國立臺灣大學財務金融學系(所)碩士論文 Department and Graduate Institute of Finance
College of Management
指導老師﹕呂育道 教授 Advisor:Dr. Yuh-Dauh Lyuu
控制變異數法在美式選擇權之應用
Variance Reduction Methods for Monte Carlo Valuation of American Options
研究生﹕陳芳婷
Advisee:Fung-Ting Chen
中華民國96年7月 July, 2007
碩士論文
控 制 變 異 數 法 在 美 式 選 擇 權 之 應 用 陳 芳 婷 撰
96
國立臺灣大學
財務金融學系
(所
)
摘要 摘要 摘要 摘要
自從 Longstaff and Swartz (2001)提出的最小平方估計法 (least-squares Monte Carlo),解決了蒙地卡羅模擬法難以用於美式選擇權之訂價的一大缺點。
於是,蒙地卡羅模擬法簡單、易懂,且易於應用至多資產商品的特性,使得蒙 地卡羅模擬廣泛地被用於選擇權的評價問題上。然而,蒙地卡羅模樣通常需要 大量的模擬路徑,才能得到較好的估計;這使得評價變得極為耗時。
本研究即是探討兩種降低變異的方法,希望能藉此提昇蒙地卡羅的模擬效 率。這兩種降低變異的方法分別是由 Rasmussen (2005)以及 Duan and Simonato (2001)所提出來的。本研究將之分別應用到美式賣權及極大值買權的評價,結 果發現由 Rasmussen (2005)所提出來的方法,皆能有效地降低模擬的變異程 度。
Abstract
For many complex options, analytical solutions are not available. In these cases
a Monte Carlo simulation is computationally inefficient, the variance reduction
method can be used to improve the efficiency of a Monte Carlo simulation.
In this thesis we apply the two variance reduction methods proposed by
Rasmussen (2005) and Duan and Simonato (1998) in American option pricing. We
find that the variance reduction method proposed by Rasmussen can provide
significant improvement of efficiency than Duan and Simonato even the
combination of these two methods does not perform better than only using the
variance reduction methods proposed by Rasmussen. We also apply this variance
reduction method proposed by Rasmussen in the valuation of two-, three- or five
max-call options and we find that they can provide significant improvement both on
efficiency and accuracy for pricing.
Content
1. Introduction .………1
2. The American Option Valuation Problem..………....6
3. Monte Carlo Valuation with Variance Reduction Method.………...8
3.1. Control Variates Method ……….8
3.2. Empirical Martingale Simulation ………..12
4. The LSM Approach………...15
4.1. The Choice of Basis Functions………..…………....16
4.2. Accuracy, Stability and Convergence………..………...17
4.3. The Control Variates Improved LSM………..……..………....18
5. Numerical Results……….21
5.1. American Put………..21
5.2. American Rainbow Options………...28
6. Conclusion………...33
Bibliography………...34
1. Introduction
Closed-form pricing formulas have been derived for many European options
under a variety of financial models, the most notable being the Black-Sholes
formula under the geometric Brownian motion model. American-type options are
options with flexible early exercise features. Examples are American equity and
fixed-income options and convertible bonds. These contracts arise in virtually all
major financial markets. However, when the option is American-type, the possibility
of early exercise should be considered for the determination of the optimal early
exercise policy. This often leads to highly complicated calculations.
There is a long and rich history of numerical methods for pricing American-style
contingent claims. Among the earliest approaches are the binomial lattice of Cox et
al. (1979) and the explicit finite difference scheme of Brennan and Schwartz (1977).
These methods work particularly well for American options on a single underlying
asset. However, many American-style options have been introduced that depend on
multiple underlying assets or state variables. Multidimensional generalizations of the
Cox et al. binomial method were proposed in Boyle (1988), Boyle et al. (1989), and
others. A related approach involves extensions of the finite difference method to
higher dimensions were proposed by Mitchell and Griffiths (2001). Adapting
for options on two or three state variables, but because their computational effort
grows exponentially with the number of state variables, these methods are
impractical for higher dimensional problems. This is the so-called curse of
dimensionality. In contrast, simulation methods do not suffer from this difficulty.
This thesis considers adapting the Monte Carlo approach for pricing American
options.
The conditional expectations involved in the iterations of dynamic programming
cause the main difficulty for the development of Monte Carlo techniques. Boyle
(1977) first proposed Monte Carlo simulation for the pricing of European claims.
However, it was not until much later that the possibility of using Monte Carlo
simulation for pricing American-style options was suggested by Bossaerts (1989)
and Tilley (1993). Tilley ranked simulated stock price from the maximum to
minimum and divided them into several groups. He computed the holding value by
averaging the discounted payoff within each group and used these holding values to
find an exercise boundary. In this method, stock price is the only factor determining
whether to exercise or not. Barraquand and Martineau (1995) developed a method
which is closely related to that of Tilley (1993) but easier to extend. The idea is to
partition the state space of simulated paths into a number of cells in such a way that
the payoff from the option is approximately equal across the paths in the particular
cell. The probabilities of moving to different cells in the next period conditional on
the current cell can then be calculated from the simulated paths. With these
probabilities in place, the expected value of keeping the option alive until the next
period can be calculated, and a strategy for exercise determined. Broadie and
Glasserman (1997) proposed a convergent algorithm based on simulated trees. Their
method generated both lower and upper bounds so that valid confidence intervals on
the true Bermudan price can be determined. The simulation tree method removes the
exponential dependence of the computation time (CPU time) on the problem
dimension; however, the CPU time is still exponential in the number of exercise
opportunities. A new and somewhat simpler simulation based method to price
American options has recently been proposed by Longstaff and Schwartz (2001).
The idea is to estimate the conditional expectation of the payoff from keeping the
option alive at each possible exercise point from a simple least squares
cross-sectional regression using the simulated paths. They show how to price
different types of path-dependent options using this least-squares Monte Carlo (LSM)
approach.
