Integer, Intent (In)	::	kopt, ns, nt, ntkeep, ldf
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	iwork(ns)
Real (Kind=nag_wp), Intent (In)	::	x, r(), q(), sigma(*), alpha
Real (Kind=nag_wp), Intent (Inout)	::	s(ns), t(nt), f(ldf,ntkeep), theta(ldf,ntkeep), delta(ldf,ntkeep), gamma(ldf,ntkeep), lambda(ldf,ntkeep), rho(ldf,ntkeep)
Real (Kind=nag_wp), Intent (Out)	::	work(4*ns)
Logical, Intent (In)	::	tdpar(3)
Character (1), Intent (In)	::	mesh

C Header Interface

#include <nag.h>

void

d03ncf_ (const Integer *kopt, const double *x, const char *mesh, const Integer *ns, double s[], const Integer *nt, double t[], const logical tdpar[], const double r[], const double q[], const double sigma[], const double *alpha, const Integer *ntkeep, double f[], double theta[], double delta[], double gamma[], double lambda[], double rho[], const Integer *ldf, double work[], Integer iwork[], Integer *ifail, const Charlen length_mesh)

The routine may be called by the names d03ncf or nagf_pde_dim1_blackscholes_fd.

3 Description

d03ncf solves the Black–Scholes equation (see Hull (1989) and Wilmott et al. (1995))

\frac{\partial f}{\partial t} + (r - q) S \frac{\partial f}{\partial S} + \frac{σ^{2} S^{2}}{2} \frac{\partial^{2} f}{\partial S^{2}} = r f

(1)

S_{\min} < S < S_{\max}, t_{\min} < t < t_{\max},

(2)

for the value

f

of a European or American, put or call stock option, with exercise price

X

. In equation (1)

t

is time,

S

is the stock price,

r

is the risk free interest rate,

q

is the continuous dividend, and

σ

is the stock volatility. According to the values in the array tdpar, the arguments

r

q

and

σ

may each be either constant or functions of time. The routine also returns values of various Greeks.

d03ncf uses a finite difference method with a choice of time-stepping schemes. The method is explicit for

alpha = 0.0

and implicit for nonzero values of alpha. Second order time accuracy can be obtained by setting

alpha = 0.5

. According to the value of the argument mesh the finite difference mesh may be either uniform, or user-defined in both

S

and

t

directions.

4 References

Hull J (1989) Options, Futures and Other Derivative Securities Prentice–Hall

Wilmott P, Howison S and Dewynne J (1995) The Mathematics of Financial Derivatives Cambridge University Press

5 Arguments

1: $kopt$ – Integer Input

On entry: specifies the kind of option to be valued.

$kopt = 1$: A European call option.
$kopt = 2$: An American call option.
$kopt = 3$: A European put option.
$kopt = 4$: An American put option.

Constraint:

kopt = 1

2

3

4

2: $x$ – Real (Kind=nag_wp) Input

On entry: the exercise price

X

3: $mesh$ – Character(1) Input

On entry: indicates the type of finite difference mesh to be used:

$mesh ='U'$: Uniform mesh.
$mesh ='C'$: Custom mesh supplied by you.

Constraint:

mesh ='U'

'C'

4: $ns$ – Integer Input

On entry: the number of stock prices to be used in the finite difference mesh.

Constraint:

ns \geq 2

5: $s (ns)$ – Real (Kind=nag_wp) array Input/Output

On entry: if

mesh ='C'

s (i)

must contain the

i

th stock price in the mesh, for

i = 1, 2, \dots, ns

. These values should be in increasing order, with

s (1) = S_{\min}

and

s (ns) = S_{\max}

mesh ='U'

s (1)

must be set to

S_{\min}

and

s (ns)

S_{\max}

, but

s (2), s (3), \dots, s (ns - 1)

need not be initialized, as they will be set internally by the routine in order to define a uniform mesh.

On exit: if

mesh ='U'

, the elements of s define a uniform mesh over

[S_{\min}, S_{\max}]

mesh ='C'

, the elements of s are unchanged.

Constraints:

if $mesh ='C'$ , $s (1) \geq 0.0$ and $s (i) < s (i + 1)$ , for $i = 1, 2, \dots, ns - 1$ ;
if $mesh ='U'$ , $0.0 \leq s (1) < s (ns)$ .

6: $nt$ – Integer Input

On entry: the number of time-steps to be used in the finite difference method.

Constraint:

nt \geq 2

7: $t (nt)$ – Real (Kind=nag_wp) array Input/Output

On entry: if

mesh ='C'

then

t (j)

must contain the

j

th time in the mesh, for

j = 1, 2, \dots, nt

. These values should be in increasing order, with

t (1) = t_{\min}

and

t (nt) = t_{\max}

mesh ='U'

then

t (1)

must be set to

t_{\min}

and

t (nt)

t_{\max}

, but

t (2), t (3), \dots, t (nt - 1)

need not be initialized, as they will be set internally by the routine in order to define a uniform mesh.

