Integer, Intent (In)	::	m, n, ldp
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(m), s, t(n), sigma, r, lambda, jvol
Real (Kind=nag_wp), Intent (Inout)	::	p(ldp,n), delta(ldp,n), gamma(ldp,n), vega(ldp,n), theta(ldp,n), rho(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 <nag.h>

void

s30jbf_ (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 *lambda, const double *jvol, double p[], const Integer *ldp, double delta[], double gamma[], double vega[], double theta[], double rho[], double vanna[], double charm[], double speed[], double colour[], double zomma[], double vomma[], Integer *ifail, const Charlen length_calput)

The routine may be called by the names s30jbf or nagf_specfun_opt_jumpdiff_merton_greeks.

3 Description

s30jbf uses Merton's jump-diffusion model (Merton (1976)) to compute the price of a European 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. Merton's model assumes that the asset price is described by a Brownian motion with drift, as in the Black–Scholes–Merton case, together with a compound Poisson process to model the jumps. The corresponding stochastic differential equation is,

\frac{d S}{S} = (α - λ k) d t + \hat{σ} {d W}_{t} + {d q}_{t} .

Here

α

is the instantaneous expected return on the asset price,

S

;

{\hat{σ}}^{2}

is the instantaneous variance of the return when the Poisson event does not occur;

{d W}_{t}

is a standard Brownian motion;

q_{t}

is the independent Poisson process and

k = E [Y - 1]

where

Y - 1

is the random variable change in the stock price if the Poisson event occurs and

E

is the expectation operator over the random variable

Y

This leads to the following price for a European option (see Haug (2007))

P_{call} = \sum_{j = 0}^{\infty} \frac{e^{- λ T} {(λ T)}^{j}}{j!} C_{j} (S, X, T, r, σ_{j}^{'}),

where

T

is the time to expiry;

X

is the strike price;

r

is the annual risk-free interest rate;

C_{j} (S, X, T, r, σ_{j}^{'})

is the Black–Scholes–Merton option pricing formula for a European call (see s30aaf).

\begin{array}{l} σ_{j}^{'} = \sqrt{z^{2} + δ^{2} (\frac{j}{T})}, \\ z^{2} = σ^{2} - λ δ^{2}, \\ δ^{2} = \frac{γ σ^{2}}{λ}, \end{array}

where

σ

is the total volatility including jumps;

λ

is the expected number of jumps given as an average per year;

γ

is the proportion of the total volatility due to jumps.

The value of a put is obtained by substituting the Black–Scholes–Merton put price for

C_{j} (S, X, T, r, σ_{j}^{'})

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

Haug E G (2007) The Complete Guide to Option Pricing Formulas (2nd Edition) McGraw-Hill

Merton R C (1976) Option pricing when underlying stock returns are discontinuous Journal of Financial Economics 3 125–144

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: $m$ – Integer Input

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

Constraint:

m \geq 1

3: $n$ – Integer Input

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

Constraint:

n \geq 1

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

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) array Input

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 annual total volatility, including jumps.

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: $lambda$ – Real (Kind=nag_wp) Input

On entry:

λ

, the number of expected jumps per year.

Constraint:

lambda > 0.0

10: $jvol$ – Real (Kind=nag_wp) Input

On entry: the proportion of the total volatility associated with jumps.

Constraint:

0.0 \leq jvol < 1.0

11: $p (ldp, n)$ – Real (Kind=nag_wp) array Output

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

12: $ldp$ – Integer Input

On entry: the first dimension of the arrays p, delta, gamma, vega, theta, rho, vanna, charm, speed, colour, zomma and vomma as declared in the (sub)program from which s30jbf is called.

Constraint:

ldp \geq m

13: $delta (ldp, n)$ – Real (Kind=nag_wp) array Output

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.

14: $gamma (ldp, n)$ – Real (Kind=nag_wp) array Output

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.

15: $vega (ldp, n)$ – Real (Kind=nag_wp) array Output

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

16: $theta (ldp, n)$ – Real (Kind=nag_wp) array Output

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

17: $rho (ldp, n)$ – Real (Kind=nag_wp) array Output

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

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

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) array Output

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) array Output

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) array Output

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) array Output

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) array Output

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: $ifail$ – 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, $calput = ⟨ value ⟩$ 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: $r \geq 0.0$ .

$ifail = 9$: On entry, $lambda = ⟨ value ⟩$ .
Constraint: $lambda > 0.0$ .

$ifail = 10$: On entry, $jvol = ⟨ value ⟩$ .
Constraint: $jvol \geq 0.0$ and $jvol < 1.0$ .

$ifail = 12$: 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 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 output is dependent on the accuracy of the cumulative Normal distribution function,

Φ

, occurring in

C_{j}

. 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

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

s30jbf 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 two European calls with jumps. The time to expiry is

6

months, the stock price is

100

and strike prices are

80

and

90

respectively. The number of jumps per year is

5

and the percentage of the total volatility due to jumps is

25 %

. The risk-free interest rate is

8 %

per year while the total volatility is

25 %

per year.

s30jb: FL CL CPP AD PY MB

NAG FL Interfaces30jbf (opt_​jumpdiff_​merton_​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

NAG FL Interface
s30jbf (opt_jumpdiff_merton_greeks)