Date post: | 04-Jan-2016 |
Category: |
Documents |
Upload: | tracy-brett-pitts |
View: | 224 times |
Download: | 1 times |
2
Content
1 Binomial Trees
2 Using the binomial tree for options on indices,
currencies, and futures contracts
3 Binomial model for a dividend-paying stock
4 Alternative procedures for constructing trees
5 Time-dependent parameters
6 Monte Carlo simulation
7 Variance reduction procedures
8 Finite difference methods
3
Binomial Trees
In each small interval of time ( Δt ) the stock price is assumed to move up by a proportional amount u or to move down by a proportional amount d
Su
Sd
S
p
1 – p
4
Risk-Neutral Valuation
1. Assume that the expected return from all traded assets is the risk-free interest rate.
2. Value payoffs from the derivative by calculating their expected values and discounting at the risk-free interest rate.
5
Determination of p, u, and d
Mean: e(r-q)t = pu + (1– p )d
Variance:2t = pu2 + (1– p )d 2 – e2(r-q)t
A third condition often imposed is u = 1/ d
6
A solution to the equations, when terms of higher order than t are ignored, is
where
)( tqr
t
t
ea
ed
eu
du
dap
7
Tree of Asset Prices
At time iΔt :
S0u 2
S0u 4
S0d 2
S0d 4
S0
S0u
S0d S0 S0
S0u 2
S0d 2
S0u 3
S0u
S0d
S0d 3
i0,1,...,j ,duSS j-ij0i
8
Working Backward through the Tree
Example : American put optionS0 = 50; K = 50; r =10%; = 40%; T = 5 months = 0.4167;
t = 1 month = 0.0833
The parameters imply : u = 1.1224; d = 0.8909;
a = 1.0084; p = 0.5073
9
Example (continued)89.070.00
79.350.00
70.70 70.700.00 0.00
62.99 62.990.64 0.00
56.12 56.12 56.122.16 1.30 0.00
50.00 50.00 50.004.49 3.77 2.66
44.55 44.55 44.556.96 6.38 5.45
39.69 39.6910.36 10.31
35.36 35.3614.64 14.64
31.5018.50
28.0721.93
G
F
ED C
B A
39.690.89091.122450duSS 31310A 14.6435.36-50,0)-max(f TG SK 2.665.45)e0.49270(0.5073efp-1pff 0.0833-0.1t-r
duE 9.9014.64)e0.49275.45(0.5073f 0.0833-0.1A
4.496.96)e0.49272.16(0.5073f 0.0833-0.1D
10
Example (continued)
In practice, a smaller value of Δt, and many more nodes, would be used. DerivaGem shows :
steps 5 30 50 100 500
f0 4.49 4.263 4.272 4.278 4.283
12
Estimating Delta and Other Greek Letters
delta ( Δ ): at time Δt
dS-uS
f-f
S
f
00
1,01,1
S0u 2
S0u 4
S0d 2
S0d 4
S0
S0u
S0d S0 S0
S0u 2
S0d 2
S0u 3
S0u
S0d
S0d 3
13
gamma ( Γ ): at time 2Δt
)dS-u0.5(S
dS-S
f-f-
S-uS
f-f
S
f2
02
0
200
2,02,1
02
0
2,12,2
2
2
S0u 2
S0u 4
S0d 2
S0d 4
S0
S0u
S0d S0 S0
S0u 2
S0d 2
S0u 3
S0u
S0d
S0d 3
14
theta ( Θ ):
t2
f-f
t
f 0,02,1
S0u 2
S0u 4
S0d 2
S0d 4
S0 S0u
S0d S0 S0
S0u 2
S0d 2
S0u 3
S0u
S0d
S0d 3 f
2
1rS 22 rSσ
16
Example
89.070.00
79.350.00
70.70 70.700.00 0.00
62.99 62.990.64 0.00
56.12 56.12 56.122.16 1.30 0.00
50.00 50.00 50.004.49 3.77 2.66
44.55 44.55 44.556.96 6.38 5.45
39.69 39.6910.36 10.31
35.36 35.3614.64 14.64
31.5018.50
28.0721.93
G
F
ED C
B A
-0.4144.55-56.12
6.96-2.16
dS-uS
f-f
00
1,01,1
0.0311.65
(-0.64)-(-0.24)
39.69)-0.5(62.9939.69-5010.36-3.77
-50-62.99
3.77-0.64
)dS-u0.5(S
-2
02
0
21
daycalendar per -0.012or
yearper -4.30.08332
4.49-3.77
t2
f-f 0,02,1
17
Using the binomial tree for options on indices, currencies, and futures contracts
As with Black-Scholes : For options on stock indices, q equals the
dividend yield on the index For options on a foreign currency, q equal
s the foreign risk-free rate For options on futures contracts : q = r
20
Binomial model for a dividend-paying stock
Known Dividend Yield : before : after :
Several known dividend yields :
i0,1,...,j ,duSS j-ij0ji,
i0,1,...,j ,d)u-(1SS j-ij0ji,
j-iji0ji, d)u-(1SS
21
Known Dollar Dividend :
i k≦ : i=k+1 : i=k+2 :
i0,1,...,j ,duSS j-ij0ji,
i0,1,...,j D,-duSS j-ij0ji,
1-i0,1,...,jfor
D)d-du(S andD)u -du(SS j-1-ij0
j-1-ij0ji,
nodes. 1mkn rather tha 2)m(k are there
mki when and nodes, 1in rather tha 2i are there
22
Simplify the problem
The stock price has two components : a part that is uncertain and a part that is the present value of all future dividends during the life of the option.
