# Pricing of American Put Option under a Jump Diffusion Process with Stochastic Volatility in an Incomplete Market

Authors: Shuang Li; Yanli Zhou; Xinfeng Ruan; B. Wiwatanapataphee
Journal: Abstract and Applied Analysis
 (2014)

---

## Abstract

We study the pricing of American options in an incomplete market in which the dynamics of the underlying risky asset is driven by a jump diffusion process with stochastic volatility. By employing a risk-minimization criterion, we obtain the Radon-Nikodym derivative for the minimal martingale measure and consequently a linear complementarity problem (LCP) for American option price. An iterative method is then established to solve the LCP problem for American put option price. Our numerical results show that the model and numerical scheme are robust in capturing the feature of incomplete finance market, particularly the influence of market volatility on the price of American options.


---

## Body

## 1. Introduction

Since the pioneering work of Black-Scholes [1] and Merton [2] on the pricing of options, option pricing has become one of the predominant concerns in financial market. By relaxing some of the restrictive assumptions, such as the assumption of constant volatility and the geometric Brownian motion for the price of the underlying asset, many option pricing models have been proposed. Today, an increasing amount of literature has focused on stochastic volatility models (see, e.g., [3]), jump diffusion or Lévy process models [4, 5], which fit some features of the observed empirical market data. In this paper, in constructing our price model, we will take account of both the feature of stochastic volatility as in Heston's model and the feature of jump diffusion as in Bates [6]. The combination of the stochastic volatility and jump diffusion in the same model enables the model to capture the nature of the real financial market and yields a challenging and difficult mathematical problem for analysis.Because of infrequent trading or by the presence of additional sources of uncertainty, the completeness of the market often breaks down. Thus, replicating the payoff of a contingent claim by a portfolio is possible, and various equivalent martingale measures (EMM) have been adopted. The question is that, among the equivalent martingale measures, one needs to establish certain criterions to determine the appropriate measure to be adopted. A unique equivalent martingale measure by using utility maximization has been identified by many authors including Kallsen [7], Bielecki and Jeanblanc [8], and Cvitanić et al. [9]. It was also found that, under the minimal martingale measure, there exists a unique risk-minimizing strategy hedging of contingent claims in complete market [10]. The criterion under the minimal martingale measure is thus referred to as risk-minimization criterion (see [11]). According to this, it is possible to price option with risk-minimization criterion. In this paper, we use the minimal martingale measure and consider the switch of assets prices with jump diffusion and stochastic volatility and obtain the Radon-Nikodym derivative and a linear complementarity problem for the pricing of American option.Unlike European options, American options, which can be exercised any time before the expiring date, have to be priced usually numerically. Comparing to European options which lead to a partial integrodifferential equation, American options lead to inequalities in the form of a linear complementarity problem (LCP). In the American option pricing context, several methods have been proposed to approximate the linear complementarity problems resulting from American option pricing. These include the finite difference method, the operator splitting method, the multigrid method, and the penalty method [12]. Moreover, Tavella and Randall [13] described a stationary iterative method for pricing European options, and then Salmi and Toivanen [14] proposed a generalization of this iterative method to price American option. But they did not consider any multifactor jump diffusion models such as stochastic volatility models with jumps. Our paper aims to further develop previous numerical method to deal with multifactor jump diffusion models via the employment of the risk-minimization criterion to obtain the Radon-Nikodym derivative for the minimal martingale measure.The rest of the paper is organized as follows. In Section2, we discuss the pricing model with stochastic volatility and jump diffusion under the minimal martingale measure and then derive the linear complementarity problem under the risk-minimization criterion. Section 3 presents the discrete space and time approximation for the LCP problem. The method of projected successive overrelaxation (projected SOR) is used to yield our results. Numerical results and discussion are reported in Section 4.


