s30ndf computes the European option price given by Heston's stochastic volatility model together with its sensitivities (Greeks), including sensitivities with respect to model parameters, and allowing negative rates.

2 Specification

Fortran Interface

Subroutine s30ndf (

calput, m, n, x, s, t, sigmav, kappa, corr, var0, eta, grisk, r, q, p, ldp, delta, gamma, vega, theta, rho, vanna, charm, speed, zomma, vomma, dp_dx, dp_dq, dp_deta, dp_dkappa, dp_dsigmav, dp_dcorr, dp_dgrisk, ifail)

Integer, Intent (In)	::	m, n, ldp
Integer, Intent (Inout)	::	ifail
Real (Kind=nag_wp), Intent (In)	::	x(m), s, t(n), sigmav, kappa, corr, var0, eta, grisk, r(n), q(n)
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), zomma(ldp,n), vomma(ldp,n), dp_dx(ldp,n), dp_dq(ldp,n), dp_deta(ldp,n), dp_dkappa(ldp,n), dp_dsigmav(ldp,n), dp_dcorr(ldp,n), dp_dgrisk(ldp,n)
Character (1), Intent (In)	::	calput

C Header Interface

#include <nag.h>

void

s30ndf_ (const char *calput, const Integer *m, const Integer *n, const double x[], const double *s, const double t[], const double *sigmav, const double *kappa, const double *corr, const double *var0, const double *eta, const double *grisk, const double r[], const double q[], double p[], const Integer *ldp, double delta[], double gamma[], double vega[], double theta[], double rho[], double vanna[], double charm[], double speed[], double zomma[], double vomma[], double dp_dx[], double dp_dq[], double dp_deta[], double dp_dkappa[], double dp_dsigmav[], double dp_dcorr[], double dp_dgrisk[], Integer *ifail, const Charlen length_calput)

The routine may be called by the names s30ndf or nagf_specfun_opt_heston_more_greeks.

3 Description

s30ndf computes the price and sensitivities of a European option using Heston's stochastic volatility model. The return on the asset price,

S

, is

\frac{d S}{S} = (r - q) d t + \sqrt{v_{t}} d W_{t}^{(1)}

and the instantaneous variance,

v_{t}

, is defined by a mean-reverting square root stochastic process,

d v_{t} = κ (η - v_{t}) d t + σ_{v} \sqrt{v_{t}} d W_{t}^{(2)},

where

r

is the risk free annual interest rate;

q

is the annual dividend rate;

v_{t}

is the variance of the asset price;

σ_{v}

is the volatility of the volatility,

\sqrt{v_{t}}

;

κ

is the mean reversion rate;

η

is the long term variance.

d W_{t}^{(i)}

, for

i = 1, 2

, denotes two correlated standard Brownian motions with

ℂov [d W_{t}^{(1)}, d W_{t}^{(2)}] = ρ d t .

The option price is computed by evaluating the integral transform given by Lewis (2000) using the form of the characteristic function discussed by Albrecher et al. (2007), see also Kilin (2006).

P_{call} = S e^{- q T} - X e^{- r T} \frac{1}{π} Re [\int_{0 + i / 2}^{\infty + i / 2} e^{- i k \bar{X}} \frac{\hat{H} (k, v, T)}{k^{2} - i k} d k],

(1)

where

\bar{X} = \ln (S / X) + (r - q) T

and

\hat{H} (k, v, T) = \exp (\frac{2 κ η}{σ_{v}^{2}} [t g - \ln (\frac{1 - h e^{- ξ t}}{1 - h})] + v_{t} g [\frac{1 - e^{- ξ t}}{1 - h e^{- ξ t}}]),

g = \frac{1}{2} (b - ξ), h = \frac{b - ξ}{b + ξ}, t = σ_{v}^{2} T / 2,

ξ = {[b^{2} + 4 \frac{k^{2} - i k}{σ_{v}^{2}}]}^{\frac{1}{2}},

b = \frac{2}{σ_{v}^{2}} [(1 - γ + i k) ρ σ_{v} + \sqrt{κ^{2} - γ (1 - γ) σ_{v}^{2}}]

with

t = σ_{v}^{2} T / 2

. Here

γ

is the risk aversion parameter of the representative agent with

0 \leq γ \leq 1

and

γ (1 - γ) σ_{v}^{2} \leq κ^{2}

. The value

γ = 1

corresponds to

λ = 0

, where

λ

is the market price of risk in Heston (1993) (see Lewis (2000) and Rouah and Vainberg (2007)).

