NAG CL Interface
s30nbc (opt_​heston_​greeks)

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1 Purpose

s30nbc computes the European option price given by Heston's stochastic volatility model together with its sensitivities (Greeks).

2 Specification

#include <nag.h>
void  s30nbc (Nag_OrderType order, Nag_CallPut option, Integer m, Integer n, const double x[], double s, const double t[], double sigmav, double kappa, double corr, double var0, double eta, double grisk, double r, double q, double p[], double delta[], double gamma[], double vega[], double theta[], double rho[], double vanna[], double charm[], double speed[], double zomma[], double vomma[], NagError *fail)
The function may be called by the names: s30nbc, nag_specfun_opt_heston_greeks or nag_heston_greeks.

3 Description

s30nbc computes the price and sensitivities of a European option using Heston's stochastic volatility model. The return on the asset price, S, is
dS S = (r-q) dt + vt d W t (1)  
and the instantaneous variance, vt, is defined by a mean-reverting square root stochastic process,
dvt = κ (η-vt) dt + σv vt d W t (2) ,  
where r is the risk free annual interest rate; q is the annual dividend rate; vt is the variance of the asset price; σv is the volatility of the volatility, vt; κ is the mean reversion rate; η is the long term variance. dWt(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).
Pcall = S e-qT - X e-rT 1π Re[ 0+i/2 +i/2 e-ikX¯ H^ (k,v,T) k2 - ik dk] , (1)
where X¯ = ln(S/X) + (r-q) T and
H^ (k,v,T) = exp( 2κη σv2 [tg -ln( 1-he-ξt 1-h )]+vtg[ 1-e-ξt 1-he-ξt ]) ,  
g = 12 (b-ξ) ,   h = b-ξ b+ξ ,   t = σv2 T/2 ,  
ξ = [b2+4 k2-ik σv2 ] 12 ,  
b = 2 σv2 [(1-γ+ik)ρσv+ κ2 - γ(1-γ) σv2 ]  
with t = σv2 T/2 . Here γ is the risk aversion parameter of the representative agent with 0γ1 and γ(1-γ) σv2 κ2 . 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.
Pput = Pcall + Xe-rT - S e-qT .  
Writing the expression for the price of a call option as
Pcall = Se-qT - Xe-rT 1π Re[ 0+i/2 +i/2 I(k,r,S,T,v)dk]  
then the sensitivities or Greeks can be obtained in the following manner,
Delta
Pcall S = e-qT + Xe-rT S 1π Re[ 0+i/2 +i/2 (ik)I(k,r,S,T,v)dk] ,  
Vega
P v = - X e-rT 1π Re[ 0-i/2 0+i/2 f2I(k,r,j,S,T,v)dk] ,  where ​ f2 = g [ 1 - e-ξt 1 - h e-ξt ] ,  
Rho
Pcall r = T X e-rT 1π Re[ 0+i/2 +i/2 (1+ik)I(k,r,S,T,v)dk] .  
The option price Pij=P(X=Xi,T=Tj) is computed for each strike price in a set Xi, i=1,2,,m, and for each expiry time in a set Tj, j=1,2,,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: order Nag_OrderType Input
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 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2: option Nag_CallPut Input
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 or Nag_Put.
3: m Integer Input
On entry: the number of strike prices to be used.
Constraint: m1.
4: n Integer Input
On entry: the number of times to expiry to be used.
Constraint: n1.
5: x[m] const double Input
On entry: x[i-1] must contain Xi, the ith strike price, for i=1,2,,m.
Constraint: x[i-1]z ​ and ​ x[i-1] 1 / z , where z = nag_real_safe_small_number , the safe range parameter, for i=1,2,,m.
6: s double Input
On entry: S, the price of the underlying asset.
Constraint: sz ​ and ​s1.0/z, where z=nag_real_safe_small_number, the safe range parameter.
7: t[n] const double Input
On entry: t[i-1] must contain Ti, the ith time, in years, to expiry, for i=1,2,,n.
Constraint: t[i-1]z, where z = nag_real_safe_small_number , the safe range parameter, for i=1,2,,n.
8: sigmav double Input
On entry: the volatility, σv, of the volatility process, vt. Note that a rate of 20% should be entered as 0.2.
Constraint: sigmav>0.0.
9: kappa double Input
On entry: κ, the long term mean reversion rate of the volatility.
Constraint: kappa>0.0.
10: corr double Input
On entry: the correlation between the two standard Brownian motions for the asset price and the volatility.
Constraint: -1.0corr1.0.
11: var0 double Input
On entry: the initial value of the variance, vt, of the asset price.
Constraint: var00.0.
12: eta double Input
On entry: η, the long term mean of the variance of the asset price.
Constraint: eta>0.0.
13: grisk double Input
On entry: the risk aversion parameter, γ, of the representative agent.
Constraint: 0.0grisk1.0 and grisk×(1-grisk)×sigmav×sigmavkappa×kappa.
14: r double 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: r0.0.
15: q double Input
On entry: q, the annual continuous yield rate. Note that a rate of 8% should be entered as 0.08.
Constraint: q0.0.
16: p[m×n] double Output
Note: where P(i,j) appears in this document, it refers to the array element
  • p[(j-1)×m+i-1] when order=Nag_ColMajor;
  • p[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: P(i,j) contains Pij, the option price evaluated for the strike price xi at expiry tj for i=1,2,,m and j=1,2,,n.
17: delta[m×n] double Output
Note: the (i,j)th element of the matrix is stored in
  • delta[(j-1)×m+i-1] when order=Nag_ColMajor;
  • delta[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: the m×n array delta contains the sensitivity, PS, of the option price to change in the price of the underlying asset.
18: gamma[m×n] double Output
Note: the (i,j)th element of the matrix is stored in
  • gamma[(j-1)×m+i-1] when order=Nag_ColMajor;