Step 1 : A tree can be structured in the usual way to model .
Step 2 : By adding to the stock price at each nodes, the present value of future dividends, the tree can be converted into model S.
*S
ti when , De-SS
ti when , SSt)i-r(-*
*
ti when , DeduSS
ti when , duSSt)i-r(-j-ij*
0ji,
j-ij*0ji,
24
Control Variate Technique
1. Using the same tree to calculate both the value of the American option ( ) and the value of the European option ( ) .
2. Calculating the Black-Scholes price of the European option ( ) .
3. This gives the estimate of the value of the American option as
EBSA -fff
Af
Ef
BSf
26
Alternative procedures for constructing trees
Instead of setting u = 1/d we can set each of the 2 probabilities to 0.5 and
ttqr
ttqr
ed
eu
)2/(
)2/(
2
2
27
Example
0.9703
ed
1.0098
eu
and 0.5pset We
0.25t , 0.75T
0.04
0.10r , 0.06r
0.795K , 0.79S
]0.250.04-.250.0016/2)0-0.1-[(0.06
]0.250.04.250.0016/2)0-0.1-[(0.06
f
0
30
Time-dependent parameters
V/Nt)(t then
steps, timeN of totala is thereIf
step th time theof end theis t
tree theof life theis T , T(T)V Define
maturity t afor y volatilit theis (t) that Suppos
: timeoffuction a make To 2
of valueforward theis (t)
rateinterest forward theis (t)
eset We
: timeoffuction a )(or and make To 1
2
2
t(t)]-(t)[
i
i
qg
f
a
rqr
ii
i
gf
f
31
Monte Carlo simulation
When used to value an option, Monte Carlo simulation uses the risk-neutral valuation result. It involves the following steps:
1. Simulate a random path for S in a risk neutral world.2. Calculate the payoff from the derivative.3. Repeat steps 1 and 2 to get many sample values of
the payoff from the derivative in a risk neutral world.4. Calculate the mean of the sample payoffs to get an
estimate of the expected payoff.5. Discount this expected payoff at risk-free rate to get
an estimate of the value of the derivative.
32
Monte Carlo simulation (continued)
In a risk neutral world the process for a stock price is
We can simulate a path by choosing time steps of length Δt and using the discrete version of this
where ε is a random sample from (0,1)
dS S dt S dz
ttSttStSttS )()(ˆ)(-)(
33
Monte Carlo simulation (continued)
T T 2
ˆ)0(ln)T(ln
thenconstant, are and ˆ if
)()(or
2
ˆ)(ln)(ln
is thisof version discrete The
2
ˆln
.n rather tha ln estimate toaccurate more isIt
2
2/ˆ
2
2
2
SS
etSttS
tttSttS
dzdtSd
SS
tt
34
Derivatives Dependent on More than One Market Variable
When a derivative depends on several underlying variables we can simulate paths for each of them in a risk-neutral world to calculate the values for the derivative :
ttSttmttt iiiiiii )()(ˆ)()(
35
Generating the Random Samples from Normal Distributions
How to get two correlated samples ε1 and ε2 from univariate standard normal distributions x1 and x2 ?
n.correlatio oft coefficien theis where
-1xx
x
2212
11
37
Number of Trials Denote the mean by μ and the standard deviation
by ω. The standard error of the estimate is
where M is the number of trials. A 95% confidence interval for the price f of the
derivative is
To double the accuracy of a simulation, we must quadruple the number of trials.