In these papers, authors introduced numerical methods based on Monte Carlo
techniques. The starting point of these methods is to replace the time interval of
exercise dates by a fixed finite subset. This amounts to approximating the American
option by a Bermudan option, i.e., options with discretely exercisable features not
continuous ones. The solution of the discrete optimal stopping problem reduces to an
effective implementation of the dynamic programming principle.
Although there are many works about least squares regression methods, most
have paid only little attention to the issue of variance reduction. Only a few articles
go beyond applying antithetic variates. In this thesis we consider the application of
control variates to the valuation of American- or Bermudan-type options. In Broadie
and Glasserman (1997), the European option’s payoff at expiry is used as a control
variate. They found that the control variates work quite well for out-of-the-money
options, but are less effective for deep-in-the-money options. They also found that
payoff processes of European options and American options are less correlated when
American options are deep-in-the-money than when American options are
out-of-the-money. The reason is that out-of-the-money American options might have
lower probability to be exercised than deep-in-the-money American options. As a
result, European options are highly correlated with out-of-the-money American
options but less correlated with deep-in-the-money American options. In other
words, traditional European option is not a very good control variate for
deep-in-the-money options.
Another idea is based on a simple observation that simulated sample paths for
the underlying asset price almost always fail to possess the martingale property even
though the theoretical model uses the assumption of martingale. The failure to
ensure the martingale property has particularly serious consequences in the later
time interval when there is more time division. It often requires a very large number
of simulation repetitions to dampen these simulation errors. Duan and Simonato
(1998) proposed a correction to the standard procedure by ensuring that the
simulated sample paths all satisfy the martingale property in each time interval. This
correction is referred to as empirical martingale simulation (EMS).
In this thesis we apply the two variance reduction methods proposed by
Rasmussen (2005) and Duan and Simonato (1998) in American option pricing. We
also compare these two methods and combine them. We find that this new variance
reduction method, which combines the above two methods, cannot provide
significant improvement of efficiency and accuracy for pricing. Then we also apply
variance reduction methods in the valuation of max-call options and we find that
they can provide significant improvement on efficiency and accuracy for pricing.
2. The American Option Valuation Problem
The problem of valuing an American option consists of finding an optimal
exercise strategy and then valuing the expected discounted payoff from this strategy
under the equivalent martingale measure.1 We let
V
r denote the time t solution tothis problem, that is,
Ε
=
∈
F
tT
sup |
) ,
( τ
τ
τ
β
β
X
V
QT t t
t
where
{ } X
t 0≤t≤T is the payoff process adapted to the filtration and T( Tt
, ) denotesthe set of stopping time
τ
satisfyingT
τt
≤ ≤We can easily define a lower bound on the American option price at time t denoted
by
L
t, since for any given exercise strategy or stopping timeτ
we havet Q t
t
t
X V
L
β β
β
ττ
≤
Ε
= | F
tFor an upper bound we refer the reader to Theorem 1 of Anderson and Broadie
1 In what follows we assume that the financial market is defined for the finite horizon [0, T] on a complete filtered probability space(Ω,F,{Ft}0≤t≤T,Ρ). Here the state Ω is the set of all realizations of the financial market,
F
is the sigma algebra of events at time T, and Ρis a probability measure defined on F. The filtration{ }
Ft 0≤t≤T is assumed to be generated by the price processes of the financial market and augmented with the null sets ofF
, and assumingF =
TF
. We furthermore assume that using the numeraire process{ } β
t 0≤t≤T there exists a measureQ
equivalent to Ρ under which all asset prices relative to the numeraire are martingales.
(2004) and leave out the details here.
In the following sections we give numerical results based on the single-asset
American put option, using the same combinations of underlying asset prices, time
to expiry and volatilities as in Table 1 of Lonstaff and Schwartz (2001). The payoff
process of the single-asset put option for strike K is given by
) 0 , (
max t
t
K S
X
= −In this case, holders can exercise on the following set of equidistant points only,
, ) / (
e d T
t
e = for e=0,1, 2,K,d and is hence a Bermudan rather than an Americanoption.
3. Monte Carlo Valuation with Variance Reduction Method
3.1. Control Variates for Monte Carlo Simulation (Rasmussen (2005))
Given a stopping time
τ
∈T (t
,T
), we want to determine the followingconditional expectation given the information at time t :
= | F
tτ τ Q
t t
β Ε X β
L
(1)Using the underlying model to generate N independent paths of the variables
determining the payoff process
{ } X
t 0≤t≤T and the numeraire process{ } β
t 0≤t≤T, thecrude Monte Carlo estimate is
∑
=
=
N
i i
i
t (N) t
τ τ
β
X N
βL
1
1
where
X
τiβ
τi is the discounted payoff from thi
path using the exercise strategygiven by the stopping time
τ .
To reduce the variance of the Monte Carlo estimate of the American option, we
can replace the path estimate
X
τiβ
τi with the following path estimate
(
tQ[ ]
i)
i i t
i
i τ i
τ θ Υ
E
Υβ
X
βZ
τ
τ + −
= (2)
for some appropriately chosen F -measurable random variable t
θ
t, whereY
iis theth
i
observation of a random variable for which we can easily compute the time tconditional expectation. The Monte Carlo estimate using control variates is then
given by
[ ] (
tQ i)
(N) t t t (N) t
N
i i
i
t (N) t
Y Ε Υ β θ
L β Z N β L
τ τ
− +
=
= ∑
=
1
1
where
∑
∑
=
=
=
=
N
i i (N)
t N
i i
i τ t
(N)
t
Y
, Υ N
βX N
βL
τ 1
1
1 1
By the standard ordinary least-squares theory, the optimal choice of
θ
t is
[ ] [ ] Υ
,Υ β X
Q t
τ τ Q t
t
Var
*
= − Cov
θ
(3)which results in the following minimum variance
[ ]
( )
( )CV
2 t
Var 1 Var 1
N
Q t Q τ
t t t τ τ
τ
L X
ρ X β ,Υ
N β
β
= −
where
[ ]
[ ] [ ]
Cov
Var Var
Q
t τ τ
τ
t Q Q
τ t τ τ t
X β ,Υ ρ X ,Υ
β X β Υ
≡
⋅
Hence the most effective control variates Y are obtained by having the largest
possible correlation, either positive of negative, with the discounted payoff from the
Bermudan option.