  • gamma[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: the m×n array gamma contains the sensitivity, 2PS2, of delta to change in the price of the underlying asset.
19: vega[m×n] double Output
Note: where VEGA(i,j) appears in this document, it refers to the array element
  • vega[(j-1)×m+i-1] when order=Nag_ColMajor;
  • vega[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: VEGA(i,j), contains the first-order Greek measuring the sensitivity of the option price Pij to change in the volatility of the underlying asset, i.e., Pij σ , for i=1,2,,m and j=1,2,,n.
20: theta[m×n] double Output
Note: where THETA(i,j) appears in this document, it refers to the array element
  • theta[(j-1)×m+i-1] when order=Nag_ColMajor;
  • theta[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: THETA(i,j), contains the first-order Greek measuring the sensitivity of the option price Pij to change in time, i.e., - Pij T , for i=1,2,,m and j=1,2,,n, where b=r-q.
21: rho[m×n] double Output
Note: where RHO(i,j) appears in this document, it refers to the array element
  • rho[(j-1)×m+i-1] when order=Nag_ColMajor;
  • rho[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: RHO(i,j), contains the first-order Greek measuring the sensitivity of the option price Pij to change in the annual risk-free interest rate, i.e., - Pij r , for i=1,2,,m and j=1,2,,n.
22: vanna[m×n] double Output
Note: where VANNA(i,j) appears in this document, it refers to the array element
  • vanna[(j-1)×m+i-1] when order=Nag_ColMajor;
  • vanna[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: VANNA(i,j), contains the second-order Greek measuring the sensitivity of the first-order Greek Δij to change in the volatility of the asset price, i.e., - Δij T = - 2 Pij Sσ , for i=1,2,,m and j=1,2,,n.
23: charm[m×n] double Output
Note: where CHARM(i,j) appears in this document, it refers to the array element
  • charm[(j-1)×m+i-1] when order=Nag_ColMajor;
  • charm[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: CHARM(i,j), contains the second-order Greek measuring the sensitivity of the first-order Greek Δij to change in the time, i.e., - Δij T = - 2 Pij ST , for i=1,2,,m and j=1,2,,n.
24: speed[m×n] double Output
Note: where SPEED(i,j) appears in this document, it refers to the array element
  • speed[(j-1)×m+i-1] when order=Nag_ColMajor;
  • speed[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: SPEED(i,j), contains the third-order Greek measuring the sensitivity of the second-order Greek Γij to change in the price of the underlying asset, i.e., - Γij S = - 3 Pij S3 , for i=1,2,,m and j=1,2,,n.
25: zomma[m×n] double Output
Note: where ZOMMA(i,j) appears in this document, it refers to the array element
  • zomma[(j-1)×m+i-1] when order=Nag_ColMajor;
  • zomma[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: ZOMMA(i,j), contains the third-order Greek measuring the sensitivity of the second-order Greek Γij to change in the volatility of the underlying asset, i.e., - Γij σ = - 3 Pij S2σ , for i=1,2,,m and j=1,2,,n.
26: vomma[m×n] double Output
Note: where VOMMA(i,j) appears in this document, it refers to the array element
  • vomma[(j-1)×m+i-1] when order=Nag_ColMajor;
  • vomma[(i-1)×n+j-1] when order=Nag_RowMajor.
On exit: VOMMA(i,j), contains the second-order Greek measuring the sensitivity of the first-order Greek Δij to change in the volatility of the underlying asset, i.e., - Δij σ = - 2 Pij σ2 , for i=1,2,,m and j=1,2,,n.
27: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ACCURACY
Solution cannot be computed accurately. Check values of input arguments.
NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_CONVERGENCE
Quadrature has not converged to the required accuracy. However, the result should be a reasonable approximation.
NE_INT
On entry, m=value.
Constraint: m1.
On entry, n=value.
Constraint: n1.
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_REAL
On entry, corr=value.
Constraint: |corr|1.0.
On entry, eta=value.
Constraint: eta>0.0.
On entry, grisk=value, sigmav=value and kappa=value.
Constraint: 0.0grisk1.0 and grisk×(1.0-grisk)×sigmav2kappa2.
On entry, kappa=value.
Constraint: kappa>0.0.
On entry, q=value.
Constraint: q0.0.
On entry, r=value.
Constraint: r0.0.
On entry, s=value.
Constraint: svalue and svalue.
On entry, sigmav=value.
Constraint: sigmav>0.0.
On entry, var0=value.
Constraint: var00.0.
NE_REAL_ARRAY
On entry, t[value]=value.
Constraint: t[i-1]value.
On entry, x[value]=value.
Constraint: x[i-1]value and x[i-1]value.

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 × |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.
s30nbc 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 function. 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 a European call using Heston's stochastic volatility model. The time to expiry is 1 year, the stock price is 100 and the strike price is 100. The risk-free interest rate is 2.5% per year, the volatility of the variance, σv, is 57.51% per year, the mean reversion parameter, κ, is 1.5768, the long term mean of the variance, η, is 0.0398 and 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.

10.1 Program Text

Program Text (s30nbce.c)

10.2 Program Data

Program Data (s30nbce.d)

10.3 Program Results

Program Results (s30nbce.r)