semimartingale

e^{z X}

show that

e^{z X} \circ G^{*} (z) = e^{z X} \circ G (z)

up to an evanescent set. Using the orthogonality of

G^{r}

and

G^{g}

and integrating the processes

e^{- z X_{-}}

and

e^{- z X},

we obtain that

G (z)

and

G^{*} (z)

are indistinguishable. Therefore, the set N of all

ω

for which there exists

z \in Q

and

t \in Q_{+}

with

G {(z)}_{t (ω)} \neq G^{*} {(z)}_{t (ω)}

is P-null.

Now, we observe that the optional cumulant function is continuous and, as a result, completely characterized by its values on

Q .

Consequently, outside of the set

N,

we have

B_{t}^{*} : = B_{t}^{r *} + B_{t}^{g *}, B_{t} : = B^{r} + B^{g}, C_{t}^{*} = C_{t}

and

ν^{j *} ([0, t] \times \cdot) = ν^{j} ([0, t] \times \cdot), j = r, g,

for all

t \in Q_{+}

(see Gnedenko and Kolmogorov (1954)). This relationship also holds for all

t \in R +

due to the right-continuity of

B^{r *}, B^{r}, C^{*}, C, ν^{r *}, ν^{r},

and the left-continuity of

B^{g *}, B^{g}, ν^{g *}, ν^{g} .

Therefore,

(B, C, ν^{r}, ν^{g})

also serves as a version of the local characteristics of X. □

A significant finding in Jacod and Shiryaev (1987) (II.2.48 Corollary) and Eberlein et al. (2009) presents a martingale version of the Lévy–Khintchine formula specifically tailored for regular semimartingales. In this context, our goal is to broaden this result to encompass optional semimartingales.

Corollary 1.

Suppose X is an exponentially special optional semimartingale and

E (G (z)) \neq 0

for all

z \in R

. If for each

z \in R, \frac{e^{z X}}{E (G (z))} \in M_{l o c}^{O}

. Then X is an optional semimartingale with characteristics

(B, C, ν^{r}, ν^{g}) .

Proof.

Denote

M (z) = \frac{e^{z X}}{E (G (z))} \in M_{l o c}^{O} .

Then by Lemma 5.2.3 in Pak (2021) we get

\begin{matrix} e^{z X} = & E (G (z)) M (z) \\ = & e^{z X_{0}} + E (G (z)) \cdot M_{t}^{r} (z) + M_{-} (z) \cdot E_{t}^{r} (G (z)) \\ + E_{+} (G (z)) ⊙ M_{t +}^{g} (z) + M (z) ⊙ E_{t +}^{g} (G (z)) \\ = & e^{z X_{0}} + E (G (z)) \cdot M_{t}^{r} (z) + M_{-} (z) E_{-}^{r} (G (z)) \cdot G_{t}^{r} (z) \\ + E_{+} (G (z)) ⊙ M_{t +}^{g} (z) + M (z) E^{r} (G (z)) ⊙ G_{t +}^{g} (z) \\ = & e^{z X_{0}} + E (G (z)) \cdot M_{t}^{r} (z) + E_{+} (G (z)) ⊙ M_{t +}^{g} (z) \\ + e^{z X} \circ G_{t} (z) \end{matrix}

Note that we have used the definition of

E (G (z))

above and the notation ⊙ for the optional integral with respect to the optional martingale process. It follows from the above that

e^{z X} - e^{z X_{-}} \circ G (z) \in M_{l o c}^{O} .

The result follows from

(b) \Rightarrow (a)

of Lemma 1. □

In addition to the canonical decomposition for an optional semimartingale, we want to establish that every optional semimartingale also allows for a multiplicative decomposition, as demonstrated in the following theorem.

Theorem 1.

Let X be an optional semimartingale with

X_{0} = 1,

such that

X, X_{-}

and

X_{+}

take their values in

(0, \infty) .

Then X admits a multiplicative decomposition

X = L D,

where

L \in M_{l o c}^{O}

L > 0

and

D \in V

is a positive strongly predictable process and

L_{0} = D_{0} = 1,

if and only if X is a special semimartingale.

In this case, the multiplicative decomposition is unique (up to evanescence), and is given as follows, where

X = 1 + M + A

is the canonical (additive) decomposition of

X,

and

H^{r} : = \frac{1}{X_{-} + Δ A}

and

H^{g} : = \frac{1}{X + Δ^{+} A}

are necessarily locally bounded and positive:

L = E (H \circ M), D = \frac{1}{E (- H \circ A)}

(9)

Proof.

If a multiplicative decomposition

X = L D

exists, by Lemma 5.2.3 in Pak (2021) we have

\begin{matrix} X = L D = 1 + \int_{] 0, t]} D_{s} d L_{s}^{r} + \int_{] 0, t]} L_{s -} d D_{s}^{r} + \int_{[0, t [} D_{s +} d L_{s +}^{g} + \int_{[0, t [} L_{s} d D_{s +}^{g} . \end{matrix}

(10)

But

\int_{] 0, t]} D_{s} d L_{s}^{r} + \int_{[0, t [} L_{s} d D_{s +}^{g}

is an optional local martingale and

\int_{] 0, t]} L_{s -} d D_{s}^{r} + \int_{[0, t [} L_{s} d D_{s +}^{g}

is a strongly predictable process with finite variation, thus X is an optional special semimartingale.

Suppose that we have two multiplicative decompositions

X = L D = \hat{L} \hat{D},

for which we can write (10). The uniqueness of the canonical decomposition yields

\int_{] 0, t]} L_{s -} d D_{s}^{r} + \int_{[0, t [} L_{s} d D_{s +}^{g} = \int_{] 0, t]} {\hat{L}}_{s -} d {\hat{D}}_{s}^{r} + \int_{[0, t [} {\hat{L}}_{s} d {\hat{D}}_{s +}^{g} (a . s .) .

Since

D^{r}

and

D^{g}

are orthogonal, we have

\int_{] 0, t]} L_{s -} d D_{s}^{r} = \int_{] 0, t]} {\hat{L}}_{s -} d {\hat{D}}_{s}^{r}, \int_{[0, t [} L_{s} d D_{s +}^{g} = \int_{[0, t [} {\hat{L}}_{s} d {\hat{D}}_{s +}^{g} (a . s .) .

Since

L, \hat{L}, D, \hat{D}

are positive and

L / \hat{L} = \hat{D} / D,

we deduce that

\int_{] 0, t]} 1 / D_{s -} d D_{s}^{r} = \int_{] 0, t]} 1 / {\hat{D}}_{s -} d {\hat{D}}_{s}^{r}

and

\int_{[0, t [} 1 / D_{s} d D_{s +}^{g} = \int_{[0, t [} 1 / {\hat{D}}_{s} d {\hat{D}}_{s +}^{g}

then if

U = \int_{] 0, t]} 1 / D_{s -} d D_{s}^{r} + \int_{[0, t [} 1 / D_{s} d D_{s +}^{g},

we also have

\int_{] 0, t]} 1 / {\hat{D}}_{s -} d {\hat{D}}_{s}^{r} + \int_{[0, t [} 1 / {\hat{D}}_{s} d {\hat{D}}_{s +}^{g} = U,

which means, we have

D = \hat{D} = E (U),

and this proves the uniqueness.