A good control variate should have the following two properties: it should be
highly correlated with the payoff of the option in question and its conditional
expectation should be easy to compute. When looking for a control variate for the
Bermudan option, the corresponding European option would be our general choice.
Let
W
T be the value of a self-financing portfolio at expiry of the Bermudan option.Using the discounted value of this portfolio, the European option control variate
Y
Tis then defined by
T T T
Y W
= β
By construction of the equivalent martingale measure Q, the process
{ } Y
t 0≤t≤Tdefined by
[
T |Ft]
Q
t Ε
Y
Y
=is a martingale.
The optimal European option control variates is given by
T T T
Y X
= β
(4)for which we have
t t
T Q T t
C Ε X
Y β = β
= | F
t (5)the discounted European option price. This control variate clearly satisfies the
second properties mentioned; however, its correlation with the payoff of the
Bermudan option is not high enough, especially for in-the-money options. Therefore,
we replace the control variate (4) with the control variates
=
=
ττ τ
τ
β β
T| F
Q
W
TW Ε
Y
(6)Thus, rather than sampling the discounted payoff process at expiry of the option,
Rasmussen (2005) suggests sampling the discounted value process at the time of
exercise of the Bermudan option. The conditional expectation of control variates (4)
is
[ ]
tQ
Y Y
Ε τ |Ft =
which is identical to the expectation of the control variate (4).
It is intuitively true that European option and American option payoffs are less
correlated when the options are in-the-money. From an option holder’s viewpoint,
the estimate accuracy is of greatest importance for in-the-money options because
this is where the critical exercise decisions have to be made. As a result, Rasmussen
He has shown that the application of the control variates to the valuation of
American or Bermudan options can be very effective if we sampled these control
variates at the exercise time rather than at expiry.
3.2. Empirical Martingale Simulation (Duan and Simonato (1998))
The theoretical works for contingent claim pricing mostly rely on the absence
of arbitrage opportunities. The martingale connection to the arbitrage-free price
system was first observed by Cox and Ross (1976) and later formalized by Harrison
and Kreps (199). For the ease of exposition, we consider a price system consisting of
two securities, one risky and the other risk-free. The risky security, a common stock,
does not pay dividends and its price, denoted by
S
(t), has the following dynamicsunder the risk-neutral probability measure Q:
( )
− +
=
S
o∫
tr s ds ∫t s dW s
t s
0 0
2( ) ( ) ( )
5 . 0 exp
)
(
σ σ
where r is the continuously compounded return on the risk-free security,
σ
(s) isthe instantaneous standard deviation of the asset return and
W
(s) is a standardBrownian motion under probability measure Q. It is easy to verify that the
discounted asset price is indeed a Q-martingale in that, for any
τ
≥ t ≥0,[ e S
( )|F ] e S
(t
)ΕQ −rτ
τ
t = −rtwhere
Ε
Q[] ⋅
denotes the expectation operator under the risk-neutral measure Q andF the information filtration up to time t. t
In a typical Monte Carlo simulation, this martingale property almost always
fails in the simulated sample. Valuation often requires a very large number of
simulation repetitions to dampen simulation errors. Duan and Simonato (1998)
proposed a simple transformation for the original simulation asset process and
adjusted them to satisfy the martingale property. Their transformation steps are listed
below:
(1) Define the discrete times by
t , t , t ,
0 1 2K , t
m, wheret
0 is the current time.(2) Simulate asset prices at times
t , t , t ,
0 1 2K , t
m for each simulated pathN
i
=1,2,K, and define the ith simulated asset price at timet
j by).
( j
i
t S
(3) Compute the ith simulated asset returns at time
t
j defined as) (
) ) (
(
1
−
=
j i
j i
j i
t S
t t S
R
, for allj
=1,2,K,m
;i
=1,2,K,N
(4). Set
S
i( t
0) = S
i( t
0) = S
0)
for all
i
=1,2,K,N
In this equation,S
i(t
j) )is
the adjusted EMS asset price at the ith sample path and at time
t
j andS
0is the initial asset price at time
t
0.(5). Do a recursive procedure for all
j
=1,2,K,m
;i
=1,2,K,N
) (
) ) (
(
) 1 (
) (
) ( ) ( ) (
0 0
1 0
1
j j i
j i
N
i
j rt i
j
j i j i j i
t Z
t S Z t S
t N Z
e t Z
t R t S t Z
j
=
=
⋅
=
∑
=
−
−
)
)
where r is the risk-free interest rate.
Although they had proposed a correction to the standard Monte Carlo
simulation procedure and imposed the martingale property on the collection of
simulated sample paths, in their paper they only applied this modification within
some specific parameters (e.g., maturities less than 1 year) and European-type
options. This thesis will apply the modification to American options with longer
maturities and different volatility parameters and combine this simulation
adjustment to the control variates method proposed by Rasmussen (2005) for
American options.
4. The LSM Approach with Control Variates
The main assumption of the LSM approach is that the time t conditional
expectation in (1) can be expressed as a linear combination of a countable set of
F -measurable basis functions as follows: t
∑
∞=
=
1 j
j t j t t
t
a F
L
β
(7)The implement this assumption in the LSM approach, (7) can be approximated with
a finite sum at a given level. We let
L
Mtβ
t denote this approximation when Mbasis functions are used. We then have
∑
=
=
M
j j t j t t
M
t
a F
L
β
1 (8) Using cross-sectional observations of the Monte Carlo generated state variable (suchas stock price), the coefficients
a
tj,j
=1,2,K,M
, are determined by least-squaresregression, where the current basis functions
F
tj,j
=1,2,K,M
, are independentvariables and the discounted payoff process
X
tβ
t is the dependent variable. Asargued by Longstaff and Schwartz (2001), we only need the approximation where
the option is in-the-money at time t and include these observations in the regression.
