NAG Library Routine Document

Integer, Intent (In)	::	m, n, ldp
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(m), s, t(n), sigma, r, b
Real (Kind=nag_wp), Intent (Inout)	::	p(ldp,n), delta(ldp,n), gamma(ldp,n), vega(ldp,n), theta(ldp,n), rho(ldp,n), crho(ldp,n), vanna(ldp,n), charm(ldp,n), speed(ldp,n), colour(ldp,n), zomma(ldp,n), vomma(ldp,n)
Character (1), Intent (In)	::	calput

C Header Interface

#include <nagmk26.h>

void

s30sbf_ (const char *calput, const Integer *m, const Integer *n, const double x[], const double *s, const double t[], const double *sigma, const double *r, const double *b, double p[], const Integer *ldp, double delta[], double gamma[], double vega[], double theta[], double rho[], double crho[], double vanna[], double charm[], double speed[], double colour[], double zomma[], double vomma[], Integer *ifail, const Charlen length_calput)

3

Description

s30sbf computes the price of an Asian geometric continuous average-rate option, together with the Greeks or sensitivities, which are the partial derivatives of the option price with respect to certain of the other input parameters. The annual volatility,

σ

, risk-free rate,

r

, and cost of carry,

b

, are constants (see Kemna and Vorst (1990)). For a given strike price,

X

, the price of a call option with underlying price,

S

, and time to expiry,

T

, is

P_{call} = S e^{(\bar{b} - r) T} Φ ({\bar{d}}_{1}) - X e^{- r T} Φ ({\bar{d}}_{2}),

and the corresponding put option price is

P_{put} = X e^{- r T} Φ (- {\bar{d}}_{2}) - S e^{(\bar{b} - r) T} Φ (- {\bar{d}}_{1}),

where

{\bar{d}}_{1} = \frac{\ln (S / X) + (\bar{b} + {\bar{σ}}^{2} / 2) T}{\bar{σ} \sqrt{T}}

and

{\bar{d}}_{2} = {\bar{d}}_{1} - \bar{σ} \sqrt{T},

with

\bar{σ} = \frac{σ}{\sqrt{3}}, \bar{b} = \frac{1}{2} (b - \frac{σ^{2}}{6}) .

Φ

is the cumulative Normal distribution function,

Φ (x) = \frac{1}{\sqrt{2 π}} \int_{- \infty}^{x} \exp (- y^{2} / 2) d y .

The option price

P_{i j} = P (X = X_{i}, T = T_{j})

is computed for each strike price in a set

X_{i}

i = 1, 2, \dots, m

, and for each expiry time in a set

T_{j}

j = 1, 2, \dots, n

4

References

Kemna A and Vorst A (1990) A pricing method for options based on average asset values Journal of Banking and Finance 14 113–129

5

Arguments

1: $calput$ – Character(1)Input

On entry: determines whether the option is a call or a put.

$calput ='C'$: A call; the holder has a right to buy.
$calput ='P'$: A put; the holder has a right to sell.

Constraint:

calput ='C'

'P'

2: $m$ – IntegerInput

On entry: the number of strike prices to be used.

Constraint:

m \geq 1

3: $n$ – IntegerInput

On entry: the number of times to expiry to be used.

Constraint:

n \geq 1

4: $x (m)$ – Real (Kind=nag_wp) arrayInput

On entry:

x (i)

must contain

X_{i}

, the

i

th strike price, for

i = 1, 2, \dots, m

Constraint:

x (i) \geq z ​ and ​ x (i) \leq 1 / z

, where

z = x02amf ()

, the safe range parameter, for

i = 1, 2, \dots, m

5: $s$ – Real (Kind=nag_wp)Input

On entry:

S

, the price of the underlying asset.

Constraint:

s \geq z ​ and ​ s \leq 1.0 / z

, where

z = x02amf ()

, the safe range parameter.

6: $t (n)$ – Real (Kind=nag_wp) arrayInput

On entry:

t (i)

must contain

T_{i}

, the

i

th time, in years, to expiry, for

i = 1, 2, \dots, n

Constraint:

t (i) \geq z

, where

z = x02amf ()

, the safe range parameter, for

i = 1, 2, \dots, n

7: $sigma$ – Real (Kind=nag_wp)Input

On entry:

σ

, the volatility of the underlying asset. Note that a rate of 15% should be entered as

0.15

Constraint:

sigma > 0.0

8: $r$ – Real (Kind=nag_wp)Input

On entry:

r

, the annual risk-free interest rate, continuously compounded. Note that a rate of 5% should be entered as

0.05

Constraint:

r \geq 0.0

9: $b$ – Real (Kind=nag_wp)Input

On entry:

b

, the annual cost of carry rate. Note that a rate of 8% should be entered as

0.08

10: $p (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

p (i, j)

contains

P_{i j}

, the option price evaluated for the strike price

x_{i}

at expiry

t_{j}

for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

11: $ldp$ – IntegerInput

On entry: the first dimension of the arrays p, delta, gamma, vega, theta, rho, crho, vanna, charm, speed, colour, zomma and vomma as declared in the (sub)program from which s30sbf is called.