## 2. The Model under Minimal Martingale Measure

Taking into account the jumps in the evolution process of the underlying asset, we introduce the following stochastic volatility (1) based on the work in Heston [3]:
(1)
d
X
t
X
t
-
=
μ
d
t
+
V
t
d
W
t
1
+
∫
R
‍
(
y
-
1
)
N
~
(
d
t
,
d
y
)
,
d
V
t
=
α
(
φ
-
V
t
)
d
t
+
σ
V
t
d
W
t
2
,
where W
t
=
(
W
t
1
,
W
t
2
) is defined on the filtered complete space (
Ω
,
ℱ
,
{
ℱ
t
}
t
≥
0
,
P
) with 2-dimensional standard Brownian motion with correlation coefficient d
W
t
1
d
W
t
2
=
ρ
d
t
,
ρ
∈
[
-
1
,
1
]. The asset price has drift μ and N
-
(
d
t
,
d
y
)
=
N
(
d
t
,
d
y
)
-
v
(
d
y
)
d
t is the compensated jump measure, N
(
d
t
,
d
y
) denotes the jump measure, and v
(
d
y
) is the Lévy measure, σ governs the volatility of the variance process, and α and φ are the mean reversion coefficients to ensure that the variance will drift back to φ at the rate of α.According to the discussions in the reference by Föllmer and Schweizer [10], the optimal hedging strategy may be established with the minimal martingale measure and it has been proved that the minimal martingale measure can be determined uniquely. Using Theorem 3.1 in Heston [3], the Radon-Nikodym derivative under the minimal martingale measure q is
(2)
Z
t
=
exp
⁡
(
-
∫
0
t
‍
θ
u
V
u
d
W
u
1
-
1
2
∫
0
t
‍
θ
u
2
V
u
d
u
+
∫
0
t
‍
∫
R
‍
ln
⁡
⁡
(
1
-
θ
u
(
y
-
1
)
)
N
(
d
u
,
d
y
)
+
∫
0
t
‍
∫
R
‍
θ
u
(
y
-
1
)
v
(
d
y
)
d
u
)
,
where
(3)
θ
u
=
μ
u
-
r
u
V
u
+
∫
R
‍
(
y
-
1
)
2
v
(
d
y
)
and r
t is the risk-free rate of interest in the discounted risky asset price process X
^
t
=
e
-
∫
0
t
‍
r
u
d
u
X
t. The derivation of (2) is given in Appendix A. Correspondingly, the Brownian motion and the compensated Poisson process under the minimal martingale measure are derived as follows:
(4)
W
~
t
1
=
W
t
1
+
∫
0
t
‍
θ
u
V
u
d
u
,
W
~
t
2
=
W
t
2
+
ρ
∫
0
t
‍
θ
u
V
u
d
u
,
v
~
(
d
y
)
d
t
=
(
1
-
θ
u
(
y
-
1
)
)
v
(
d
y
)
d
t
,
N
~
(
d
t
,
d
y
)
=
N
(
d
t
,
d
y
)
-
v
~
(
d
y
)
d
t
.In our model, there are two dimensions of risks from the Lévy process and the Brownian motion. Thus the financial market is not complete and such a claim has an intrinsic risk. In this context, we construct the pricing model under the risk-minimization criterion by employing the minimal martingale measure method, and therefore model (1) can be written as follows:
(5)
d
X
t
X
t
-
=
μ
d
t
+
V
t
d
W
~
t
1
+
∫
R
‍
(
y
-
1
)
N
~
(
d
t
,
d
y
)
-
θ
t
V
t
d
t
-
∫
R
‍
θ
t
(
y
-
1
)
2
v
(
d
y
)
d
t
,
d
V
t
=
α
(
φ
-
V
t
)
d
t
+
σ
V
t
d
W
~
t
2
-
ρ
σ
θ
t
V
t
d
t
.The value of the American put optionP
(
t
,
X
t
,
V
t
) at time t, under strike price K and maturity date T, is
(6)
P
(
t
,
X
t
,
V
t
)
=
max
⁡
τ
E
q
[
e
-
∫
t
τ
‍
r
u
d
u
(
K
-
X
τ
)
+
∣
F
t
]
,
where τ, representing the stopping times in [
0
,
T
], is to be determined to maximize the discounted option payoff.Under the minimal martingale measureq, we obtain the following partial integrodifferential equation (see Appendix B for the detailed derivation):
(7)
∂
P
∂
t
=
(
r
+
K
0
-
θ
t
K
1
)
P
(
t
,
X
,
V
)
-
∂
P
∂
X
(
r
-
K
1
+
θ
t
K
2
)
X
-
∂
P
∂
V
(
α
(
φ
-
V
)
-
ρ
σ
θ
t
V
)
-
1
2
∂
2
P
∂
X
2
V
X
2
-
∂
2
P
∂
X
∂
V
X
V
σ
ρ
-
1
2
∂
2
P
∂
V
2
V
σ
2
-
∫
R
‍
P
(
t
,
X
y
,
V
)
v
(
d
y
)
+
θ
t
∫
R
‍
P
(
t
,
X
y
,
V
)
(
y
-
1
)
v
(
d
y
)
,
where
(8)
K
0
=
∫
R
‍
v
(
d
y
)
,
K
1
=
∫
R
‍
(
y
-
1
)
v
(
d
y
)
,
K
2
=
∫
R
‍
(
y
-
1
)
2
v
(
d
y
)
.Under Merton’s jump diffusion model, we assumev
(
d
y
)
=
λ
f
(
y
)
d
y, where λ is the jump intensity and f
(
y
) is distribution of the jump size y, and assume that the stochastic volatility V is the log-normal distribution with the density
(9)
f
(
y
)
=
1
y
δ
2
π
exp
⁡
(
-
(
log
⁡
y
-
γ
)
2
2
δ
2
)
.
Let λ
=
1; thus the notations under the log-normal density functions above are K
0
=
1,K
1
=
e
δ
/
2
+
γ
-
1, and K
2
=
(
1
/
1
-
2
δ
2
)
e
γ
2
/
(
1
-
2
δ
2
)
-
2
e
δ
/
2
+
γ
+
1.Now we take the time transformτ
=
T
-
t and introduce an operator L by
(10)
L
P
~
=
-
(
r
+
K
0
-
θ
K
1
)
P
~
(
τ
,
X
,
V
)
+
∂
P
~
∂
X
(
r
-
K
1
+
θ
K
2
)
X
+
∂
P
~
∂
V
(
α
(
φ
-
V
)
+
ρ
σ
θ
V
)
+
1
2
∂
2
P
~
∂
X
2
V
X
2
+
∂
2
P
~
∂
X
∂
V
X
V
σ
ρ
+
1
2
∂
2
P
~
∂
V
2
V
σ
2
+
∫
R
‍
P
~
(
τ
,
X
y
,
V
)
v
(
d
y
)
-
θ
∫
R
‍
P
~
(
τ
,
X
y
,
V
)
(
y
-
1
)
v
(
d
y
)
.Then, the value of the American put option satisfies the following linear complementarity problem:(11)
LCP