It remains to prove the following: if

X = 1 + M + A

is the canonical decomposition of the optional special semimartingale X (with

X > 0, X_{-} > 0

and

X_{+} > 0

), and if

N = H \circ M

and

B \circ A,

then the strongly predictable process H is both locally bounded and positive. Moreover,

X = L D,

where

L = E (N)

and

D = 1 / E (- B)

(as defined in (9)). Note that N, and L are optional local martingale, while B, and D are strongly predictable with finite variation. First, we observe that

{}^{p}X = {}^{p}A + {}^{p}M = A_{-} + Δ A + M_{-} = X_{-} + Δ A,

where the notation

^{p}

denotes a predictable projection of the process. Then for any predictable stopping time

T,

and since

X > 0,

we get

X_{T -} + Δ A_{T} = E (X_{T} | F_{T}) > 0

a.s. on

{T < \infty} .

Then Theorem 2.4.52 in Abdelghani and Melnikov (2020) yields that

X_{-} + Δ A > 0

outside P-null set, and we deduce

0 < H^{r} < \infty .

Next, notice that

{}^{o}X_{+} = {}^{o}A_{+} + {}^{o}M_{+} = A + Δ^{+} A + M = X + Δ^{+} A,

where ^o denotes an optional projection of the process. Then for any stopping time

T,

and since

X > 0

, we get

X_{T} + Δ^{+} A_{T} = E (X_{T +} | F_{T}) > 0

a.s. on

{T < \infty} .

Then Theorem 2.4.52 in Abdelghani and Melnikov (2020) yields that

X + Δ^{+} A > 0

outside P-null set, and we have we deduce

0 < H^{g} < \infty .

Furthermore, Lemma

H^{r}

and

H^{g}

are locally bounded because they both are strongly predictable. Since we know that H is locally bounded we can define L and D as above, and it remains to prove that

X = L D = E (N) / E (- B) .

Observe that

Δ B = \frac{Δ A}{X_{-} + Δ A} < 1

and

Δ^{+} B = \frac{Δ^{+} A}{X + Δ^{+} A} < 1

identically, hence

E (- B) > 0

and

E {(- B)}_{-} > 0, E {(- B)}_{+} > 0 .

Then we apply the change of variables formula to the function

f (x, y) = x / y,

or rather to a

C^{2}

function coinciding with f for y outside an arbitrary small neighborhood of

0,

to obtain with

D^{*} = 1 / D

and

\hat{X} = L D :

\begin{matrix} {\hat{X}}_{t} = & 1 + {\hat{X}}_{-} \cdot N_{t}^{r} + {\hat{X}}_{-} \cdot B_{t}^{r} + \sum_{s \leq t} (\frac{1 + Δ N_{s}}{1 - Δ B_{s}} - 1 - Δ N_{s} - Δ B_{s}) {\hat{X}}_{s -} \\ + \hat{X} ⊙ N_{t +}^{g} + \hat{X} ⊙ B_{t +}^{g} + \sum_{s < t} (\frac{1 + Δ^{+} N_{s}}{1 - Δ^{+} B_{s}} - 1 - Δ^{+} N_{s} - Δ^{+} B_{s}) {\hat{X}}_{s} \\ = & 1 + {\hat{X}}_{-} \cdot N_{t}^{r} + {\hat{X}}_{-} \cdot B_{t}^{r} + \sum_{s \leq t} {\hat{X}}_{s -} \frac{Δ N_{s} + Δ B_{s}}{1 - Δ B_{s}} Δ B_{s} \\ + \hat{X} ⊙ N_{t +}^{g} + \hat{X} ⊙ B_{t +}^{g} + \sum_{s < t} {\hat{X}}_{s -} \frac{Δ^{+} N_{s} + Δ^{+} B_{s}}{1 - Δ^{+} B_{s}} Δ^{+} B_{s} \\ = & 1 + {\hat{X}}_{-} (1 + \frac{Δ B_{s}}{1 - Δ B_{s}}) \cdot N_{t}^{r} + {\hat{X}}_{-} (1 + \frac{Δ B_{s}}{1 - Δ B_{s}}) \cdot B_{t}^{r} \\ + \hat{X} (1 + \frac{Δ^{+} B_{s}}{1 - Δ^{+} B_{s}}) ⊙ N_{t +}^{g} + \hat{X} (1 + \frac{Δ^{+} B_{s}}{1 - Δ^{+} B_{s}}) ⊙ B_{t +}^{g} \\ = & 1 + \frac{{\hat{X}}_{-} H^{r}}{1 - Δ B} \cdot X_{t}^{r} + \frac{\hat{X} H^{g}}{1 - Δ^{+} B} ⊙ X_{t +}^{g} \\ = & 1 + \frac{{\hat{X}}_{-}}{X_{-}} \cdot X_{t}^{r} + \frac{\hat{X}}{X} ⊙ X_{t +}^{g} . \end{matrix}

Note that

Δ B = H^{r} Δ A, Δ^{+} B = H^{g} Δ^{+} A

and

1 - H^{r} Δ A = H^{r} X_{-}, 1 - H^{g} Δ^{+} A = H^{g} X,

hence

\frac{H^{r}}{1 - Δ B} = \frac{1}{X_{-}}

and

\frac{H^{g}}{1 - Δ^{+} B} = \frac{1}{X} .

Then

\frac{1}{{\hat{X}}_{-}} \cdot {\hat{X}}^{r} + \frac{1}{\hat{X}} ⊙ {\hat{X}}^{g} = \frac{1}{X_{-}} \cdot X^{r} + \frac{1}{X} ⊙ X^{g}

Thus, similarly to how we proved in the beginning, we deduce that

\hat{X} = X .

□

To conclude this section, we present a corollary addressing the uniqueness of the exponential optional compensator.

Corollary 2.

A real-valued optional semimartingale X has a unique (up to indistinguishably) exponential optional compensator V if X is exponentially special optional semimartingales.

Proof.

Suppose that X is exponentially special optional semimartingales. By Theorem 1, there exists a unique positive process

D \in V

such that

D_{0} = 1

and

L : = \frac{\exp (X - X_{0})}{D} \in M_{l o c}^{O} .

Since

\exp (X - X_{0}) > 0

and

\frac{\exp (X_{-} - X_{0})}{D_{-}} = L_{-} < \infty,

we have

D_{-} > 0 .

Therefore,

V : = \log (D) \in V .

□

4. Multidimensional optional dual measures

In this section, our objective is to extend the findings presented in Eberlein et al. (2009) to the case of optional semimartingales. Eberlein, Papapantoleon, and Shiryaev, in their study, provided a characterization for one-dimensional semimartingales. These semimartingales were defined as scalar products of the driving semimartingale and d-dimensional vectors, under a suitable equivalent probability measure. Our goal is to generalize this result for optional semimartingales H, emphasizing its relevance to interest rate modeling through the optional stochastic integral in the Esscher transform.

Let

L (H)

represent the space of predictable integrable processes concerning the semimartingale H. As previously defined in Section 2,

v^{'} \circ H

signifies the optional stochastic integral of

v^{'}

with respect to the optional semimartingale H. Additionally,

\tilde{G} (v)

is used to denote the logarithmic transform of the cumulant process

G (v)

, expressed as

E (G (v)) = \exp (\tilde{G} (v))

Theorem 2.

Let H be an

R^{d}

-valued optional semimartingale with characteristics

Q (H | P) = (B, C, ν^{r}, ν^{g}) .

Let u be a vector in

R^{d} .