With a total of N Monte Carlo generated paths, we let
a
tj( N),j
=1,2,K,M
, denotethe time t coefficients determined from the regression using only the in-the-money
paths. Hence the approximation used in the implementation denoted by
L
M(N)β
isgiven by
∑
=
=
M
j
j t N j t t
M
t
a F
L
1 ) (
β
(9)Given the time t value of the state variable summarized by the basis
functions
F
tj, j=1, 2, …, M, we exercise the option whenever the time t exercisevalue exceeds the corresponding conditional expectation. In other words, we will
exercise when the following inequality is satisfied
t M t
t
t
L
X β β ≥ 4.1. The Choice of Basis Functions
For the sake of simplicity and to maintain some financial intuition, we use the
following basis functions corresponding to
M
=3.
All functions depend on thecurrent time t and the current stock price
S
t, i.e.,F
tj =f
j(t
,S
t), where) , ( )
, (
) , ( ) , (
) , (
) , (
3 2 1 0
s t C s s t f
s t C s t f
s s t f
K s t f
⋅
=
=
=
=
Above, K is the strike price of the Bermudan put option, s is the current asset price
and
C
( st
, ) is the time t European put option price expiring at time T and with itsother parameters identical to the Bermudan option.
4.2. Accuracy, Stability and Convergence of the LSM Approach
The accuracy of the LSM exercise strategy is solely determined by the accuracy
of the following two approximations:
t N M t
t M t
t M t
t t
L L
L L
β β
β β
) (
≅
≅
With an infinite computational budget, we should require that
L
Mt (N) →L
t as∞
→
N
andM
→∞. However, in Clement et al (2002), the convergence oft M
t
L
L
→ asM
→∞ is established when an orthogonal set of basis functions isused. They also considered the convergence of
L
Mt (N) →L
t asN
→∞. Finally,they established a central limit result for the rate of convergence of the LSM
algorithm. These results are very important especially from a theoretical point of
view. However, from a practical point of view, we are more concerned with the
performance given a finite sample of N paths and a finite number of basis functions
M. Hence we focus in the following section on improving the above approximation
for a given number of paths and basis functions. The object is to replace (8) with a
more accurate approximation.
4.3. Improvement of LSM with Control Variates
In traditional LSM approach, we only choose the discounted payoff
X
tβ
t ofin-the-money paths to do regression for evaluating the conditional expectation value
= | F
tτ τ Q
t t
β Ε X β L
as in (1). In this section, we reuse and generalize the concept of control variates as
the Monte Carlo variation in section 3.1. We replace the discounted payoff
X
tβ
twith the random variable
Z
tβ
t :
(
τ[ ]
τ)
τ τ τ
τ
θ Υ E Υ
β X β
Z
Qt
t
−
+
=
(10)where
Y
τ is the control variates sampled from European option’s payoffs atexercise points as in (6), and
θ
t is an appropriately chosen F -measurable random tvariable. Rather than a point estimate as in (3), we now use a functional estimate of
∗
θ
t . By the definition of[ X
tβ
tY
τ]
Q
t ,
Cov and V
ar
tQ[ ] Y
τ we get[ ] [ ] ( [ ] )
( )
( )
2( )
2Q 2 2 Q
Q Q
Q
E
t t
t t t t t
t t
t t
t
t
Y Y
L Y LY
Y E Y E
X Y E X Y
E
−
⋅
−
−
=
−
⋅
−
⋅
−
∗ =
β β
β θ β
τ τ
τ τ τ τ
τ τ
where
L
tβ
t is defined in (1),Y
t is defined in (5), and( ) Y
2 t and( LY )
tβ
t aredefined by
( ) [ ]
( )
⋅
≡
≡
τ t Q t t t
t
τ Q t t
β Y E X β
LY
, Y E
Y
2 2respectively. Generalizing the assumptions of LSM as (7), we assume that the time t
conditional expectations
Y
t,( ) Y
2 t and( LY )
tβ
t can also be expressed as acountable sum of the same set of F -measurable basis functions, i.e., t
( )
( )
jt j
j t t
t
j t j
j t t
j t j
j t t
F LY d
F c Y
F b Y
⋅
=
⋅
=
⋅
=
∑
∑
∑
∞
=
∞
=
∞
=
1 1 2
1
, ,
β
Again we can approximate the conditional expectations by truncating the sums at the
same level M. Let
Y
tM,( ) Y
2 tM and( )
t MLY
tβ
denote the approximations as in (8).Now we have
( )
( )
jt M
j j t t
M t
j t M
j j t M
t
j t M
j j t M
t
F LY d
F c Y
F b Y
⋅
=
⋅
=
⋅
=
∑
∑
∑
=
=
=
1 1 2
1
, ,
β
We can approximate
θ
t∗ withθ
tM given by( )
( ) Y
2( Y M )
2L Y LY
t M t
M t t M t t M t
M
t −
⋅
−
=
β β
θ
Using the concept of control variates in (10), we can approximate the time t
conditional expectation of the discounted payoff from following the strategy
τ
byCV , M
L
t given by the following expression:(
t)
M t M
t M
t M
Y Υ β θ
L β
L
t t
t
= + −
CV ,
5. Numerical Results
5.1. American Put
In this chapter, we use some numerical examples2 to compare the efficiency
and accuracy of three different variance reduction methods. First, we use normalized
antithetic paths, i.e., we use traditional moment matching simulation and antithetic
method, and denote this method as “AN+MM” in the following. Second, we
replicate the simulated paths in the first method and use discounted European
options sampling at exercise as control variates and denote this as “CV-at-exercise.”
Third, we also replicate the simulated paths in the first method but only adjusting the
underlying process to satisfy martingale property and denote this as “EMS” method.
