NAG Library Function Document

nag_bsm_greeks (s30abc)

nag_bsm_greeks (Nag_OrderType order, Nag_CallPut option, Integer m, Integer n, const double x[], double s, const double t[], double sigma, double r, double q, double p[], double delta[], double gamma[], double vega[], double theta[], double rho[], double crho[], double vanna[], double charm[], double speed[], double colour[], double zomma[], double vomma[], NagError *fail)

3 Description

nag_bsm_greeks (s30abc) computes the price of a European call (or put) 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, by the Black–Scholes–Merton formula (see Black and Scholes (1973) and Merton (1973)). The annual volatility,

σ

, risk-free interest rate,

r

, and dividend yield,

q

, must be supplied as input. For a given strike price,

X

, the price of a European call with underlying price,

S

, and time to expiry,

T

, is

P_{call} = S e^{- q T} Φ (d_{1}) - X e^{- r T} Φ (d_{2})

and the corresponding European put price is

P_{put} = X e^{- r T} Φ (- d_{2}) - S e^{- q T} Φ (- d_{1})

and where

Φ

denotes the cumulative Normal distribution function,

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

and

\begin{array}{l} d_{1} = \frac{\ln (S / X) + (r - q + σ^{2} / 2) T}{σ \sqrt{T}}, \\ d_{2} = d_{1} - σ \sqrt{T} . \end{array}

4 References

Black F and Scholes M (1973) The pricing of options and corporate liabilities Journal of Political Economy 81 637–654

Merton R C (1973) Theory of rational option pricing Bell Journal of Economics and Management Science 4 141–183

5 Arguments

1: order – Nag_OrderTypeInput

On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by

order = Nag_RowMajor

. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

or Nag_ColMajor.

2: option – Nag_CallPutInput

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

$option = Nag_Call$: A call. The holder has a right to buy.
$option = Nag_Put$: A put. The holder has a right to sell.

Constraint:

option = Nag_Call

Nag_Put

3: m – IntegerInput

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

Constraint:

m \geq 1

4: n – IntegerInput

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

Constraint:

n \geq 1

5: x[m] – const doubleInput

On entry:

x [i - 1]

must contain

X_{i}

, the

i

th strike price, for

i = 1, 2, \dots, m

Constraint:

x [i - 1] \geq z ​ and ​ x [i - 1] \leq 1 / z

, where

z = nag_real_safe_small_number

, the safe range parameter, for

i = 1, 2, \dots, m

6: s – doubleInput

On entry:

S

, the price of the underlying asset.

Constraint:

s \geq z ​ and ​ s \leq 1.0 / z

, where

z = nag_real_safe_small_number

, the safe range parameter.

7: t[n] – const doubleInput

On entry:

t [i - 1]

must contain

T_{i}

, the

i

th time, in years, to expiry, for

i = 1, 2, \dots, n

Constraint:

t [i - 1] \geq z

, where

z = nag_real_safe_small_number

, the safe range parameter, for

i = 1, 2, \dots, n

8: sigma – doubleInput

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

9: r – doubleInput

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

10: q – doubleInput

On entry:

q

, the annual continuous yield rate. Note that a rate of 8% should be entered as 0.08.

Constraint:

q \geq 0.0

11: p[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix

P

is stored in

$p [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$p [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array p contains the computed option prices.

12: delta[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$delta [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$delta [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

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[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$gamma [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$gamma [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

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[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$vega [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$vega [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array vega contains the sensitivity,

\frac{\partial P}{\partial σ}

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

15: theta[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$theta [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$theta [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array theta contains the sensitivity,

- \frac{\partial P}{\partial T}

, of the option price to change in the time to expiry of the option.

16: rho[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$rho [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$rho [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array rho contains the sensitivity,

\frac{\partial P}{\partial r}

, of the option price to change in the annual risk-free interest rate.

17: crho[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$crho [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$crho [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array crho containing the sensitivity,

\frac{\partial P}{\partial b}

, of the option price to change in the annual cost of carry rate,

b

, where

b = r - q

18: vanna[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$vanna [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$vanna [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array vanna contains the sensitivity,

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

, of vega to change in the price of the underlying asset or, equivalently, the sensitivity of delta to change in the volatility of the asset price.

19: charm[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$charm [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$charm [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array charm contains the sensitivity,

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

, of delta to change in the time to expiry of the option.

20: speed[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$speed [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$speed [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array speed contains the sensitivity,

\frac{\partial^{3} P}{\partial S^{3}}

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

21: colour[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$colour [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$colour [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array colour contains the sensitivity,

- \frac{\partial^{3} P}{\partial S^{2} \partial T}

, of gamma to change in the time to expiry of the option.

22: zomma[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$zomma [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$zomma [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array zomma contains the sensitivity,

\frac{\partial^{3} P}{\partial S^{2} \partial σ}

, of gamma to change in the volatility of the underlying asset.

23: vomma[ $m \times n$ ] – doubleOutput

Note: the

(i, j)

th element of the matrix is stored in

$vomma [(j - 1) \times m + i - 1]$ when $order = Nag_ColMajor$ ;
$vomma [(i - 1) \times n + j - 1]$ when $order = Nag_RowMajor$ .

On exit: the

m \times n

array vomma contains the sensitivity,

\frac{\partial^{2} P}{\partial σ^{2}}

, of vega to change in the volatility of the underlying asset.

24: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_INT: On entry, $m = 〈value〉$ .
Constraint: $m \geq 1$ .; On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NE_REAL: On entry, $q = 〈value〉$ .
Constraint: $q \geq 0.0$ .; On entry, $r = 〈value〉$ .
Constraint: $r \geq 0.0$ .; On entry, $s = 〈value〉$ .
Constraint: $s \geq 〈value〉$ and $s \leq 〈value〉$ .; On entry, $sigma = 〈value〉$ .
Constraint: $sigma > 0.0$ .
NE_REAL_ARRAY: On entry, $t [〈value〉] = 〈value〉$ .
Constraint: $t [i] \geq 〈value〉$ .; On entry, $x [〈value〉] = 〈value〉$ .
Constraint: $x [i] \geq 〈value〉$ and $x [i] \leq 〈value〉$ .

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 nag_cumul_normal (s15abc) and nag_erfc (s15adc)). An accuracy close to machine precision can generally be expected.

8 Further Comments

None.

9 Example

This example computes the price of a European put with a time to expiry of

0.7

years, a stock price of

55

and a strike price of

60

. The risk-free interest rate is

10 %

per year and the volatility is

30 %

per year.

NAG Library Function Documentnag_bsm_greeks (s30abc)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Further Comments

9 Example

9.1 Program Text

9.2 Program Data

9.3 Program Results

NAG Library Function Document

nag_bsm_greeks (s30abc)