On exit: if

mesh ='U'

, the elements of t define a uniform mesh over

[t_{\min}, t_{\max}]

mesh ='C'

, the elements of t are unchanged.

Constraints:

if $mesh ='C'$ , $t (1) \geq 0.0$ and $t (j) < t (j + 1)$ , for $j = 1, 2, \dots, nt - 1$ ;
if $mesh ='U'$ , $0.0 \leq t (1) < t (nt)$ .

8: $tdpar (3)$ – Logical array Input

On entry: specifies whether or not various arguments are time-dependent. More precisely,

r

is time-dependent if

tdpar (1) = .TRUE.

and constant otherwise. Similarly,

tdpar (2)

specifies whether

q

is time-dependent and

tdpar (3)

specifies whether

σ

is time-dependent.

9: $r (*)$ – Real (Kind=nag_wp) array Input

Note: the dimension of the array r must be at least

nt

tdpar (1) = .TRUE.

, and at least

1

otherwise.

On entry: if

tdpar (1) = .TRUE.

then

r (j)

must contain the value of the risk-free interest rate

r (t)

at the

j

th time in the mesh, for

j = 1, 2, \dots, nt

tdpar (1) = .FALSE.

then

r (1)

must contain the constant value of the risk-free interest rate

r

. The remaining elements need not be set.

10: $q (*)$ – Real (Kind=nag_wp) array Input

Note: the dimension of the array q must be at least

nt

tdpar (2) = .TRUE.

, and at least

1

otherwise.

On entry: if

tdpar (2) = .TRUE.

then

q (j)

must contain the value of the continuous dividend

q (t)

at the

j

th time in the mesh, for

j = 1, 2, \dots, nt

tdpar (2) = .FALSE.

then

q (1)

must contain the constant value of the continuous dividend

q

. The remaining elements need not be set.

11: $sigma (*)$ – Real (Kind=nag_wp) array Input

Note: the dimension of the array sigma must be at least

nt

tdpar (3) = .TRUE.

, and at least

1

otherwise.

On entry: if

tdpar (3) = .TRUE.

then

sigma (j)

must contain the value of the volatility

σ (t)

at the

j

th time in the mesh, for

j = 1, 2, \dots, nt

tdpar (3) = .FALSE.

then

sigma (1)

must contain the constant value of the volatility

σ

. The remaining elements need not be set.

12: $alpha$ – Real (Kind=nag_wp) Input

On entry: the value of

λ

to be used in the time-stepping scheme. Typical values include:

$alpha = 0.0$: Explicit forward Euler scheme.
$alpha = 0.5$: Implicit Crank–Nicolson scheme.
$alpha = 1.0$: Implicit backward Euler scheme.

The value

0.5

gives second-order accuracy in time. Values greater than

0.5

give unconditional stability. Since

0.5

is at the limit of unconditional stability this value does not damp oscillations.

Suggested value:

alpha = 0.55

Constraint:

0.0 \leq alpha \leq 1.0

13: $ntkeep$ – Integer Input

On entry: the number of solutions to be stored in the time direction. The routine calculates the solution backwards from

t (nt)

t (1)

at all times in the mesh. These time solutions and the corresponding Greeks will be stored at times

t (i)

, for

i = 1, 2, \dots, ntkeep

, in the arrays f, theta, delta, gamma, lambda and rho. Other time solutions will be discarded. To store all time solutions set

ntkeep = nt

Constraint:

1 \leq ntkeep \leq nt

14: $f (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

On exit:

f (i, j)

, for

i = 1, 2, \dots, ns

and

j = 1, 2, \dots, ntkeep

, contains the value

f

of the option at the

i

th mesh point

s (i)

at time

t (j)

15: $theta (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

16: $delta (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

17: $gamma (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

18: $lambda (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

19: $rho (ldf, ntkeep)$ – Real (Kind=nag_wp) array Output

On exit: the values of various Greeks at the

i

th mesh point

s (i)

at time

t (j)

, as follows:

\begin{array}{l} theta (i, j) = \frac{\partial f}{\partial t}, & delta (i, j) = \frac{\partial f}{\partial S}, & gamma (i, j) = \frac{\partial^{2} f}{\partial S^{2}}, \\ lambda (i, j) = \frac{\partial f}{\partial σ}, & rho (i, j) = \frac{\partial f}{\partial r} . \end{array}

20: $ldf$ – Integer Input

On entry: the first dimension of the arrays f, theta, delta, gamma, lambda and rho as declared in the (sub)program from which d03ncf is called.