:
∂
P
~
∂
τ
-
L
P
~
τ
≥
0
,
P
~
τ
≥
(
K
-
X
τ
)
+
,
(
∂
P
~
∂
τ
-
L
P
~
τ
)
[
P
~
τ
-
(
K
-
X
τ
)
+
]
=
0
with the boundary conditions
(12)
P
~
(
τ
,
0
,
V
)
=
K
,
∂
P
~
(
τ
,
X
-
,
V
)
∂
X
=
0
,
P
~
(
τ
,
X
,
0
)
=
(
K
-
X
)
+
,
∂
P
~
(
τ
,
X
,
V
-
)
∂
V
=
0
and the initial condition
(13)
P
~
(
0
,
X
,
V
)
=
(
K
-
X
)
+
.


## 3. Numerical Discretization

We use the uniform gridΔ
τ
=
Γ
/
n, Δ
x
=
X
-
/
m, and Δ
v
=
V
-
/
l on the domain [
0
,
Γ
]
×
[
0
,
X
-
]
×
[
0
,
V
-
]. Denoting P
~
i
,
j
=
P
~
(
τ
,
i
Δ
x
,
j
Δ
v
) and using the central difference scheme, we have
(14)
∂
P
~
∂
x
(
τ
,
x
i
,
v
j
)
=
P
~
i
+
1
,
j
(
τ
)
-
P
~
i
-
1
,
j
(
τ
)
2
Δ
x
,
∂
2
P
~
∂
x
2
(
τ
,
x
i
,
v
j
)
=
P
~
i
+
1
,
j
(
τ
)
-
2
P
~
i
,
j
(
τ
)
+
P
~
i
-
1
,
j
(
τ
)
(
Δ
x
)
2
,
∂
P
~
∂
v
(
τ
,
x
i
,
v
j
)
=
P
~
i
,
j
+
1
(
τ
)
-
P
~
i
,
j
-
1
(
τ
)
2
Δ
v
,
∂
2
P
~
∂
v
2
(
τ
,
x
i
,
v
j
)
=
P
~
i
,
j
+
1
(
τ
)
-
2
P
~
i
,
j
(
τ
)
+
P
~
i
,
j
-
1
(
τ
)
(
Δ
v
)
2
,
∂
2
P
~
∂
x
∂
v
(
τ
,
x
i
,
v
j
)
=
P
~
i
+
1
,
j
+
1
(
τ
)
+
P
~
i
-
1
,
j
-
1
(
τ
)
-
P
~
i
+
1
,
j
-
1
(
τ
)
-
P
~
i
-
1
,
j
+
1
(
τ
)
4
Δ
x
Δ
v
.Through a long derivation, we obtain from (10) the following set of discrete equations and inequalities in matrix form:
(15)
∂
P
~
∂
τ
-
C
P
~
≥
0
,
P
~
≥
g
,
(
∂
P
~
∂
τ
-
C
P
~
)
T
(
P
~
-
g
)
=
0
,
where P
~
=
(
P
~
00
P
~
10
P
~
20
⋯
P
~
m
0
P
~
01
P
~
11
P
~
21
⋯
P
~
m
1
⋯
P
~
0
l
P
~
1
l
P
~
2
l
⋯
P
~
m
l
)
T,C
=
D
-
R, in which D is a block tridiagonal matrix due to the first six terms in (10), and R is a full matrix contributed from the two integral terms of (10). The matrix D can be expressed in the form
(16)
D
=
(
B
1
D
1
E
1
0
…
0
0
0
0
B
2
D
2
E
2
…
0
0
0
⋮
⋮
⋮
⋮
⋱
⋮
⋮
⋮
0
0
0
0
…
B
l
-
1
D
l
-
1
E
l
-
1
0
0
0
0
…
0
B
¯
l
D
l
)
,
B
j

=

(
a
1
,
j
b
1
,
j

-
a
1
,
j
0
…
0
0
0
0
a
2
,
j
b
2
,
j
-
a
2
,
j
…
0
0
0
⋮
⋮
⋮
⋮
⋱
⋮
⋮
⋮
0
0
0
0
…
a
m

-

1
,
j
b
m
-
1
,
j

-
a
m
-
1
,
j
0
0
0
0
…
0
0
b
m
,
j
)
,
B
¯
l
=
(
2
a
1
,
l
0
…
0
0
2
a
2
,
l
…
0
⋮
⋮
⋱
⋮
0
0
…
2
a
m
,
l
)
,
D
j

=

(
c
1
,
j
d
1
,
j

-

c
1
,
j
0
…
0
0
0
0
c
2
,
j
d
2
,
j
-
c
2
,
j
…
0
0
0
⋮
⋮
⋮
⋮
⋱
⋮
⋮
⋮
0
0
0
0
…
c
m