Consider an

R^{d}

-valued strongly predictable process

v,

such that

v \in L (H)

and

v^{'} \circ H

is exponentially special optional semimartingale. Define the measure

P_{v}

via the Radon-Nikodym derivative

\frac{d P_{v}}{d P} = \exp (v^{'} \circ H_{T} - \tilde{G} {(v)}_{T}),

assuming that

e^{v^{'} \circ H - \tilde{G} (v)} \in M^{O} (P)

. Then the process

H^{u}

with

H^{u} = 〈 u, H 〉 = u^{'} H

is a 1-dimensional optional semimartingale with characteristics

Q (H^{u} | P_{v}) = (B^{u}, C^{u}, ν^{u r}, ν^{u g})

of the form

\begin{matrix} \begin{matrix} B^{u} = & u^{'} B + u^{'} d C v + \sum_{j = r, g} (h (u^{'} x) \frac{e^{v^{'} x}}{1 + W^{j} (v)} - u^{'} h (x)) * ν^{j}, \\ C^{u} = & u^{'} C u, \\ ν^{u r} (ω & , t, E) = 1_{E} (u^{'} x) \frac{e^{v^{'} x}}{1 + W^{r} (v)} * ν^{r}, \\ ν^{u g} (ω & , t, E) = 1_{E} (u^{'} x) \frac{e^{v^{'} x}}{1 + W^{g} (v)} * ν^{g}, \end{matrix} \end{matrix}

(11)

where

E \in B (R ∖ {0})

, and

W^{j} {(v)}_{t} = \int (e^{v^{'} w} - 1) ν^{j} ({t} \times d w), j = r, g

Proof.

Using Corollary 1 in Section 3, it is sufficient to show that

\frac{e^{z H^{u}}}{E (G^{u} (z))} \in M_{l o c}^{O} (P_{v})

(12)

for all

z \in Z,

where

Z \subseteq R

open, such that

1_{{| y | > 1}} e^{z y} * ν^{u r} \in V, 1_{{| y | > 1}} e^{z y} * ν^{u g} \in V .

(13)

Here

G^{u}

denotes the Laplace cumulant process associated with the characteristics

(B^{u}, C^{u}, ν^{u r}, ν^{u g})

Using Lemma 3.1 in Gasparyan (1988), (12) is equivalent to

Z \frac{e^{z H^{u}}}{E (G^{u} (z))} = \frac{e^{v^{'} \circ H}}{E (G (v))} \frac{e^{z H^{u}}}{E (G^{u} (z))} \in M_{l o c}^{O} (P)

(14)

Furthermore, using (11), condition (13) transforms to

1_{{| u^{'} x | > 1}} e^{{(z u + v)}^{'} x} * ν^{r} \in V, 1_{{| u^{'} x | > 1}} e^{{(z u + v)}^{'} x} * ν^{g} \in V,

(15)

because

W^{r} (v)

and

W^{g} (v)

do not depend on

x .

Now, the exponent of the numerator in (14) is

v^{'} \circ H + z H^{u} = v^{'} \circ H + z u^{'} H = {(v + z u)}^{'} \circ H,

which has the unique exponential optional compensator by Corollary 2 in Section 3

G (v + z u)

under the measure

P,

for the cumulant defined in (5); that is,

\frac{e^{{(v + z u)}^{'} \circ H}}{E (G (v + z u))} \in M_{l o c}^{O} (P) .

(16)

Therefore, to complete the proof, it suffices to show

E (G (v)) E (G^{u} (z)) = E (G (v + z u)) .

(17)

Now, using the multiplication rule for optional stochastic exponential (see Lemma 8.1.2, Abdelghani and Melnikov (2020)) we get

E (G (v)) E (G^{u} (z)) = E (G (v) + G^{u} (z) + [G (v), G^{u} (z)]) .

Hence, (17) reduces to showing that

G (v) + G^{u} (z) + [G (v), G^{u} (z)] = G (v + z u) .

(18)

On the right-hand side of (18), we have

\begin{matrix} \begin{matrix} G (v + z u) = & {(v + z u)}^{'} \cdot B + \frac{1}{2} \int {(v + z u)}^{'} d C (v + z u) \\ + \sum_{j = r, g} (e^{{(v + z u)}^{'} x} - 1 - {(v + z u)}^{'} h (x)) * ν^{j} \end{matrix} \end{matrix}

(19)

Where operator · is the lebesgue integral. On the left-hand side, we have similarly

\begin{matrix} \begin{matrix} G (v) = & v^{'} \cdot B + \frac{1}{2} \int v^{'} d C v + \sum_{j = r, g} (e^{v^{'} x} - 1 - v^{'} h (x)) * ν^{j} . \end{matrix} \end{matrix}

(20)

The second term on the left-hand side of (18), using (11), is

\begin{matrix} \begin{matrix} G^{u} (z) = & z B^{u} + \frac{1}{2} z^{2} C^{u} + \sum_{j = r, g} (e^{z y} - 1 - z h (y)) * ν^{u j} \\ = & z u^{'} B + \int z v^{'} d C u + \sum_{j = r, g} z (h (u^{'} x) \frac{e^{v^{'} x}}{1 + W^{j} (v)} - u^{'} h (x)) * ν^{j} \\ + \frac{1}{2} z^{2} u^{'} C u + \sum_{j = r, g} (e^{z u^{'} x} - 1 - z h (u^{'} x)) \frac{e^{v^{'} x}}{1 + W^{j} (v)} * ν^{j} \\ = & z u^{'} B + \int z v^{'} d C u + \frac{1}{2} z^{2} u^{'} C u \\ + \sum_{j = r, g} (\frac{e^{{(z u + v)}^{'} x} - e^{v^{'} x}}{1 + W^{j} (v)} - z u^{'} h (x)) * ν^{j} . \end{matrix} \end{matrix}

(21)

The last term on the left-hand side of (18) is

[G (v), G^{u} (z)] = \sum_{t \leq \cdot} Δ G {(v)}_{t} Δ G^{u} {(z)}_{t} + \sum_{t < \cdot} Δ^{+} G {(v)}_{t} Δ^{+} G^{u} {(z)}_{t}

(22)

since

G, G^{u}

are optional processes of finite variation. Now, we proceed as follows. First, we show that the drift terms on the left-hand side and right-hand side of (18) are equal. Then we show that the diffusive terms are equal. Finally, we prove that the jump terms on the left-hand side and right-hand side are equal. The drift term and the diffusive term are rather easy to handle; indeed from (20) and (21), we have that the drift term of the left-hand side of (18) is

v^{'} \cdot B + z u^{'} B = (v + z u^{'} B) \cdot B

(23)

Similarly, the diffusive term of the left-hand side of (18) is

\frac{1}{2} (\int v^{'} d C v + \int z v^{'} d C u + \int z u^{'} d C v + z^{2} u^{'} C u) = \frac{1}{2} \int {(v + z u)}^{'} d C (v + z u),

(24)

since the matrix C is symmetric. Hence, both terms agree with the right-hand side.