The price of a put option is obtained by put-call parity.

P_{put} = P_{call} + X e^{- r T} - S e^{- q T} .

Writing the expression for the price of a call option as

P_{call} = S e^{- q T} - X e^{- r T} \frac{1}{π} Re [\int_{0 + i / 2}^{\infty + i / 2} I (k, r, S, T, v) d k]

then the sensitivities or Greeks can be obtained in the following manner,

Delta

\frac{\partial P_{call}}{\partial S} = e^{- q T} + \frac{X e^{- r T}}{S} \frac{1}{π} Re [\int_{0 + i / 2}^{\infty + i / 2} (i k) I (k, r, S, T, v) d k],

Vega

\frac{\partial P}{\partial v} = - X e^{- r T} \frac{1}{π} Re [\int_{0 - i / 2}^{0 + i / 2} f_{2} I (k, r, j, S, T, v) d k],  where ​ f_{2} = g [\frac{1 - e^{- ξ t}}{1 - h e^{- ξ t}}],

Rho

\frac{\partial P_{call}}{\partial r} = T X e^{- r T} \frac{1}{π} Re [\int_{0 + i / 2}^{\infty + i / 2} (1 + i k) I (k, r, S, T, v) d k] .

The option price

P_{i j} = P (X = X_{i}, T = T_{j}, r = r_{j}, q = q_{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

. The continuously compounded annual risk-free interest rate used is

r_{j}

j = 1, 2, \dots, n

, and the annual continuous yield rate used is

q_{j}

j = 1, 2, \dots, n

4 References

Albrecher H, Mayer P, Schoutens W and Tistaert J (2007) The little Heston trap Wilmott Magazine January 2007 83–92

Heston S (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options Review of Financial Studies 6 327–343

Kilin F (2006) Accelerating the calibration of stochastic volatility models MPRA Paper No. 2975 https://mpra.ub.uni-muenchen.de/2975/

Lewis A L (2000) Option valuation under stochastic volatility Finance Press, USA

Rouah F D and Vainberg G (2007) Option Pricing Models and Volatility using Excel-VBA John Wiley and Sons, Inc

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: $sigmav$ – Real (Kind=nag_wp) Input

On entry: the volatility,

σ_{v}

, of the volatility process,

\sqrt{v_{t}}

. Note that a rate of 20% should be entered as

0.2

Constraint:

sigmav > 0.0

8: $kappa$ – Real (Kind=nag_wp) Input

On entry:

κ

, the long term mean reversion rate of the volatility.

Constraint:

kappa > 0.0

9: $corr$ – Real (Kind=nag_wp) Input

On entry: the correlation between the two standard Brownian motions for the asset price and the volatility.

Constraint:

- 1.0 \leq corr \leq 1.0

10: $var0$ – Real (Kind=nag_wp) Input

On entry: the initial value of the variance,

v_{t}

, of the asset price.

Constraint:

var0 \geq 0.0

11: $eta$ – Real (Kind=nag_wp) Input

On entry:

η

, the long term mean of the variance of the asset price.

Constraint:

eta > 0.0

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

On entry: the risk aversion parameter,

γ

, of the representative agent.

Constraint:

0.0 \leq grisk \leq 1.0

and

grisk \times (1 - grisk) \times sigmav \times sigmav \leq kappa \times kappa

13: $r (n)$ – Real (Kind=nag_wp) array Input

On entry:

r (i)

must contain

R_{i}

, the

i

th annual risk-free interest rate, continuously compounded, for

i = 1, 2, \dots, n

. Note that a rate of 5% should be entered as

0.05

14: $q (n)$ – Real (Kind=nag_wp) array Input

On entry:

q (i)

must contain

Q_{i}

, the

i

th annual continuous yield rate, for

i = 1, 2, \dots, n

. Note that a rate of 8% should be entered as

0.08

15: $p (ldp, n)$ – 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

16: $ldp$ – Integer Input

On entry: the first dimension of the arrays p, delta, gamma, vega, theta, rho, vanna, charm, speed, zomma, vomma, dp_dx, dp_dq, dp_deta, dp_dkappa, dp_dsigmav, dp_dcorr and dp_dgrisk as declared in the (sub)program from which s30ndf is called.

Constraint:

ldp \geq m

17: $delta (ldp, n)$ – 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.

18: $gamma (ldp, n)$ – 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.