In order to show the efficiency and accuracy of the AN+MM, CV-at-exercise
and EMS, we use binomial-based put prices as the benchmark and compare their
standard error (S.E.) and mean absolute percentage error (MAPE) to see the
efficiency and accuracy of these three methods (AN+MM, CV-at-exercise and EMS).
Also we will use ratios of S.E and MAPE between AN+MM and CV-at-exercise or
AN+MM and EMS to see whether CV-at-exercise or EMS can improve the
efficiency or accuracy of the LSM approach.
From the results of CV-at-exercise method in the second category in Table 1,
we find the standard errors in CV-at-exercise are only one tenth of the AN+NN
method in the third column of the second category. These improvements are more
significant when options are at-the-money (
S
=40) or out-of-the-money (S
=42,44)and with shorter maturity (
t
=1). This can be confirmed by observing the smallerratio of S.E of CV divided by S.E of AN+MM listed in the third column of the
second category. But from the next two columns, improvement in terms of MAPE is
not very significant in CV-at-exercise method except when option is
deep-out-of-the-money (
S
=44). Also from the third column of third category inTable 1, we find that standard errors in the EMS method are not less than the
AN+MM method but the MAPE in this method are smaller than AN+MM method
when options are in-the-money (
S
=36,38).In Table 1, the improvements of standard errors using CV-at-exercise are quite
significant. Now we try to combine CV-at-exercise and EMS and evaluate if the
resulting method can improve the efficiency or accuracy in pricing Bermudan
options compared with the AN+MM method. The results are shown in Table 2.
As before, we use the parameters of Longstaff and Schwartz (2001) in the
following numerical examples whose results are listed in Table 2. In our second test,
we combine the CV-at-exercise method and the EMS method, denoted by CV+EMS.
We also compare the result of CV+EMS with CV-at-exercise and list the results in
Table 2. In order to more clearly compare the results, we find the ratios of their S.E
and MAPE and list the results in Table 3. From the results in Table 3, we find only
when
σ
=0.2 and options are deep-in-the-money (S
=36), can CV+EMS obtainsmaller S.E. than CV-at-exercise in all our maturity parameters. In other parameters,
the improvement in terms of S.E. in CV+EMS method is not significant. Also, the
improvement in terms of MAPE in CV+EMS is not very significant in most
parameters of our test except when
σ
=0.5and options are deep-in-the-money(
S
=36).
Table 1. This is Monte Carlo valuation of the Bermudan options. We use the binomial tree-based
exercise strategy as the benchmark and compare three different variance reduction methods. First, we only use antithetic pair paths and do moment matching method (AN+MM) in the simulation. Second, we replicate the AN+MM and use European options sampling at exercise as control variates (CV-at-exercise). Last, we also replicate the AN+MM and use empirical martingale simulation (EMS) to adjust underlying asset processes. All simulations are based on 2000 pairs of antithetic paths. All options have strike K=40, and the interest rate equals r=0.06. The current stock price S, the volatility σ, and the time to expiry T are given.AN+MM CV-at-exercise EMS
S t σ binomial
model price S.E.
(%)
MAPE
(%) price S.E.
(%)
CV/
AN+MM
MAPE (%)
CV/
AN+MM price S.E.
(%)
EMS/
AN+MM
MAPE (%)
EMS/
AN+MM 0.2 4.4845 4.4885 2.6433 0.0891 4.4726 0.2777 0.1051 0.2645 2.9693 4.4874 2.6627 1.0074 0.0642 0.7213 1
0.4 7.0997 7.1392 4.1982 0.5568 7.0852 0.2724 0.0649 0.2043 0.3670 7.1143 4.9029 1.1678 0.2059 0.3699 0.2 4.8512 4.8498 3.3725 0.0287 4.8474 0.5526 0.1639 0.0790 2.7514 4.8500 3.3443 0.9916 0.025 0.8713 36
2
0.4 8.5310 8.5397 4.6569 0.1019 8.5328 0.6612 0.1420 0.0207 0.2032 8.5383 6.5843 1.4139 0.0857 0.8415 0.2 3.2529 3.2672 2.2367 0.4402 3.2420 0.2285 0.1022 0.3355 0.7622 3.2586 2.3066 1.0313 0.1747 0.3969 1
0.4 6.1805 6.1843 4.2744 0.0610 6.1782 0.2400 0.0561 0.0372 0.6090 6.1816 6.0043 1.4047 0.0185 0.3034 0.2 3.7546 3.7524 2.7642 0.0591 3.7564 0.4651 0.1683 0.0481 0.8138 3.7526 2.9799 1.0780 0.0542 0.9171 38
2
0.4 7.6990 7.6943 4.7164 0.0605 7.6929 0.5717 0.1212 0.0792 1.3098 7.6983 7.0241 1.4893 0.0092 0.1515 0.2 2.3130 2.3369 2.6482 1.0347 2.2966 0.2157 0.0815 0.7081 0.6844 2.3388 2.8427 1.0735 1.1139 1.0766 1
0.4 6.3028 5.3505 4.3863 15.1086 5.2802 0.2161 0.0493 16.2243 1.0738 5.3539 6.1141 1.3939 15.0553 0.9965 0.2 2.8800 2.8947 2.7106 0.5089 2.8532 0.4146 0.1529 0.9298 1.8271 2.8951 3.1117 1.1480 0.5249 1.0315 40
2