The jump terms are more difficult to manipulate due to the presence of the fixed times of discontinuity for the optional semimartingale

H,

which entails that the Laplace cumulant process is discontinuous. Regarding the fixed times of discontinuity, we have

\begin{matrix} \begin{matrix} Δ G {(v)}_{t} = & \int (e^{v^{'} x} - 1) ν^{r} ({t} \times d x) = W^{r} {(v)}_{t}, \\ Δ^{+} G {(v)}_{t} = & \int (e^{v^{'} x} - 1) ν^{g} ({t} \times d x) = W^{g} {(v)}_{t}, \end{matrix} \end{matrix}

(25)

and

\begin{matrix} \begin{matrix} Δ G^{u} {(z)}_{t} = & \int (e^{z y} - 1) ν^{u r} ({t} \times d y) \\ = & \int (e^{z u^{'} x} - 1) \frac{e^{v^{'} x}}{1 + W^{r} {(v)}_{t}} ν^{r} ({t} \times d x) \\ = & \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) \frac{1}{1 + W^{r} {(v)}_{t}} ν^{r} ({t} \times d x) . \end{matrix} \end{matrix}

(26)

Similarly,

\begin{matrix} Δ^{+} G^{u} {(z)}_{t} = \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) \frac{1}{1 + W^{g} {(v)}_{t}} ν^{g} ({t} \times d x) . \end{matrix}

(27)

An important observation here is that

W (v)

does not depend on the integrating variable

x .

Hence, we can pull it out of the integration, and get

\begin{matrix} \begin{matrix} Δ G^{u} {(z)}_{t} = & \frac{1}{1 + W^{r} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{r} ({t} \times d x), \\ Δ^{+} G^{u} {(z)}_{t} = & \frac{1}{1 + W^{g} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{g} ({t} \times d x) . \end{matrix} \end{matrix}

(28)

Hence, for the last term in (18), we can calculate further using (28)

\begin{matrix} \begin{matrix} [G (v), G^{u} (z)] = & \sum_{t \leq \cdot} Δ G {(v)}_{t} Δ G^{u} {(z)}_{t} + \sum_{t < \cdot} Δ^{+} G {(v)}_{t} Δ^{+} G^{u} {(z)}_{t} \\ = & \sum_{t \leq \cdot} \frac{W^{r} {(v)}_{t}}{1 + W^{r} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{r} ({t} \times d x) \\ + \sum_{t < \cdot} \frac{W^{g} {(v)}_{t}}{1 + W^{g} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{g} ({t} \times d x) \\ = & \sum_{t \leq \cdot} \frac{1 + W^{r} {(v)}_{t} - 1}{1 + W^{r} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{r} ({t} \times d x) \end{matrix} \end{matrix}

(29)

\begin{matrix} \begin{matrix} + \sum_{t < \cdot} \frac{1 + W^{g} {(v)}_{t} - 1}{1 + W^{g} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{g} ({t} \times d x) \\ = & \sum_{t \leq \cdot} (\int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{r} ({t} \times d x) \\ - \frac{1}{1 + W^{r} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{r} ({t} \times d x)) \\ + \sum_{t < \cdot} (\int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{g} ({t} \times d x) \\ - \frac{1}{1 + W^{g} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x}) ν^{g} ({t} \times d x)) . \end{matrix} \end{matrix}

Now, using the above observations, we can show that the jump terms on the left-hand side and right-hand side of (18) are equal. Indeed, we can express the integrals with respect to the compensators

ν^{r}

and

ν^{g}

as sums in (20) and (21). Then we have that the jump term on the left-hand side of (18) is

\begin{matrix} \begin{matrix} \sum_{t \leq \cdot} \int (e^{v^{'} x} - 1 - v^{'} h (x) + \frac{e^{{(z u + v)}^{'} x} - e^{v^{'} x}}{1 + W^{r} {(v)}_{t}} \\ - z u^{'} h (x) + e^{{(v + z u)}^{'} x} - e^{v^{'} x} \\ - \frac{1}{1 + W^{r} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x})) ν^{r} ({t} \times d x) \\ + \sum_{t \leq \cdot} \int (e^{v^{'} x} - 1 - v^{'} h (x) + \frac{e^{{(z u + v)}^{'} x} - e^{v^{'} x}}{1 + W^{g} {(v)}_{t}} \\ - z u^{'} h (x) + e^{{(v + z u)}^{'} x} - e^{v^{'} x} \\ - \frac{1}{1 + W^{g} {(v)}_{t}} \int (e^{{(v + z u)}^{'} x} - e^{v^{'} x})) ν^{g} ({t} \times d x) \\ = & \sum_{t \leq \cdot} \int (e^{{(v + z u)}^{'} x} - 1 - {(v + z u)}^{'} h (x)) ν^{r} ({t} \times d x) \\ + \sum_{t < \cdot} \int (e^{{(v + z u)}^{'} x} - 1 - {(v + z u)}^{'} h (x)) ν^{g} ({t} \times d x) \\ = & \sum_{t \leq \cdot} (e^{{(v + z u)}^{'} x} - 1 - {(v + z u)}^{'} h (x)) * ν^{r} \\ + (e^{{(v + z u)}^{'} x} - 1 - {(v + z u)}^{'} h (x)) * ν^{g} \end{matrix} \end{matrix}

which equals the corresponding quantity on the right-hand side of (18). The proof is finished. □

5. Duality relations

In this section, we establish an equivalent relation, namely the duality relation, between the prices of Margrabe options, and quanto options with European call and put options in a market characterized by an unusual probability space. This analysis takes into account a portfolio composed of optional processes.

To initiate, we aim to establish the duality relationship between the value of a swap option, specifically a Margrabe option or Spread option, with a payoff of

M_{T} = {(S_{T}^{1} - S_{T}^{2})}^{+}

, and the payoff of European call and put option. It is important to note that we represent the payoff of a European call option at maturity T by

C_{T} = {(S_{T} - K)}^{+}

, where K is the strike price, and for the put option, it is denoted by

P_{T} = {(K - S_{T})}^{+}

Theorem 3.

Assume that the asset price processes

S^{1}

and

S^{2}

are exponential special optional semimartingales and

e^{H^{i}} \in M^{O} (P), i = 1, 2 .

Then we can relate the value of a swap option and a European option via the following equality:

M_{T} = P_{T}^{v} (1, S_{T}^{u}) = C_{T}^{θ} (S_{T}^{ζ}, 1),

(30)

E {(S_{T}^{1} - S_{T}^{2})}^{+} = E_{v} {(1 - S_{T}^{u})}^{+} = E_{θ} {(S_{T}^{ζ} - 1)}^{+},

(31)

where the characteristics

(C^{u}, ν^{u r}, ν^{u g})

and

(C^{ζ}, ν^{ζ r}, ν^{ζ g})

H^{u} = \log S^{u}

and

H^{ζ} = \log S^{ζ},

respectively, are given by Theorem 2 for

v = {(1, 0)}^{'}, u = {(- 1, 1)}^{'},

and

θ = {(0, 1)}^{'}, ζ = {(1, - 1)}^{'} .

Proof.

We will use asset

S^{1}

as the numeraire asset; if we use asset

S^{2}

instead, then we get the duality relationship with a call option. The value of the swap option is

\begin{matrix} M_{T} = E {(S_{T}^{1} - S_{T}^{2})}^{+} = & E [e^{H_{T}^{1}} {(1 - \frac{S_{T}^{2}}{S_{T}^{1}})}^{+}] \\ = & E [e^{v^{'} H_{T}} {(1 - \frac{S_{T}^{2}}{S_{T}^{1}})}^{+}] \end{matrix}

(32)

where

v = {(1, 0)}^{'} .