Constraint:

ldf \geq ns

21: $work (4 \times ns)$ – Real (Kind=nag_wp) array Workspace

22: $iwork (ns)$ – Integer array Workspace

23: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

−1

1

to set behaviour on detection of an error; these values have no effect when no error is detected.

A value of

0

causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of

−1

means that an error message is printed while a value of

1

means that it is not.

If halting is not appropriate, the value

−1

1

is recommended. If message printing is undesirable, then the value

1

is recommended. Otherwise, the value

0

is recommended. When the value $- 1$ or $1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

ifail = 0

−1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$: On entry, $alpha = ⟨ value ⟩$ .
Constraint: $alpha \leq 1.0$ .

On entry, $alpha = ⟨ value ⟩$ .
Constraint: $alpha \geq 0.0$ .

On entry, $kopt = ⟨ value ⟩$ .
Constraint: $kopt = 1$ , $2$ , $3$ or $4$ .

On entry, $ldf = ⟨ value ⟩$ and $ns = ⟨ value ⟩$ .
Constraint: $ldf \geq ns$ .

On entry, $mesh = ⟨ value ⟩$ .
Constraint: $mesh ='U'$ or $'C'$ .

On entry, $ns = ⟨ value ⟩$ .
Constraint: $ns \geq 2$ .

On entry, $nt = ⟨ value ⟩$ .
Constraint: $nt \geq 2$ .

On entry, $ntkeep = ⟨ value ⟩$ .
Constraint: $ntkeep \geq 1$ .

On entry, $ntkeep = ⟨ value ⟩$ and $nt = ⟨ value ⟩$ .
Constraint: $ntkeep \leq nt$ .

On entry, $s (1) = ⟨ value ⟩$ .
Constraint: $s (1) \geq 0.0$ .

On entry, $t (1) = ⟨ value ⟩$ .
Constraint: $t (1) \geq 0.0$ .

$ifail = 2$: On entry, $s (ns) = ⟨ value ⟩$ and $s (1) = ⟨ value ⟩$ .
Constraint: $s (ns) > s (1)$ .

On entry, $t (nt) = ⟨ value ⟩$ and $t (1) = ⟨ value ⟩$ .
Constraint: $t (nt) > t (1)$ .

$ifail = 3$: On entry, $s (⟨ value ⟩ + 1) \leq s (⟨ value ⟩)$ .
Constraint: when $mesh ='C'$ , $s (i) < s (i + 1)$ , for $i = 1, 2, \dots, ns - 1$ .

On entry, $t (⟨ value ⟩ + 1) \leq t (⟨ value ⟩)$ .
Constraint: when $mesh ='C'$ , $t (i) < t (i + 1)$ , for $i = 1, 2, \dots, nt - 1$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

The accuracy of the solution

f

and the various derivatives returned by the routine is dependent on the values of ns and nt supplied, the distribution of the mesh points, and the value of alpha chosen. For most choices of alpha the solution has a truncation error which is second-order accurate in

S

and first order accurate in

t

. For

alpha = 0.5

the truncation error is also second-order accurate in

t

The simplest approach to improving the accuracy is to increase the values of both ns and nt.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

d03ncf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

d03ncf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

9.1 Timing

Each time-step requires the construction and solution of a tridiagonal system of linear equations. To calculate each of the derivatives lambda and rho requires a repetition of the entire solution process. The time taken for a call to the routine is, therefore, proportional to

ns \times nt

9.2 Algorithmic Details

d03ncf solves equation (1) using a finite difference method. The solution is computed backwards in time from

t_{\max}

t_{\min}

using a

λ

scheme, which is implicit for all nonzero values of

λ

, and is unconditionally stable for values of

λ > 0.5

. For each time-step a tridiagonal system is constructed and solved to obtain the solution at the earlier time. For the explicit scheme (

λ = 0

) this tridiagonal system degenerates to a diagonal matrix and is solved trivially. For American options the solution at each time-step is inspected to check whether early exercise is beneficial, and amended accordingly.

To compute the arrays lambda and rho, which are derivatives of the stock value

f

with respect to the problem arguments

σ

and

r

respectively, the entire solution process is repeated with perturbed values of these arguments.

10 Example

This example, taken from Hull (1989), solves the one-dimensional Black–Scholes equation for valuation of a

5

-month American put option on a non-dividend-paying stock with an exercise price of $

50

. The risk-free interest rate is 10% per annum, and the stock volatility is 40% per annum.

A fully implicit backward Euler scheme is used, with a mesh of

20

stock price intervals and

10

time intervals.

d03nc: FL CL CPP AD PY MB

NAG FL Interfaced03ncf (dim1_​blackscholes_​fd)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Timing

9.2 Algorithmic Details

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
d03ncf (dim1_blackscholes_fd)