nag_heston_price (s30nac) (PDF version)
s Chapter Contents
s Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_heston_price (s30nac)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_heston_price (s30nac) computes the European option price given by Heston's stochastic volatility model.

2  Specification

#include <nag.h>
#include <nags.h>
void  nag_heston_price (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[], NagError *fail)

3  Description

nag_heston_price (s30nac) computes the price 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. dWti, 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 d k , (1)
where X- = lnS/X + r-q T  and
H^ k,v,T = exp 2κη σv2 tg - ln 1-he-ξt 1-h + vt g 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 - γ1-γ σv2
with t = σv2 T/2 . Here γ is the risk aversion parameter of the representative agent with 0γ1 and γ1-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.

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 6 347–343 Review of Financial Studies
Kilin F (2006) Accelerating the calibration of stochastic volatility models MPRA Paper No. 2975 http://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:     orderNag_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:     optionNag_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 or Nag_Put.
3:     mIntegerInput
On entry: the number of strike prices to be used.
Constraint: m1.
4:     nIntegerInput
On entry: the number of times to expiry to be used.
Constraint: n1.
5:     x[m]const doubleInput
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:     sdoubleInput
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 doubleInput
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:     sigmavdoubleInput
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:     kappadoubleInput
On entry: κ, the long term mean reversion rate of the volatility.
Constraint: kappa>0.0.
10:   corrdoubleInput
On entry: the correlation between the two standard Brownian motions for the asset price and the volatility.
Constraint: -1.0corr1.0.
11:   var0doubleInput
On entry: the initial value of the variance, vt, of the asset price.
Constraint: var00.0.
12:   etadoubleInput
On entry: η, the long term mean of the variance of the asset price.
Constraint: eta>0.0.
13:   griskdoubleInput
On entry: the risk aversion parameter, γ, of the representative agent.
Constraint: 0.0grisk1.0 and grisk×1.0-grisk×sigmav×sigmavkappa×kappa.
14:   rdoubleInput
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:   qdoubleInput
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]doubleOutput
Note: the i,jth element of the matrix P is stored in
  • p[j-1×m+i-1] when order=Nag_ColMajor;
  • p[i-1×n+j-1] when order=Nag_RowMajor.
On exit: the m×n array p contains the computed option prices.
17:   failNagError *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_CONVERGENCE
Quadrature has not converged to the specified 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.
NE_REAL
On entry, corr=value.
Constraint: corr1.0.
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  Further Comments

None.

9  Example

This example computes the price of a European call using Heston's stochastic volatility model. The time to expiry is 6 months, the stock price is 100 and the strike price is 100. The risk-free interest rate is 5% per year, the volatility of the variance, σv, is 22.5% per year, the mean reversion parameter, κ, is 2.0, the long term mean of the variance, η, is 0.01 and the correlation between the volatility process and the stock price process, ρ, is 0.0. The risk aversion parameter, γ, is 1.0 and the initial value of the variance, var0, is 0.01.

9.1  Program Text

Program Text (s30nace.c)

9.2  Program Data

Program Data (s30nace.d)

9.3  Program Results

Program Results (s30nace.r)


nag_heston_price (s30nac) (PDF version)
s Chapter Contents
s Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012