Moreover,

e^{v^{'} H} \in M^{O} (P)

by assumption. Define a new measure

P_{v}

via the Radon-Nikodym derivative

\frac{d P_{v}}{d P} = e^{v^{'} H_{T}},

then the pricing problem (32) becomes

M_{T} = E {(S_{T}^{1} - S_{T}^{2})}^{+} = E_{v} [{(1 - \frac{S_{T}^{2}}{S_{T}^{1}})}^{+}]

where we define the process

S^{u} = {(S_{t}^{u})}_{0 \leq t \leq T}

via

S_{t}^{u} = \frac{S_{t}^{2}}{S_{t}^{1}} = \frac{e^{H_{t}^{2}}}{e^{H_{t}^{1}}} = e^{u^{'} H_{t}} = e^{H_{t}^{u}}, 0 \leq t \leq T,

for

u = {(- 1, 1)}^{'} .

The triplet of local characteristics of the semimartingale

H^{u}

is given by theorem 2 for

v = {(1, 0)}^{'}

and

u = {(- 1, 1)}^{'} .

Now, applying Lemma 3.1 in Gasparyan (1988), we obtain that

e^{u^{'} H} \in M^{O} (P_{v})

since

e^{u^{'} H} e^{v^{'} H} = e^{H^{2}} \in M^{O} (P)

Therefore, we conclude that

M_{T} = E {(S_{T}^{1} - S_{T}^{2})}^{+} = E_{v} {(1 - S_{T}^{u})}^{+} = P_{T}^{v} (1, S_{T}^{u}) .

Using the same methodology, we derive the proof of equivalency for the Margrabe option and the call option as well. □

Additionally, we demonstrate another duality, this time between a quanto call option, with the payoff

Q_{T}^{C} = S_{T}^{1} {(S_{T}^{2} - K)}^{+}

, and a European call option with payoff as defined before.

Theorem 4.

Assume that the asset price processes

S^{1}

and

S^{2}

are exponential special optional semimartingales and

e^{H^{i}} \in M^{O} (P), i = 1, 2 .

Then we can relate the value of a quanto call option and a European call option via the following duality:

Q_{T}^{C} = C_{T}^{v} (S_{T}^{u}, K)

(33)

E [S_{T}^{1} {(S_{T}^{2} - K)}^{+}] = E_{v} {(S_{T}^{u} - K)}^{+},

(34)

where the characteristics

(C^{u}, ν^{u r}, ν^{u g})

H^{u} = \log S^{u}

are given by Theorem 2 for

v = {(1, 0)}^{'}, u = {(0, 1)}^{'} .

An analogous duality result relates the quanto put option and the European put option.

Proof.

The value of the quanto call option is

\begin{matrix} Q_{T}^{C} = E [S_{T}^{1} {(S_{T}^{2} - K)}^{+}] = & E [e^{H_{T}^{1}} {(S_{T}^{2} - K)}^{+}] \\ = & E [e^{v^{'} H} {(e^{H_{T}^{2}} - K)}^{+}] \\ = & E_{v} [{(e^{H_{T}^{u}} - K)}^{+}], \end{matrix}

where

\frac{d P_{v}}{d P} = e^{v^{'} H_{T}}

for

v = {(1, 0)}^{'}

and

H^{u} = u^{'} H

for

u = {(0, 1)}^{'} .

Hence, the statement follows. □

Additionally, we delve into the Call-Put duality within this market, exploring two relations for these two contracts. The proof for one of them (a) requires utilizing theorem (2) to establish this duality, while the proof for the other relation (b) involves portfolio value and the Black-Scholes model.

Theorem 5.

Assume that the asset price process S is exponential special optional semimartingales and

e^{H} \in M^{O} (P)

. Then we can relate the value of a European call option and a European put option via the following equivalencies:

a) P_{T} (K, S_{T}) = K C_{T}^{v} (S_{T}^{u}, 1 / K)

(35)

where the characteristics

(C^{u}, ν^{u r}, ν^{u g})

H^{u} = \log S^{u}

is given by Theorem 2 for

v = 1, u = - 1

. Furthermore, we can relate the value of a standard put option with volatility σ to a standard call option via the following equality with volatility

- σ

, as follows:

b) P_{T} (K, S_{T}) = C_{T} (- S_{T}, - k), Or P_{T} (K, S_{T}, σ) = C_{T} (- S_{T}, - k, - σ)

(36)

a. The value of a put option is

\begin{matrix} P_{T} (K, S_{T}) = E [{(K - S_{T})}^{+}] = E & [e^{H_{T}} {(K e^{- H_{T}} - 1)}^{+}] \\ = K E & [e^{H_{T}} {(e^{- H_{T}} - 1 / K)}^{+}] \\ = K E & [e^{v H_{T}} {(e^{- H_{T}} - 1 / K)}^{+}] \\ = K E_{v} & [{(e^{- H_{T}} - 1 / K)}^{+}] \end{matrix}

where

\frac{d p_{v}}{d p} = e^{v H_{T}}

for

v = 1

and

H^{u} = u H

for

u = - 1

. Thus, the statement (a) follows. □

b. To prove this duality, we employ a different approach than the previous, specifically utilizing the Black-Scholes model. In the following, we provide detailed proof of the pricing of a European call option and subsequently investigate the pricing of a European put option. We then establish the relationship between these two.

Assume that the market consists of two types of securities B and S and a portfolio

π = (η, ξ)

which is composed of the optional processes

η

and

η . ξ

is the volume of the reference asset B, while

ξ

is the volume of the security S. Suppose

B_{t} > 0

and

S_{t} \geq 0

for all

t \geq 0

and write the ratio process

R_{t} = S_{t} / B_{t}

. Then, the discounted value of the portfolio is

Y_{t} = η_{t} + ξ_{t} R_{t}

which is a real-valued optional semimartingale that had right and left limits. Furthermore, we restrict the portfolio,

π

, to be self-financing that is

d Y_{t} = ξ d R_{t}

where the interest rate is zero. Now let’s consider the augmented Balck-Scholes model with left and right jumps as described in Abdelghani and Melnikov (2020),

\begin{matrix} B_{t} = & B_{0} + \int_{] 0, t]} r B_{s} d s \end{matrix}

(37)

\begin{matrix} S_{t} = & S_{0} + \int_{] 0, t]} S_{s^{-}} (μ d s + σ d W_{s} + a d L_{s}^{r}) + \int_{[0, t [} b S_{s} d L_{s^{+}}^{g} \end{matrix}

(38)

where

L_{t}^{r} = L_{t} - λ t

L_{t}^{g} = - {\bar{L}}_{t^{-}} + γ t

, and r,

μ

σ

, a, and b are constants. W is diffusion term and L and

\bar{L}

are independent Poisson with constant intensity

λ

and

γ

, respectively. Let the initial wealth account for

B_{0}

and the initial price be

S_{0}

. We can write S as

S_{t} = S_{0} E (H)

, where

H_{t} = μ t + σ W_{t} + a (L_{t} - λ t) + b (γ t - {\bar{L}}_{t^{-}})

, with

H_{0} = 0

, and

B_{t} = B_{0} \exp (h_{t})

where

h_{t} = r t

. Furthermore, we are gonna use the stochastic exponential form mentioned in Abdelghani and Melnikov (2020) as follows:

E {(H)}_{t} = \exp [H_{t} - \frac{1}{2} 〈 H^{c}, H^{c} 〉] \prod_{0 < s \leq t} (1 + Δ S_{s}) e^{- Δ S_{s}} \prod_{0 \leq s < t} (1 + Δ^{+} S_{s}) e^{- Δ^{+} S_{s}}

(39)

In this case, the Ratio process is:

\begin{matrix} R_{t} = S_{0} \exp \{(μ - r - \frac{1}{2} (σ^{2} - λ a^{2} - γ b^{2})) t + σ W_{t}\} \times \\ \prod_{0 < s \leq t} [(1 + a Δ L_{t}) e^{- a Δ L_{t}}] \prod_{0 \leq s < t} [(1 - b Δ^{+} {\bar{L}}_{s^{-}}) e^{- b Δ^{+} {\bar{L}}_{s^{-}}}] \end{matrix}

and is not a local optional martingale. So we need to find a suitable derivative to transfer this ratio under a martingale measure Q such that

Y_{t}

is a local optional martingale with respect to Q (see Melnikov and Shiryaev (1996) and for further explanation, you can refer to Abdelghani and Melnikov (2020) chapter 9). In the following, we repeat some of the key concepts. So we want the derivative

Z = E (N)

for which we have to find a local martingale N such that

Ψ (h, H, N)

\begin{matrix} Ψ (h, H, N) = & N_{t} + H_{t} - h_{t} + {〈 h^{c} - N^{c}, h^{c} - H^{c} 〉}_{t} \\ + & \sum_{0 < s \leq t} \frac{(Δ h_{s} - Δ N_{s}) (Δ h_{s} - Δ H_{s})}{1 + Δ h_{s}} \\ + & \sum_{0 < s \leq t} \frac{(Δ^{+} h_{s} - Δ^{+} N_{s}) (Δ^{+} h_{s} - Δ^{+} H_{s})}{1 + Δ^{+} h_{s}} \end{matrix}

is a local martingale. Thus, for the case of European options, chapter 9 of Abdelghani and Melnikov (2020) takes

N_{t} = ς W_{t} + c (L_{t} - λ t) + d (γ t - {\bar{L}}_{t^{-}})

which is an optional local martingale that will render Z as an optional scaling factor. By this choice of N we get the following:

\begin{matrix} \begin{matrix} Ψ (h, H, N) = & (ς + σ) W_{t} + (a + c + a c) (L_{t} - λ t) \\ + & (b + d - b d) (γ t - {\bar{L}}_{t^{-}}) \\ + & (μ - r + ς σ + 2 a c λ + 2 b d γ) t \end{matrix} \end{matrix}

(40)

is local martingale if

μ - r + ς σ + 2 a c λ + 2 b d γ = 0

(41)

Thus we have to find

(ς, σ, d)

such that (41) is satisfied. We find the martingale measure Q by solving this equation which has infinitely many solutions that mean the market of Black-Scholes with left and right jumps is incomplete.

Now let’s turn our attention to the problem at hand, pricing a European call option. As outlined in chapter 9, section 6 of Abdelghani and Melnikov (2020) with the following choice of parameters for equation (41):

\{c = \frac{- a}{1 + a}, d = \frac{- b}{1 - b}, ς = \frac{r - μ + 2 λ \frac{a^{2}}{1 + a} + 2 γ \frac{b^{2}}{1 + b}}{σ}\}

(42)

leads to the normalized price of R under Q that is:

\begin{matrix} \begin{matrix} R_{t} = & R_{0} E (Ψ (h, H, N)) \\ = & R_{0} E ((ς + σ) W_{t}) \\ = & S_{0} \exp {(ς + σ) W_{t} - \frac{1}{2} {(ς + σ)}^{2} t}, \\ B_{0} = & 1 \end{matrix} \end{matrix}

(43)

It is just a function of the Wiener process and, all the left jumps are absorbed. Thus,

R_{t}

Q -

martingale. Thus,

E_{Q} [R_{T} | F_{t}] = R_{t} = e^{- r t} B_{t}

(44)

As calculated in Abdelghani and Melnikov (2020), with this choice of parameters, the price of a call option at arbitrary time

0 \leq t < T

is:

C_{t} = e^{r t} R_{t} Φ ({\tilde{K}}_{t} + (ς + σ) \sqrt{T - t}) + e^{- r (T - t)} K Φ ({\tilde{K}}_{t})

(45)

where:

\begin{matrix} \tilde{K} = \frac{1}{(ς + σ) \sqrt{T}} [\ln (\frac{S_{0}}{K}) + (r - \frac{1}{2} {(ς + σ)}^{2}) T] \end{matrix}

(46)

Now, we proceed with the pricing of a European Put Option for optional processes. The payoff function of a European put option can be written as:

P_{T} = {(K - S_{T})}^{+} = {(S_{T} - K)}^{+} + K - S_{T}

(47)

Using the payoff of the call option, we can write:

\begin{matrix} \begin{matrix} P_{t} = & E_{Q} [P_{T} | F_{t}] \\ = & E_{Q} [{(S_{T} - K)}^{+} | F_{t}] + E_{Q} [k - S_{T} | F_{t}] \\ = & C_{t} + E_{Q} [k - S_{T} | F_{t}] \end{matrix} \end{matrix}

(48)

Where

C_{t}

is the price of the call option. Now if we denote:

{\tilde{P}}_{T} = \frac{P_{T}}{B_{T}}

(49)

We have:

\begin{matrix} \begin{matrix} {\tilde{P}}_{t} = & E_{Q} [\frac{P_{T}}{B_{T}} | F_{t}] \\ = & E_{Q} [\frac{C_{T}}{B_{T}} | F_{t}] + E_{Q} [\frac{K - S_{T}}{B_{T}} | F_{t}] \\ = & {\tilde{C}}_{t} + K e^{- r T} - E_{Q} [R_{T} | F_{t}] \end{matrix} \end{matrix}

(50)

To compute

E_{Q} [R_{T} | F_{t}]

, we use the same martingale measure as it is used for pricing the European call option. Thus, by (44) We can rewrite (50) using (49) as follows:

\begin{matrix} \begin{matrix} {\tilde{P}}_{t} = & e^{- r t} P_{t} \\ = & e^{- r t} C_{t} + K e^{- r T} - e^{- r t} S_{t} \end{matrix} \end{matrix}

(51)

By simplifying we get the following relation which is called Call-Put Parity.

P_{t} = C_{t} + K e^{- r (T - t)} - S_{t}

(52)

By using this Call-Put Parity relation, we can calculate the fair price of the put option and also establish the duality relation. Using the fact that

Φ (- x) = 1 - Φ (x)

, we have:

\begin{matrix} P_{0} = & C_{0} + K e^{- r T} - S_{0} \\ = & S_{0} Φ (\tilde{K} + (ς + σ) \sqrt{T}) - e^{- r T} K Φ (\tilde{K}) + K e^{- r T} - S_{0} \\ = & S_{0} [Φ (\tilde{K} + (ς + σ) \sqrt{T}) - 1] - e^{- r T} (- K) (Φ (\tilde{K}) - 1) \\ = & (- S_{0}) Φ (- \tilde{K} - (ς + σ) \sqrt{T}) - e^{- r T} (- K) Φ (- \tilde{K}) \end{matrix}

Therefore, using (46) definition, we have:

\begin{matrix} - \tilde{K} = \frac{1}{- (ς + σ) \sqrt{T}} [\ln (\frac{- S_{0}}{- K}) + (r - \frac{1}{2} {(- ς - σ)}^{2}) T] \end{matrix}

(53)

Now given the definition of

ς

in (42), we can see that:

- ς = \frac{r - μ + 2 λ \frac{a^{2}}{1 + a} + 2 γ \frac{b^{2}}{1 + b}}{- σ}

is achieved simply by using

- σ

thus we established the following relation which is called Call-Put Dulaity.