-

1
,
j
d
m

-

1
,
j
-
c
m
-
1
,
j
0
0
0
0
…
0
0
d
m
,
j
)
,
E
j

=

(
a
1
,
j
-

b
1
,
j
a
1
,
j
0
…
0
0
0
0
a
2
,
j
-

b
2
,
j
a
2
,
j
…
0
0
0
⋮
⋮
⋮
⋮
⋱
⋮
⋮
⋮
0
0
0
0
…
a
m
-

1
,
j
-
b
m
-
1
,
j
a
m
-
1
,
j
0
0
0
0
…
0
2
a
m
,
j

-
b
m
,
j
)
in which a
i
j
=
-
i
j
σ
ρ
/
4,  b
i
j
=
(
α
(
φ
-
j
Δ
v
)
-
ρ
σ
θ
t
j
Δ
v
-
j
σ
2
)
/
2
Δ
v, c
i
j
=
(
i
(
r
-
k
1
+
θ
t
k
2
)
-
j
Δ
v
i
2
)
/
2, and d
i
j
=
r
+
k
0
-
θ
t
k
1
+
j
Δ
v
i
2
+
(
j
σ
2
/
Δ
v
).To calculate the matrixR due to the two integral terms in (10), let z
=
X
y and rewrite the integrals by
(17)
I
1
(
τ
,
X
,
V
)
=
∫
0
∞
‍
P
~
(
τ
,
X
y
,
V
)
f
(
y
)
d
y
=
∫
0
∞
‍
P
~
(
τ
,
z
,
V
)
f
(
z
/
X
)
X
d
z
,
I
2
(
τ
,
X
,
V
)
=
∫
0
∞
‍
P
~
(
τ
,
X
y
,
V
)
(
y
-
1
)
f
(
y
)
d
y
=
∫
0
∞
‍
P
~
(
τ
,
z
,
V
)
(
z
/
X
-
1
)
f
(
z
/
X
)
X
d
z
.The above integrals can then be discretized by using linear interpolation, namely,(18)
I
1
(
i
,
j
)
τ
=
∫
0
∞
‍
P
~
(
τ
,
z
,
j
Δ
v
)
f
(
z
/
i
Δ
x
)
(
i
Δ
x
)
d
z
≈
∑
k
=
0
m
-
1
‍
A
i
,
j
τ
,
k
,
where
(19)
A
i
,
j
τ
,
k
=
∫
k
Δ
x
(
k
+
1
)
Δ
x
‍
P
~
(
τ
,
z
,
j
Δ
v
)
f
(
z
/
i
Δ
x
)
(
i
Δ
x
)
d
z
=
∫
k
Δ
x
(
k
+
1
)
Δ
x
‍
[
(
k
+
1
)
Δ
x
-
z
Δ
x
P
~
(
τ
,
k
Δ
x
,
j
Δ
v
)
+
z
-
k
Δ
x
Δ
x
P
~
(
τ
,
(
k
+
1
)
Δ
x
,
j
Δ
v
)
]
×
f
(
z
/
i
Δ
x
)
(
i
Δ
x
)
d
z
=
1
2
[
k
P
~
(
τ
,
(
k
+
1
)
Δ
x
,
j
Δ
v
)
-
(
k
+
1
)
P
~
(
τ
,
k
Δ
x
,
j
Δ
v
)
]
×
[
erf
⁡
⁡
(
μ
-
ln
⁡
⁡
(
(
k
+
1
)
/
i
)
2
σ
)
-
erf
⁡
⁡
(
μ
-
ln
⁡
⁡
(
k
/
i
)
2
σ
)
]
+
1
2
i
exp
⁡
(
σ
2
2
+
μ
)
×
[
P
~
(
τ
,
k
Δ
x
,
j
Δ
v
)
-
P
~
(
τ
,
(
k
+
1
)
Δ
x
,
j
Δ
v
)
]
×
[
erf
⁡
(
μ
-
ln
⁡
(
(
k
+
1
)
/
i
)
+
σ
2
2
σ
)
-
erf
⁡
(
μ
-
ln
⁡
⁡
(
k
/
i
)
+
σ
2
2
σ
)
]
.Lete
1
i
k
=
(
1