19: $vega (ldp, n)$ – 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 v_{t}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

20: $theta (ldp, n)$ – 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

21: $rho (ldp, n)$ – 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_{i}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

22: $vanna (ldp, n)$ – 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 σ} = \frac{\partial^{2} P_{i j}}{\partial S \partial σ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

23: $charm (ldp, n)$ – 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

24: $speed (ldp, n)$ – 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

25: $zomma (ldp, n)$ – 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

26: $vomma (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

vomma (i, j)

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

P_{i j}

to second-order changes in the volatility of the underlying asset, i.e.,

\frac{\partial^{2} P_{i j}}{\partial σ^{2}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

27: $dp_dx (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dx (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the strick price,

X_{i}

, i.e.,

\frac{\partial P_{i j}}{\partial X_{i}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

28: $dp_dq (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dq (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the annual continuous yield rate,

q_{j}

, i.e.,

\frac{\partial P_{i j}}{\partial q_{j}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

29: $dp_deta (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_deta (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the long term mean of the variance

η

of the asset price, i.e.,

\frac{\partial P_{i j}}{\partial η}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

30: $dp_dkappa (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dkappa (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the long term mean reversion rate

κ

of the volatility, i.e.,

\frac{\partial P_{i j}}{\partial κ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

31: $dp_dsigmav (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dsigmav (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the volatility

σ_{v}

of the volatility process,

\sqrt{v_{t}}

, i.e.,

\frac{\partial P_{i j}}{\partial σ_{v}}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

32: $dp_dcorr (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dcorr (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the correlation

ρ

between the two standard Brownian motions for the asset price and the volatility, i.e.,

\frac{\partial P_{i j}}{\partial ρ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

33: $dp_dgrisk (ldp, n)$ – Real (Kind=nag_wp) array Output

On exit:

dp_dgrisk (i, j)

, contains the derivative measuring the sensitivity of the option price

P_{i j}

to change in the risk aversion parameter

γ

of the representative agent,, i.e.,

\frac{\partial P_{i j}}{\partial γ}

, for

i = 1, 2, \dots, m

and

j = 1, 2, \dots, n

34: $ifail$ – 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, $sigmav = ⟨ value ⟩$ .
Constraint: $sigmav > 0.0$ .

$ifail = 8$: On entry, $kappa = ⟨ value ⟩$ .
Constraint: $kappa > 0.0$ .

$ifail = 9$: On entry, $corr = ⟨ value ⟩$ .
Constraint: $| corr | \leq 1.0$ .

$ifail = 10$: On entry, $var0 = ⟨ value ⟩$ .
Constraint: $var0 \geq 0.0$ .

$ifail = 11$: On entry, $eta = ⟨ value ⟩$ .
Constraint: $eta > 0.0$ .

$ifail = 12$: On entry, $grisk = ⟨ value ⟩$ , $sigmav = ⟨ value ⟩$ and $kappa = ⟨ value ⟩$ .
Constraint: $0.0 \leq grisk \leq 1.0$ and $grisk \times (1.0 - grisk) \times {sigmav}^{2} \leq {kappa}^{2}$ .

$ifail = 16$: On entry, $ldp = ⟨ value ⟩$ and $m = ⟨ value ⟩$ .
Constraint: $ldp \geq m$ .

$ifail = 17$: Quadrature has not converged to the required accuracy. However, the result should be a reasonable approximation.

$ifail = 18$: Solution cannot be computed accurately. Check values of input arguments.

$ifail = - 99$: 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 determined by the accuracy of the numerical quadrature used to evaluate the integral in (1). An adaptive method is used which evaluates the integral to within a tolerance of

\max (10^{- 8}, 10^{- 10} \times | I |)

, where

| I |

is the absolute value of the integral.

8 Parallelism and Performance

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

s30ndf 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 and sensitivities of four European calls using Heston's stochastic volatility model. In each case, the time to expiry is

1

year, the stock price is

100

, the strike price is

100

, the volatility of the variance (

σ_{v}

) is

57.51 %

per year, the mean reversion parameter (

κ

) is

1.5768

, the long term mean of the variance (

η

) is

0.0398

, the correlation between the volatility process and the stock price process (

ρ

) is

- 0.5711

, the risk aversion parameter (

γ

) is

1.0

and the initial value of the variance (var0) is

0.0175

. The risk-free interest rate values for each call are

2.5 %

2.5 %

- 2.5 %

- 2.5 %

per year. The annual continuous yield rate values are

1 %

- 1 %

1 %

- 1 %

per year.

s30nd: FL CL CPP AD PY MB

NAG FL Interfaces30ndf (opt_​heston_​more_​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
s30ndf (opt_heston_more_greeks)