P_{0} (S_{0}, K, σ) = C_{0} (- S_{0}, - K, - σ)

(54)

And for arbitrary time

0 \leq t < T

, we have:

\begin{matrix} P_{t} = & C_{t} + K e^{- r (T - t)} - S_{t} \\ = & r^{r t} R_{t} Φ ({\tilde{K}}_{t} + (ς + σ) \sqrt{T - t}) - e^{- r (T - t)} K Φ ({\tilde{K}}_{t}) + K e^{- r (T - t)} - S_{t} \\ = & (- S_{t}) Φ (- {\tilde{K}}_{t} - (ς + σ) \sqrt{T - t}) - e^{- r (T - t)} (- K) Φ (- {\tilde{K}}_{t}) \end{matrix}

where:

\begin{matrix} {\tilde{K}}_{t} = \frac{1}{(ς + σ) \sqrt{T - t}} [\ln (\frac{S_{t}}{K}) + (r - \frac{1}{2} {(ς + σ)}^{2}) (T - t)] \end{matrix}

(55)

Thus, the Call-Put Duality for arbitrary time

0 \leq t < T

is:

P_{t} (S_{t}, K, σ) = C_{t} (- S_{t}, - K, - σ)

(56)

Upon examining the duality relation in chapter 4 of the Melnikov (2011) and drawing a parallel, it becomes apparent that the (56) relation bears similarity to the duality relation involving cadlag processes. □

6. Applications and Examples

In this section, we proceed with concrete examples and explicit calculations for the previously obtained results in Section 5. The first example delves into an optional version of the well-known Merton’s jump-diffusion model and calculate the price of a quanto call option.

Example 1.

Let’s consider an optional version of the well-known Merton’s jump-diffusion model. The two assets

S^{1}

and

S^{2}

are modeled as optional exponential jump-diffusion processes:

S_{t}^{i} = \exp (b_{i} t + W_{t}^{i} + \sum_{k = 0}^{N_{t}^{i, r}} U_{k}^{i} + \sum_{k = 0}^{N_{t}^{i, g}} Z_{k}^{i}), i = 1, 2,

(57)

where the drift terms

b^{i}

are determined by martingale condition; a Wiener process

W_{t}^{i}

have a variance

σ_{i} \geq 0

and a correlation coefficient

- 1 \leq ρ \leq 1,

i.e.

〈 W^{1}, W^{2} 〉 = ρ

and

c = (\begin{matrix} σ_{1}^{2} & ρ σ_{1} σ_{2} \\ ρ σ_{1} σ_{2} & σ_{2}^{2} \end{matrix});

the process

N_{t}^{i, r}, i = 1, 2

is a right-continuous Poisson process with intensity

λ_{i}^{r}

while the process

N_{t}^{i, r}, i = 1, 2

is a left-continuous version of a Poisson process with intensity

λ_{i}^{g},

the random variables

U_{k}^{i} \sim N o r m a l (s_{i}^{r}, 0)

and

Z_{k}^{i} \sim N o r m a l (s_{i}^{g}, 0) .

The Poisson processes

N_{t}^{i, r}, i = 1, 2

and

N_{t}^{i, g}, i = 1, 2

are independent. Thus, the local characteristics of

H = (H^{1}, H^{2})

are

b = (\begin{matrix} b_{1} \\ b_{2} \end{matrix}); c = (\begin{matrix} σ_{1}^{2} & ρ σ_{1} σ_{2} \\ ρ σ_{1} σ_{2} & σ_{2}^{2} \end{matrix});

(58)

and

F^{r}

and

F^{g}

have the Lebesgue densities

f^{r}

and

f^{g}

where

f^{j} (x_{1}, x_{2}) = \frac{1}{s_{1}^{j} s_{2}^{j} 2 π} \exp (- \frac{x_{1}^{2}}{2 {(s_{1}^{j})}^{2}} - \frac{x_{2}^{2}}{2 {(s_{2}^{j})}^{2}}) λ_{1}^{j} λ_{2}^{j}, j = r, g .

(59)

Now, the price of a quanto call option with strike price K, according to Theorem 4, is equal to the price of a call option with the same strike

K,

on an asset

S^{u}

with characteristics

(B^{u}, C^{u}, ν^{u r}, ν^{u g})

provided by Theorem 2 for

v = {(1, 0)}^{'}

and

u = {(0, 1)}^{'} .

Hence, we can calculate

\begin{matrix} b^{u} = & b_{2} + ρ σ_{1} σ_{2}, \end{matrix}

(60)

\begin{matrix} c^{u} = & σ_{2}^{2}; \end{matrix}

(61)

for the Levy measures, using the independence of the normal variables and completing the square, we have for

y \in R, E \in B (R ∖ 0) :

\begin{matrix} 1_{E} (y) * F^{u j} = & 1_{E} (x_{2}) e^{x_{1}} * F \\ = & \int_{R^{2}} 1_{E} (x_{2}) e^{x_{1}} \frac{1}{s_{1}^{j} s_{2}^{j} 2 π} \exp (- \frac{x_{1}^{2}}{2 {(s_{1}^{j})}^{2}} - \frac{x_{2}^{2}}{2 {(s_{2}^{j})}^{2}}) λ_{1}^{j} λ_{2}^{j} d x_{1} d x_{2} \\ = & λ_{2}^{j} \int_{E} \frac{1}{s_{2}^{j} \sqrt{2 π}} \exp (- \frac{x_{2}^{2}}{2 {(s_{2}^{j})}^{2}}) d x_{2} \\ \times λ_{1}^{j} e^{{(s_{1}^{j})}^{2} / 2} \int_{R} \frac{1}{s_{1}^{j} \sqrt{2 π}} \exp (- \frac{{(x_{1} - {(s_{1}^{j})}^{2})}^{2}}{2 {(s_{1}^{j})}^{2}}) d x_{1} \\ = & λ_{2}^{j} λ_{1}^{j} e^{{(s_{1}^{j})}^{2} / 2} \int_{E} \frac{1}{s_{2}^{j} \sqrt{2 π}} \exp (- \frac{x_{2}^{2}}{2 {(s_{2}^{j})}^{2}}) d x_{2}, j = r, g . \end{matrix}

(62)

Therefore, pricing a quanto option in case the two assets follow the optional version of Merton’s jump-diffusion model, is equivalent to pricing a call option in a univariate jump-diffusion model of the same class, with parameters given by (60)-(62). In particular, left jumps occur according to a compound Poisson process with intensity

λ^{u r} = λ_{2}^{r} λ_{1}^{r} e^{{(s_{1}^{r})}^{2} / 2}

, and jump heights are normally distributed with jump variance

s_{2}^{r}

and zero mean, while right jumps occur according to a left-continuous compound Poisson process with intensity

λ^{u g} = λ_{2}^{g} λ_{1}^{g} e^{{(s_{1}^{g})}^{2} / 2},

and jump heights are normally distributed with jump variance

s_{2}^{g}

and zero mean.