/
2
)
(
erf
⁡
(
(
μ
-
ln
⁡
(
(
k
+
1
)
/
i
)
)
/
2
σ
)
-
erf
⁡
(
(
μ
-
ln
⁡
(
k
/
i
)
)
/
2
σ
)
), and e
2
i
k
=
(
1
/
2
)
i
exp
⁡
(
σ
2
/
2
+
μ
)
(
erf
⁡
(
(
μ
-
ln
⁡
(
(
k
+
1
)
/
i
)
+
σ
2
)
/
2
σ
)
-
erf
⁡
(
(
μ
-
ln
⁡
(
k
/
i
)
+
σ
2
)
/
2
σ
)
). Then (19) becomes
(20)
A
i
,
j
τ
,
k
=
(
e
2
i
k
-
(
k
+
1
)
e
1
i
k
)
P
~
(
τ
,
k
Δ
x
,
j
Δ
v
)
+
(
k
e
1
i
k
-
e
2
i
k
)
P
~
(
τ
,
(
k
+
1
)
Δ
x
,
j
Δ
v
)
,
I
1
(
i
,
j
)
τ
≈
∑
k
=
0
m
-
1
‍
A
i
,
j
τ
,
k
=
e
2
,
i
0
-
2
e
1
,
i
0
+
∑
k
=
1
m
-
1
‍
[
(
k
-
1
)
e
1
,
i
k
-
1
-
e
2
,
i
k
-
1
+
e
2
,
i
k
-
(
k
+
1
)
e
1
,
i
k
]
+
(
m
-
1
)
e
1
,
i
m
-
1
-
e
2
,
i
m
-
1
,
where
(21)
A
=
(
A
0
0
…
0
0
A
0
…
0
⋮
⋮
⋱
⋮
0
0
…
A
0
)
,
A
0
=
(
a
0,0
a
0,1
a
0,2
…
a
0
,
m
-
1
a
0
,
m
a
1,0
a
1,1
a
1,2
…
a
1
,
m
-
1
a
1
,
m
a
2,0
a
2,1
a
2,2
…
a
2
,
m
-
1
a
2
,
m
⋮
⋮
⋮
⋮
⋮
⋮
a
m
,
0
a
m
,
1
a
m
,
2
…
a
m
,
m
-
1
a
m
,
m
)
in which a
i
,
0
=
e
2
,
i
0
-
e
1
,
i
0,  a
i
,
k
=
(
k
-
1
)
e
1
,
i
k
-
1
-
e
2
,
i
k
-
1
+
e
2
,
i
k
-
(
k
+
1
)
e
1
,
i
k, k
=
1,2
,
…
,
m
-
1, and a
i
,
m
=
(
m
-
1
)
e
1
,
i
m
-
1
-
e
2
,
i
m
-
1,i
=
0,1
,
…
,
m. Hence I
1
τ
=
A
P
~.In a similar way, we obtainI
2
τ
=
B
P
~, where
(22)
B
=
(
B
0
0
…
0
0
B
0
…
0
⋮
⋮
⋱
⋮
0
0
…
B
0
)
,
B
0
=
(
b
00
b
01
b
02
…
b
0
,
m
-
1
b
1
,
m
b
10
b
11
b
12
…
b
1
,
m
-
1
b
1
,
m
b
20
b
21
b
22
…
b
2
,
m
-
1
b
2
,
m
⋮
⋮
⋮
⋮
⋮
⋮
b
m
0
b
m
1
b
m
2
…
b
m
,
m
-
1
b
m
,
m
)
in which b
i
0
=
e
4
i
0
-
2
e
3
i
0
-
e
2
i
0
-
2
e
1
i
0,  b
i
,
k
=
(
k
-
1
)
e
3
,
i
k
-
1
-
e
4
,
i
k
-
1
+
e
4
,
i
k
-
(
k
+
1
)
e
3
,
i
k
-
(
k
-
1
)
e
1
,
i
k
-
1
+
e
2
,
i
k
-
1
-
e
2
,
i
k
+
(
k
+
1
)
e
1
,
i
k
,
k
=
1,2
,
…
,
m
-
1,  b
i
m
=
(
m
-
1
)
e