0.4 6.9036 6.9458 4.6295 0.6117 6.8654 0.5427 0.1172 0.5527 0.9035 6.9502 7.3925 1.5968 0.6755 1.1042 0.2 1.6239 1.6400 2.8891 0.9903 1.6163 0.1686 0.0583 0.4685 0.4731 1.6409 3.3076 1.1449 1.0492 1.0594 1
0.4 4.6137 4.6182 4.8191 0.0982 4.6128 0.2047 0.0425 0.0192 0.1958 4.6252 6.9900 1.4505 0.2486 2.5310 0.2 2.2249 2.2274 3.0713 0.1132 2.2277 0.4061 0.1322 0.1242 1.0971 2.2291 3.5747 1.1639 0.1908 1.6856 42
2
0.4 6.2670 6.2690 4.8283 0.0312 6.2680 0.4679 0.0969 0.0157 0.5040 6.2682 8.1596 1.6899 0.0199 0.6403 0.2 1.1212 1.1300 2.6878 0.7858 1.1198 0.1496 0.0557 0.1210 0.1539 1.1309 2.9862 1.1110 0.8653 1.1011 1
0.4 3.9616 3.9797 5.6508 0.4576 3.9502 0.1908 0.0338 0.2877 0.6288 3.9657 7.6675 1.3569 0.1034 0.2260 0.2 1.6907 1.7082 3.1561 1.0339 1.6885 0.3569 0.1131 0.1295 0.1253 1.7088 3.6018 1.1412 1.0719 1.0367 44
2
0.4 5.6781 5.6620 4.8480 0.2835 5.6764 0.3953 0.0815 0.0307 0.1081 5.6682 7.9300 1.6357 0.1749 0.6167
Table 2. This is Monte Carlo valuation of the Bermudan options. We use the binomial tree-based exercise
strategy as the benchmark and compare two variance reduction methods. First we use CV-at-exercise method. Second, we combine CV-at-exercise and EMS method and denote this as EMS+CV method. All these simulations are based on 2000 pairs of antithetic paths. All options have strike K=40, and the interest rate equal to r=0.06. The current stock price S is given the same value as in Table 1. In order to see the effect of the volatility σ and the time to expiry T , we give additional values to these two parameters besides in Table 1.T=0.25 T=0.5
σ S
binomial
model CV S.E
(%) MAPE CV+
EMS S.E
(%) MAPE binomia
l model CV S.E
(%) MAPE CV+
EMS S.E
(%) MAPE 4.0000 4.0000 0.0000 0.0000 4.0000 0.0000 0.0000 4.0000 4.0007 0.0052 0.0002 4.0007 0.0052 0.0002 2.0000 2.0000 0.0105 0.0000 2.0000 0.0105 0.0000 2.0000 1.9948 0.0190 0.0026 1.9948 0.0190 0.0026 0.2245 0.2215 0.0549 0.0134 0.2215 0.0546 0.0134 0.2587 0.2541 0.0934 0.0178 0.2541 0.0949 0.0178 0.0019 0.0018 0.0053 0.0618 0.0018 0.0051 0.0666 0.0082 0.0080 0.0141 0.0288 0.0080 0.0141 0.0284 0.05
36 38 40 42
44 0.0000 0.0000 0.0000 * 0.0000 0.0000 * 0.0001 0.0001 0.0011 0.3318 0.0001 0.0011 0.3318 4.0000 4.0023 0.0171 0.0006 4.0023 0.0171 0.0006 4.0000 4.0018 0.0348 0.0004 4.0018 0.0348 0.0004 2.0000 2.0003 0.7655 0.0001 2.0003 0.7629 0.0002 2.0123 2.0148 0.4546 0.0012 2.0148 0.4477 0.0012 0.5861 0.5819 0.0494 0.0071 0.5819 0.0495 0.0071 0.7372 0.7304 0.1057 0.0092 0.7304 0.1064 0.0092 0.1051 0.1051 0.0177 0.0003 0.1051 0.0197 0.0002 0.2173 0.2178 0.0570 0.0021 0.2177 0.0576 0.0021 0.1
36 38 40 42
44 0.0104 0.0102 0.0140 0.0219 0.0102 0.0136 0.0208 0.0493 0.0489 0.0273 0.0085 0.0489 0.0262 0.0087 4.0480 4.0472 0.3861 0.0002 4.0472 0.3669 0.0002 4.2105 4.2122 0.3066 0.0004 4.2122 0.2947 0.0004 2.4718 2.4701 0.0814 0.0007 2.4701 0.0785 0.0007 2.8242 2.8212 0.1404 0.0011 2.8212 0.1389 0.0011 1.3527 1.3472 0.0507 0.0041 1.3472 0.0513 0.0041 1.7921 1.7825 0.0964 0.0053 1.7825 0.0959 0.0053 0.6700 0.6685 0.0357 0.0022 0.6685 0.0353 0.0022 1.0928 1.0921 0.0801 0.0007 1.0921 0.0805 0.0007 0.2
36 38 40 42
44 0.2937 0.2934 0.0273 0.0010 0.2934 0.0289 0.0010 0.6362 0.6361 0.0543 0.0001 0.6361 0.0603 0.0001 4.5098 4.5090 0.1621 0.0002 4.5090 0.1495 0.0002 5.0238 5.0219 0.1863 0.0004 5.0219 0.1737 0.0004 3.1775 3.1732 0.0622 0.0013 3.1732 0.0614 0.0013 3.8403 3.8365 0.1376 0.0010 3.8365 0.1339 0.0010 2.1314 2.1249 0.0468 0.0031 2.1249 0.0477 0.0031 2.8760 2.8646 0.1010 0.0039 2.8647 0.1002 0.0039 1.3800 1.3787 0.0357 0.0009 1.3787 0.0372 0.0009 2.1361 2.1308 0.0836 0.0025 2.1308 0.0831 0.0025 0.3
36 38 40 42
44 0.8569 0.8564 0.0355 0.0006 0.8563 0.0343 0.0006 1.5595 1.5579 0.0743 0.0010 1.5579 0.0813 0.0010 5.1170 5.1138 0.1156 0.0006 5.1138 0.1038 0.0006 5.9823 5.9777 0.1664 0.0008 5.9777 0.1570 0.0008 3.9069 3.9033 0.0546 0.0009 3.9033 0.0528 0.0009 4.9086 4.9074 0.1267 0.0003 4.9074 0.1256 0.0003 2.9128 2.9056 0.0440 0.0025 2.9056 0.0446 0.0025 3.9653 3.9526 0.0984 0.0032 3.9527 0.1008 0.0032 2.1422 2.1377 0.0360 0.0021 2.1377 0.0357 0.0021 3.2317 3.2285 0.0924 0.0010 3.2286 0.0957 0.0010 0.4
36 38 40 42
44 1.5469 1.5445 0.0417 0.0016 1.5445 0.0379 0.0015 2.5772 2.5753 0.0859 0.0007 2.5753 0.0878 0.0007 5.8042 5.8021 0.0719 0.0004 5.8021 0.0664 0.0004 6.9490 6.9351 0.1388 0.0020 6.9351 0.1354 0.0020 4.6715 4.6698 0.0482 0.0004 4.6697 0.0474 0.0004 5.9766 5.9741 0.1251 0.0004 5.9741 0.1291 0.0004 3.6939 3.6861 0.0443 0.0021 3.6861 0.0452 0.0021 5.0539 5.0400 0.0948 0.0028 5.0400 0.0983 0.0028 0.5
36 38 40
*When T=0.25 and σ=0.05, the option price of binomial model is nearly zero. In this case, MAPE is infinite and has no meaning..