Moreover, the second example extends the previous model by incorporating exponential jumps and proceeds to calculate the price of a quanto call option.

Example 2.

Let us now consider the previous example in a case when the random variables

U_{k}^{i}

and

Z_{k}^{i}

are mutually independent and exponentially distributed with parameters

α^{i}

and

β^{i}

respectively. Thus, the density functions

f^{r}

and

f^{g}

have the following forms

\begin{matrix} f^{r} (x_{1}, x_{2}) = α^{1} α^{2} e^{- α^{1} x_{1} - α^{2} x_{2}}, \\ f^{g} (x_{1}, x_{2}) = β^{1} β^{2} e^{- β^{1} x_{1} - β^{2} x_{2}} . \end{matrix}

Now, the price of a quanto call option with strike

K,

according to Theorem 4, is equal to the price of a call option with the same strike

K,

on an asset

S^{u}

with characteristics

(B^{u}, C^{u}, ν^{u r}, ν^{u g})

provided by Theorem 2 for

v = {(1, 0)}^{'}

and

u = {(0, 1)}^{'} .

Consequently, in this case

b^{u}

and

c^{u}

are the same as in Example 1, and we obtain for

y \in R, E \in B (R ∖ {0}) :

\begin{matrix} 1_{E} (y) * F^{u r} = & λ_{1}^{r} λ_{2}^{r} \int_{R^{2}} 1_{E} (x_{2}) e^{x_{1}} α_{1} α_{2} e^{- α_{1} x_{1} - α_{2} x_{2} d x_{1} d x_{2}} \\ = & λ_{1}^{r} λ_{2}^{r} α_{1} α_{2} \int_{R} e^{x_{1} (1 - α_{1})} d x_{1} \int_{E} e^{- α_{2} x_{2}} d x_{2} \\ = & λ_{1}^{r} λ_{2}^{r} \frac{α_{1} α_{2}}{α_{1} - 1} \int_{E} e^{- α_{2} x_{2}} d x_{2} . \end{matrix}

The levy measure for left jumps is calculated in a similar way. As a result, pricing a quanto option in case of two assets are modeled as optional jump-diffusion’s with exponential jumps, is equivalent to pricing a call option in a univariate optional jump-diffusion model of the same class. In particular, left jumps occur according to a compound Poisson process with intensity

λ^{u r} = λ_{2}^{r} λ_{1}^{r} \frac{α_{1}}{α_{1} - 1}

, and jump heights are exponentially distributed with a parameter

α_{2}

, while right jumps occur according to a left-continuous compound Poisson process with intensity

λ^{u g} = λ_{2}^{g} λ_{1}^{g} \frac{β_{1}}{β_{1} - 1},

and jump heights are exponentially distributed with a parameter

β_{2}

Author Contributions

All authors contributed equally to this study and they have read and agreed to the published version of the manuscript.

Funding

The research was supported by the NSERC Discovery Grant RES0043487.

Conflicts of Interest

The authors declare no conflict of interest.

References

Dellacherie, C. Deux remarques sur la separabilite optionelle. Springer-Verlag: Sem. Probabilites XI Lecture Notes in Math. 1975, 581, 47–50.
Abdelghani, M. N.; Melnikov, A.On linear stochastic equations of optional semimartingales and their applications. Elsevier: Statistics & Probability Letters 2017, 125, 207–214.
Meyer, P. A. Probability and Potentials, Volume 1318; Blaisdell Publishing Company, 1966.
Abdelghani, M., and A. Melnikov. Optional Processes; by Chapman & Hall/CRC, 2020.
Galchuk, L.I. Stochastic Integrals with Respect to Optional Semimartingales and Random Measures. In Theory of Probability and its Applications XXIX 1985, 1, 93-108.
Abdelghani, M. N., and A. V. Melnikov. Financial Markets in the Context of the General Theory of Optional Processes. In Mathematical and Computational Approaches in Advancing Modern Science and Engineering; Springer, 2016; pp. 519–528.
Galchuk, L. I. Optional Martingales. In : Matematicheskii Sbornik by Russian Academy of Sciences, Steklov Mathematical Institute of Russian 1980, Volume 154(4), 483–521.
Melnikov, A. Risk Analysis in Finance and Insurance. by Chapman and Hall/CRC, 2011.
Melnikov, A., and A. Shiryaev. Criteria for the Absence of Arbitrage in the Financial Market.. In Frontiers in Pure and Appl. Probab. II, TVP, Moscow, 1996, pp. 121-134.
Eberlein, E., A. Papapantoleon, and A.N. Shiryaev. Esscher transform and the duality principle for multidimensional semimartingales. In Institute of Mathematical Statistics: The Annals of Applied Probability2009, 19(5), 1944–1971. [CrossRef]
Margrabe,W. The Value of an Option to Exchange One Asset for Another. In The Journal of Finance: American Finance Association, Wiley 1978, 33(1), 177–186. [CrossRef]
Geman, H.; El Karoui, N.; Rochet, J.C. Changes of Numéraire, Changes of Probability Measure and Option Pricing. In Journal of Applied Probability:Applied Probability Trust, 1995, 32(2), 443–458. [CrossRef]
Gerber, H.U., and H.S.W. Shiu. Martingale Approach To Pricing Perpetual American Options On Two Stocks. In Mathematical Finance, 1996, Volume 6(3), 303-322. [CrossRef]
Eberlein, E., and A. Papapantoleon. Symmetries and Pricing of Exotic Options in Lévy Models. In Exotic Option Pricing and Advanced Lévy Models, 2005, pp. 99-128.
Fajardo, J., and E. Mordecki. Pricing Derivatives on Two-Dimensional Lévy Processes. In International Journal of Theoretical and Applied Finance (IJTAF), 2006, Volume 9, 185-197. [CrossRef]
Schröder, M. Changes of Numeraire for Pricing Futures, Forwards, and Options. In The Review of Financial Studies, 2015, Vilume 12(5), 1143-1163, ISSN: 0893-9454. [CrossRef]
Gasparyan, K. B. Some Results on Optional Martingales. Girsanov Theorem. In Mathematical Problems of Computer Science: Academy of Sci.ArmSSR, 1988, Volume 15, 199-210.
Pak, A. Optional Processes and Their Applications. In Mathematical Finance, Risk Theory, and Statistics, 2021.
Abdelghani, M., A. Melnikov, and A. Pak. On Comparison Theorem for Optional Stochastic Differential Equations via Local Times and Applications. In Stochastics by Taylor & Francis, 2022, Volume 94(3), 365-385. [CrossRef]
Gnedenko, B.V., and A.N. Kolmogorov. Limit Distributions for Sums of Independent. In American Journal of Mathematics, 1954, Volume 105.
Jacod, J., Shiryaev, A. N. Limit Theorems for Stochastic Processes, 1987, https://api.semanticscholar.org/CorpusID:122222429.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

MDPI Initiatives

Important Links

Choose an area of interest and we will send you notifications of new preprints at your preferred frequency.

Disclaimer

The Duality Principle for Multidimensional Optional Semimartingales

Abstract

1. Introduction

2. Canonical Decomposition of Optional Semimartingales

3. Exponentially Optional semimartingale models

4. Multidimensional optional dual measures

5. Duality relations

6. Applications and Examples

Author Contributions

Funding

Conflicts of Interest

References

MDPI Initiatives

Important Links

Subscribe