M
Mf
M
1.961.96-
38
Applications Advantage : 1. It tends to be numerically more efficient
( increases linearly ) than other procedures ( increases exponentially ) when there are more stochastic variables.
2. It can provide a standard error for the estimates.
3. It is an approach that can accommodate complex payoffs and complex stocastic processes.
39
Applications (continued)
An estimate for the hedge parameter is
Sampling through a Tree :
x
ff *
ˆ-ˆ
40
Variance reduction procedures
Antithetic Variable Techniques :
standard error of the estimate is
Control Variate Technique :
2
21 fff
BBAA ffff **
M
41
Variance reduction procedures (continued)
Importance Sampling:
Stratified Sampling:
Moment Matching:
Using Quasi-Random Sequences:
)0.5-
(1-
n
iN
s
mi*i
-
42
Finite difference methods
Define ƒi,j as the value of ƒ at time it when the stock price is jS
ΔT=T/N; ΔS=Smax /M
43
Implicit Finite Difference Method
Forward difference approximation
backward difference approximation
S
ff
S
f jiji
,1,
S
ff
S
f jiji
1,,
44
Implicit Finite Difference Methods(continued)
ƒ2ƒƒƒ
or
ƒƒƒƒƒ
2
ƒƒƒset we
ƒƒ
2
1ƒƒIn
2
,1,1,
2
2
1,,,1,
2
2
1,1,
2
222
SS
SSSS
SS
rS
SS
rSt
jijiji
jijijiji
jiji
45
Implicit Finite Difference Methods(continued)
1, ,
, 1 , , 1 1,
If we also set and S
we obtain:
where t
i j i j
j i j j i j j i j i j
2 2j
2j
f ffS jΔ
t t
a f b f c f f
1 1a (r - q)jΔ - σ j Δt
2 2
b 1 σ
t
t
2
2 2j
j Δt rΔ
1 1 c - (r - q)jΔ - σ j Δt
2 2
47
Explicit Finite Difference Methods
:obtain we
point )( at the are they aspoint )1( at the
same thebe toassumed are and If 22
i,j,ji
SfSf
2
1,11,11,
2
2
11,11,
ƒ2ƒƒƒ
2
ƒƒƒ
SS
SS
jijiji
jiji
48
Explicit Finite Difference Methods(continued)
1,1*
,1*
1,1*
, ƒƒƒ
isequation difference The
jijjijjijji cbaf
)t(1
1
)-(1
1
)t(-1
1 where *
Δtjσ2
1(r-q)jΔ
2
1
tr c
Δtjσ1tr
b
Δtjσ2
1(r-q)jΔ
2
1
tra
22*j
22*j
22j
50
Difference between implicit and explicit finite difference methods
ƒi +1, j +1
ƒi , j ƒi +1, j
ƒi +1, j –1
ƒi +1, jƒi , j
ƒi , j –1
ƒi , j +1
Implicit Method Explicit Method
51
Change of Variable
ƒZ
ƒ
2
1
Z
ƒ)
2-q-(
ƒ
, ln ZDefine
2
22
2
rrt
S
2j
2j
2j
jijijjijjij
σ2
)-(r-q2
rΔσ 1
σ2
)-(r-q2
ffff
2
2
2
2
2
,11,,1,
Z
t
2-
Z
t-
tZ
t
Z
t
2-
Z
t where
becomes methodimplicit for theequation difference The
52
Change of Variable (continued)
1,1*
,1*
1,1*
, ƒƒƒ
becomes methodexplicit for theequation difference The
jijjijjijjif
)Z
t)
2-(
Z
t(
1
1
)Z
t-(
1
1
)Z
t)
2-(
Z
t(-
1
1 where
2
2
2
2
2*
2*j
2*j
2j
σ2
r-q2tr
σ1tr
σ2
r-q2tr
54
Relation to Trinomial Tree Approaches (continued)
2 2
2
2
2
2
2
1- j t is negative when j 13.
We can use change-of-variable approch:
t t - ( - )
Z 2 Zt
-Z
t t ( - )
Z 2 Z
2u
2m
2d
p r - q σ2 2
p 1 σ
p r - q σ2 2
56
Summary
We have three different numerical procedures for valuing derivatives when no analytic solution: trees, Monte Carlo simulation, and finite difference methods.
Trees: derivative price are calculated by starting at the end of the tree and working backwards.
Monte Carlo simulation: works forward from the beginning, and becomes relatively more efficient as the number of underlying variables increases.
Finite difference method: similar to tree approaches. The implicit finite difference method is more complicated but has the advantage that does not have to take any special precautions to ensure convergence.