T=1 T=2
σ S
binomial
model CV S.E
(%) MAPE CV+
EMS S.E
(%) MAPE
binomial
model CV S.E (%) MAPE CV+
EMS S.E (%) MAPE 4.0000 4.0045 0.0170 0.0011 4.0045 0.0170 0.0011 4.0000 4.0014 0.0376 0.0003 4.0014 0.0356 0.0003 2.0000 2.0082 0.0374 0.0041 2.0081 0.0354 0.0041 2.0000 2.0142 0.0583 0.0071 2.0142 0.0587 0.0071 0.2825 0.2728 0.1600 0.0342 0.2729 0.1654 0.0341 0.2936 0.2786 0.2646 0.0509 0.2786 0.2645 0.0509 0.0176 0.0177 0.0369 0.0066 0.0177 0.0378 0.0070 0.0243 0.0243 0.1118 0.0015 0.0242 0.1106 0.0035 0.05
36 38 40 42
44 0.0007 0.0006 0.0080 0.0906 0.0006 0.0079 0.0912 0.0019 0.0019 0.0247 0.0185 0.0018 0.0245 0.0267 4.0000 4.0076 0.1311 0.0019 4.0076 0.1286 0.0019 4.0000 4.0234 0.7864 0.0058 4.0234 0.7863 0.0058 2.0554 2.0424 0.2309 0.0063 2.0424 0.2310 0.0063 2.1077 2.0950 0.4239 0.0060 2.0950 0.4237 0.0060 0.8894 0.8775 0.1849 0.0134 0.8775 0.1894 0.0134 1.0188 1.0012 0.3712 0.0173 1.0012 0.3814 0.0173 0.3558 0.3549 0.1522 0.0026 0.3549 0.1555 0.0026 0.4874 0.4847 0.3222 0.0055 0.4847 0.3363 0.0056 0.1
36 38 40 42
44 0.1276 0.1279 0.0766 0.0025 0.1279 0.0780 0.0026 0.2267 0.2273 0.2072 0.0027 0.2273 0.2087 0.0026 4.4845 4.4722 0.3610 0.0027 4.4722 0.3475 0.0027 4.8512 4.8475 0.5288 0.0008 4.8476 0.5208 0.0008 3.2529 3.2421 0.2553 0.0033 3.2420 0.2533 0.0033 3.7546 3.7562 0.4683 0.0004 3.7562 0.4766 0.0004 2.3130 2.2967 0.2157 0.0071 2.2967 0.2235 0.0071 2.8800 2.8535 0.4498 0.0092 2.8536 0.4612 0.0092 1.6239 1.6163 0.1686 0.0047 1.6163 0.1750 0.0047 2.2249 2.2272 0.3702 0.0010 2.2271 0.3828 0.0010 0.2
36 38 40 42
44 1.1212 1.1192 0.1396 0.0018 1.1192 0.1450 0.0018 1.6907 1.6887 0.3168 0.0012 1.6887 0.3287 0.0012 5.7476 5.7399 0.2565 0.0013 5.7399 0.2506 0.0013 6.6160 6.5993 0.5140 0.0025 6.5993 0.5262 0.0025 4.7055 4.7062 0.2296 0.0001 4.7062 0.2361 0.0001 5.1707 5.7116 0.4534 0.1046 5.7116 0.4815 0.1046 3.8016 3.7820 0.2002 0.0052 3.7820 0.2024 0.0052 4.8797 4.8461 0.4179 0.0069 4.8461 0.4395 0.0069 3.1002 3.0987 0.1858 0.0005 3.0987 0.1879 0.0005 4.2214 4.2240 0.4182 0.0006 4.2241 0.4457 0.0006 0.3
36 38 40 42
44 2.4738 2.4689 0.1682 0.0020 2.4688 0.1736 0.0020 3.6232 3.6126 0.3780 0.0029 3.6127 0.4028 0.0029 7.0997 7.0854 0.2724 0.0020 7.0855 0.2725 0.0020 8.5310 8.5338 0.5882 0.0003 8.5339 0.6032 0.0003 6.1805 6.1784 0.2400 0.0003 6.1784 0.2427 0.0003 7.6990 7.6943 0.5167 0.0006 7.6944 0.5507 0.0006 6.3028 5.2804 0.2161 0.1622 5.2805 0.2223 0.1622 6.9036 6.8665 0.4681 0.0054 6.8665 0.5161 0.0054 4.6137 4.6129 0.2047 0.0002 4.6129 0.2139 0.0002 6.2670 6.2682 0.4657 0.0002 6.2683 0.5115 0.0002 0.4
36 38 40 42
44 3.9616 3.9503 0.1908 0.0028 3.9503 0.1998 0.0028 5.6781 5.6767 0.4536 0.0002 5.6768 0.5056 0.0002 8.5205 8.5181 0.2791 0.0003 8.5182 0.2760 0.0003 10.4470 10.4480 0.6471 0.0001 10.4475 0.7124 0.0000 7.6491 7.6438 0.2661 0.0007 7.6438 0.2748 0.0007 9.6697 9.6552 0.5931 0.0015 9.6547 0.6680 0.0015 6.7993 6.7754 0.2316 0.0035 6.7755 0.2476 0.0035 8.9128 8.8707 0.5587 0.0047 8.8703 0.6297 0.0048 6.1322 6.1311 0.2179 0.0002 6.1312 0.2321 0.0002 8.3091 8.3051 0.5202 0.0005 8.3047 0.5774 0.0005 0.5
36 38 40 42
44 5.4964 5.4900 0.1974 0.0012 5.4901 0.2185 0.0012 7.7441 7.7442 0.4541 0.0000 7.7438 0.5161 0.0000
Table 3. This is Monte Carlo valuation of the Bermudan options. We select S.E. and MAPE of CV-at-exercise
and CV+EMS from Table 2 and define new ratios called S.E-ratio and MAPE-ratio. S.E.-ratios are derived by S.E of EMS+CV divided by S.E. of CV-at-exercise and MAPE-ratios are derived by MAPE of EMS+CV divided by MAPE of CV-at-exercise.S.E.-ratio ( EMS+CV/CV ) MAPE-ratio ( EMS+CV/CV )