3
,
i
m
-
1
-
e
4
,
i
m
-
1
-
(
m
-
1
)
e
1
,
i
m
-
1
+
e
2
,
i
m
-
1
,
i
=
0,1
,
…
,
m, e
3
i
k
=
(
1
/
2
)
exp
⁡
(
σ
2
/
2
+
μ
)
×
[
erf
⁡
(
(
μ
-
ln
⁡
(
(
k
+
1
)
/
i
)
+
σ
2
)
/
2
σ
)
-
erf
⁡
(
(
μ
-
ln
⁡
(
k
/
i
)
+
σ
2
)
/
2
σ
)
], and e
4
i
k
=
(
1
/
2
i
1
-
2
σ
2
)
exp
⁡
⁡
(
μ
2
/
(
1
-
2
σ
2
)
)
×
[
erf
⁡
(
(
1
-
2
σ
2
(
μ
-
ln
⁡
(
(
k
+
1
)
/
i
)
)
)
/
2
σ
-
2
μ
σ
/
1
-
2
σ
2
)
-
erf
⁡
(
(
1
-
2
σ
2
(
μ
-
ln
⁡
(
k
/
i
)
)
)
/
2
σ
-
2
μ
σ
/
1
-
2
σ
2
)
].Hence, the integral part of the operatorL is R
=
(
A
-
θ
τ
B
).Now for American options we obtain a semidiscrete LCP. The Crank-Nicolson method [15] is then used for time discretization. Let Δ
τ
=
Γ
/
n; we obtain
(23)
(
I
-
1
2
Δ
τ
C
(
n
)
)
P
~
(
n
)
≥
(
I
+
1
2
Δ
τ
C
(
n
+
1
)
)
P
~
(
n
+
1
)
,
(
I
-
1
2
Δ
τ
C
(
n
)
)
P
~
(
n
)
≥
iiiiiii
P
~
(
n
+
1
)
+
P
~
(
n
)
≥
2
g
,
(
(
I
-
1
2
Δ
τ
C
(
n
)
)
P
~
(
n
)
-
(
I
+
1
2
Δ
τ
C
(
n
+
1
)
)
P
~
(
n
+
1
)
)
T
iiiiiiiii
×
(
P
~
(
n
+
1
)
+
P
~
(
n
)
-
2
g
)
=
0
which can be denoted by
(24)
LCP
(
M
,
P
~
(
n
+
1
)
,
N
P
~
(
n
)
,
g
)
:
N
P
~
(
n
)
≥
M
P
~
(
n
+
1
)
,
P
~
(
n
+
1
)
+
P
~
(
n
)
≥
2
g
,
(
N
P
~
(
n
)
-
M
P
~
(
n
+
1
)
)
T
×
(
P
~
(
n
+
1
)
+
P
~
(
n
)
-
2
g
)
=
0
,
where M
=
I
+
(
1
/
2
)
Δ
τ
C
(
n
+
1
) and N
=
I
-
(
1
/
2
)
Δ
τ
C
(
n
).The projected SOR algorithm PSOR(M
,
P
~
(
n
+
1
)
,
N
P
~
(
n
)
,
g) can then be used to solve the LCP problem. The algorithm is as follows.Forn
=
1, dim
⁡
(
M
),
(25)
r
n
=
N
P
~
n
-
∑
j
‍
M
i
,
j
P
~
j
,
P
~
n
=
max
⁡
{
P
~
j
+
ω
r
n
M
i
,
j
,
g
n
}
,
where the relaxation parameter ω
=
2
/
(
1
+
1
-
φ
2), wherein φ
=
max
⁡
i
{
(
1
/
M
i
,
i
)
∑
j
≠
i
|
M
i
,
j
|
}.