Constraint:

ldp \geq m

12: $delta (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit: the leading

m \times n

part of the array delta contains the sensitivity,

\frac{\partial P}{\partial S}

, of the option price to change in the price of the underlying asset.

13: $gamma (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit: the leading

m \times n

part of the array gamma contains the sensitivity,

\frac{\partial^{2} P}{\partial S^{2}}

, of delta to change in the price of the underlying asset.

14: $vega (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

vega (i, j)

, contains the first-order Greek measuring the sensitivity of the option price

P_{i j}

to change in the volatility of the underlying asset, i.e.,

\frac{\partial P_{i j}}{\partial σ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

15: $theta (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

theta (i, j)

, contains the first-order Greek measuring the sensitivity of the option price

P_{i j}

to change in time, i.e.,

- \frac{\partial P_{i j}}{\partial T}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

, where

b = r - q

16: $rho (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

rho (i, j)

, contains the first-order Greek measuring the sensitivity of the option price

P_{i j}

to change in the annual risk-free interest rate, i.e.,

- \frac{\partial P_{i j}}{\partial r}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

17: $crho (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

delta (i, j)

, contains the first-order Greek measuring the sensitivity of the option price

P_{i j}

to change in the price of the underlying asset, i.e.,

- \frac{\partial P_{i j}}{\partial S}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

18: $vanna (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

vanna (i, j)

, contains the second-order Greek measuring the sensitivity of the first-order Greek

Δ_{i j}

to change in the volatility of the asset price, i.e.,

- \frac{\partial Δ_{i j}}{\partial T} = - \frac{\partial^{2} P_{i j}}{\partial S \partial σ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

19: $charm (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

charm (i, j)

, contains the second-order Greek measuring the sensitivity of the first-order Greek

Δ_{i j}

to change in the time, i.e.,

- \frac{\partial Δ_{i j}}{\partial T} = - \frac{\partial^{2} P_{i j}}{\partial S \partial T}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

20: $speed (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

speed (i, j)

, contains the third-order Greek measuring the sensitivity of the second-order Greek

Γ_{i j}

to change in the price of the underlying asset, i.e.,

- \frac{\partial Γ_{i j}}{\partial S} = - \frac{\partial^{3} P_{i j}}{\partial S^{3}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

21: $colour (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

colour (i, j)

, contains the third-order Greek measuring the sensitivity of the second-order Greek

Γ_{i j}

to change in the time, i.e.,

- \frac{\partial Γ_{i j}}{\partial T} = - \frac{\partial^{3} P_{i j}}{\partial S \partial T}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

22: $zomma (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

zomma (i, j)

, contains the third-order Greek measuring the sensitivity of the second-order Greek

Γ_{i j}

to change in the volatility of the underlying asset, i.e.,

- \frac{\partial Γ_{i j}}{\partial σ} = - \frac{\partial^{3} P_{i j}}{\partial S^{2} \partial σ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

23: $vomma (ldp, n)$ – Real (Kind=nag_wp) arrayOutput

On exit:

vomma (i, j)

, contains the second-order Greek measuring the sensitivity of the first-order Greek

Δ_{i j}

to change in the volatility of the underlying asset, i.e.,

- \frac{\partial Δ_{i j}}{\partial σ} = - \frac{\partial^{2} P_{i j}}{\partial σ^{2}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

24: $ifail$ – IntegerInput/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 or 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this argument, the recommended value is

0

. 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, $calput = 〈value〉$ was an illegal value.

$ifail = 2$: On entry, $m = 〈value〉$ .
Constraint: $m \geq 1$ .

$ifail = 3$: On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .

$ifail = 4$: On entry, $x (〈value〉) = 〈value〉$ .
Constraint: $x (i) \geq 〈value〉$ and $x (i) \leq 〈value〉$ .

$ifail = 5$: On entry, $s = 〈value〉$ .
Constraint: $s \geq 〈value〉$ and $s \leq 〈value〉$ .

$ifail = 6$: On entry, $t (〈value〉) = 〈value〉$ .
Constraint: $t (i) \geq 〈value〉$ .

$ifail = 7$: On entry, $sigma = 〈value〉$ .
Constraint: $sigma > 0.0$ .

$ifail = 8$: On entry, $r = 〈value〉$ .
Constraint: $r \geq 0.0$ .

$ifail = 11$: On entry, $ldp = 〈value〉$ and $m = 〈value〉$ .
Constraint: $ldp \geq m$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

The accuracy of the output is dependent on the accuracy of the cumulative Normal distribution function,

Φ

. This is evaluated using a rational Chebyshev expansion, chosen so that the maximum relative error in the expansion is of the order of the machine precision (see s15abf and s15adf). An accuracy close to machine precision can generally be expected.

8

Parallelism and Performance

s30sbf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

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

None.

10

Example

This example computes the price of an Asian geometric continuous average-rate call with a time to expiry of

3

months, a stock price of

80

and a strike price of

97

. The risk-free interest rate is

5 %

per year, the cost of carry is

8 %

and the volatility is

20 %

per year.

NAG Library Routine Document

s30sbf (opt_asian_geom_greeks)

▸▿ 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

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

6

Error Indicators and Warnings

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

10.2

Program Data

10.3

Program Results