σ t S=36 S=38 S=40 S=42 S=44 σ t S=36 S=38 S=40 S=42 S=44
0.25 0.9045 1.0000 0.9941 0.9608 * 0.25 0.9557 0.9874 1.0003 1.0780 * 0.5 1.0001 1.0000 1.0151 0.9985 1.0889 0.5 1.0009 0.9998 0.9999 0.9878 1.0000
1 1.0002 0.9448 1.0341 1.0247 0.9776 1 1.0002 0.9978 0.9989 1.0562 1.0069 0.05
2 0.9456 1.0060 0.9997 0.9892 0.9940 0.05
2 1.0075 0.9997 0.9999 2.3503 1.4443 0.25 1.0000 0.9965 1.0011 1.1127 0.9728 0.25 1.0005 1.1457 1.0003 0.6734 0.9488 0.5 0.9998 0.9848 1.0066 1.0122 0.9609 0.5 1.0010 0.9844 1.0007 0.9635 1.0295 1 0.9812 1.0003 1.0243 1.0216 1.0191 1 1.0045 1.0009 1.0000 0.9927 1.0362 0.1
2 0.9999 0.9993 1.0275 1.0437 1.0070 0.1
2 1.0008 1.0008 1.0002 1.0075 0.9884 0.25 0.9503 0.9638 1.0125 0.9894 1.0577 0.25 0.9909 1.0031 0.9999 0.9977 1.0138 0.5 0.9610 0.9890 0.9942 1.0061 1.1122 0.5 1.0026 1.0013 0.9992 0.9900 1.2660 1 0.9624 0.9922 1.0362 1.0381 1.0390 1 1.0030 1.0046 1.0000 1.0042 0.9995 0.2
2 0.9849 1.0177 1.0254 1.0342 1.0374 0.2
2 0.9936 1.0080 0.9987 0.9823 0.9968 0.25 0.9222 0.9866 1.0191 1.0412 0.9658 0.25 0.9360 0.9955 0.9989 0.9997 1.0535 0.5 0.9322 0.9731 0.9925 0.9950 1.0939 0.5 1.0088 0.9957 0.9993 0.9940 1.0211 1 0.9770 1.0281 1.0109 1.0111 1.0324 1 0.9948 1.0017 0.9994 1.0079 1.0070 0.3
2 1.0238 1.0622 1.0518 1.0658 1.0656 0.3
2 0.9966 1.0001 1.0008 1.0115 0.9947 0.25 0.8982 0.9671 1.0138 0.9906 0.9078 0.25 1.0011 1.0018 1.0018 1.0031 0.9831 0.5 0.9436 0.9917 1.0248 1.0361 1.0220 0.5 0.9947 0.9924 0.9977 0.9949 1.0070 1 1.0003 1.0114 1.0287 1.0454 1.0471 1 0.9949 0.9913 1.0000 0.9779 0.9992 0.4
2 1.0256 1.0657 1.1026 1.0984 1.1147 0.4
2 1.0544 0.9782 0.9984 1.0939 0.9175 0.25 0.9237 0.9830 1.0217 1.0183 1.0287 0.25 0.9962 1.0021 1.0007 1.0045 0.9800 0.5 0.9752 1.0314 1.0364 1.0614 1.0172 0.5 0.9991 0.9879 0.9994 1.0004 1.0003 1 0.9891 1.0326 1.0691 1.0654 1.4106 1 0.9741 0.9884 0.9980 0.9208 0.9844 0.5
2 1.1010 1.1263 1.1270 1.1099 1.1367 0.5
2 0.4883 1.0348 1.0100 1.0902 4.1065
*When T=0.25 and σ=0.05, the option price of binomial model is nearly zero. In this case, MAPE is infinite and has no meaning..
5.2. American Rainbow Options
To investigate the application of control variates to a more complex example
than the single-asset put option investigated in the previous numerical examples, we
now investigate the n-asset Bermudan rainbow option for the case of n=2,3, and 5
assets. We use the same parameters as the example in Andersen and Broadie (2004)
without any use of variance reduction and compare the results.
Andersen and Broadie use a set of 13 basis functions involving the highest and
second highest asset prices, as well as polynomials of these, together with the value
of European max-call option on the two largest assets and polynomials of this.
Inspired by their choice and the concept of Rasmussen (2005), we use another set of
basis functions.
For the case of two assets, the European max-call option price can easily be
computed according to Stulz (1982). Although the pricing formula of multi-asset
max-option has been derived by Johnson (1987) the computation of multivariate
normal cumulated probability requires numerical integration. To circumvent this
shortcoming we therefore choose to use combinations of two-asset max-call options
as basis functions in our regression and as control variates. We list our basis
functions as following. For the case of two assets, the six basis functions are