## 4. Numerical Results and Discussion

In this section, we present a set of results to demonstrate the feature of the model and the robustness of the model for the determination of American put option price and then investigate the influence of market volatility. The basic parameter values used in the computation are given in Table1. The numerical algorithm is implemented in computer by using the MATLAB 2012 software package. Figure 1 shows the payoff as the function of stock price. The numerical solutions obtained by solving the LCP problem (24) are showed in Figure 2. We also compute the value of American option price under various values of volatility. The results are plotted in Figure 3.Table 1
The basic parameter values used in numerical investigation.
μ
0.23
κ
3.46
φ
0.089
4
2
δ
0.0001
r
0.039
σ
0.14
λ
0.77
γ
−0.086
ρ
−0.82
T
1
S
max
⁡
200
v
max
⁡
1
K
100
N
3000
I
30
J
30Figure 1
The payoff as a function of stock price (stock price = stock step* 20/3; volatility = volatility step * 1/30).Figure 2
The American option price.Figure 3
American option price under different volatilities.From the numerical results, we can see that volatility has no influence on the payoff of American option, but it plays an important role in American option pricing. From Figures2 and 3, we can see that, when the stock price is relatively low, the influence of volatility is not significant, while, as the stock price increases, the influence of volatility becomes more and more significant. It can also be found that, at the same level of stock price, the larger the volatility is, the more the influence it plays on American option price is, which is in agreement with the market reality. Thus, the results show that the model and numerical scheme are robust in capturing the feature of incomplete finance market, particularly the influence of market volatility on the price of American options.



---

*Source: 236091-2014-02-26.xml*