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Chapter Contents
Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_specfun_opt_heston_greeks (s30nb)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_specfun_opt_heston_greeks (s30nb) computes the European option price given by Heston's stochastic volatility model together with its sensitivities (Greeks).

Syntax

[p, delta, gamma, vega, theta, rho, vanna, charm, speed, zomma, vomma, ifail] = s30nb(calput, x, s, t, sigmav, kappa, corr, var0, eta, grisk, r, q, 'm', m, 'n', n)
[p, delta, gamma, vega, theta, rho, vanna, charm, speed, zomma, vomma, ifail] = nag_specfun_opt_heston_greeks(calput, x, s, t, sigmav, kappa, corr, var0, eta, grisk, r, q, 'm', m, 'n', n)

Description

nag_specfun_opt_heston_greeks (s30nb) 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. 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.
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 d k  
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 f2 I 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=PX=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.

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 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

Parameters

Compulsory Input Parameters

1:     calput – string (length ≥ 1)
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' or 'P'.
2:     xm – double array
xi must contain Xi, the ith strike price, for i=1,2,,m.
Constraint: xiz ​ and ​ xi 1 / z , where z = x02am , the safe range parameter, for i=1,2,,m.
3:     s – double scalar
S, the price of the underlying asset.
Constraint: sz ​ and ​s1.0/z, where z=x02am, the safe range parameter.
4:     tn – double array
ti must contain Ti, the ith time, in years, to expiry, for i=1,2,,n.
Constraint: tiz, where z = x02am , the safe range parameter, for i=1,2,,n.
5:     sigmav – double scalar
The volatility, σv, of the volatility process, vt. Note that a rate of 20% should be entered as 0.2.
Constraint: sigmav>0.0.
6:     kappa – double scalar
κ, the long term mean reversion rate of the volatility.
Constraint: kappa>0.0.
7:     corr – double scalar
The correlation between the two standard Brownian motions for the asset price and the volatility.
Constraint: -1.0corr1.0.
8:     var0 – double scalar
The initial value of the variance, vt, of the asset price.
Constraint: var00.0.
9:     eta – double scalar
η, the long term mean of the variance of the asset price.
Constraint: eta>0.0.
10:   grisk – double scalar
The risk aversion parameter, γ, of the representative agent.
Constraint: 0.0grisk1.0 and grisk×1-grisk×sigmav×sigmavkappa×kappa.
11:   r – double scalar
r, the annual risk-free interest rate, continuously compounded. Note that a rate of 5% should be entered as 0.05.
Constraint: r0.0.
12:   q – double scalar
q, the annual continuous yield rate. Note that a rate of 8% should be entered as 0.08.
Constraint: q0.0.

Optional Input Parameters

1:     m int64int32nag_int scalar
Default: the dimension of the array x.
The number of strike prices to be used.
Constraint: m1.
2:     n int64int32nag_int scalar
Default: the dimension of the array t.
The number of times to expiry to be used.
Constraint: n1.

Output Parameters

1:     pldpn – double array
ldp=m.
pij contains Pij, the option price evaluated for the strike price xi at expiry tj for i=1,2,,m and j=1,2,,n.
2:     deltaldpn – double array
ldp=m.
The leading m×n part of the array delta contains the sensitivity, PS, of the option price to change in the price of the underlying asset.
3:     gammaldpn – double array
ldp=m.
The leading m×n part of the array gamma contains the sensitivity, 2PS2, of delta to change in the price of the underlying asset.
4:     vegaldpn – double array
ldp=m.
vegaij, 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.
5:     thetaldpn – double array
ldp=m.
thetaij, 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.
6:     rholdpn – double array
ldp=m.
rhoij, 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.
7:     vannaldpn – double array
ldp=m.
vannaij, 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.
8:     charmldpn – double array
ldp=m.
charmij, 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.
9:     speedldpn – double array
ldp=m.
speedij, 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.
10:   zommaldpn – double array
ldp=m.
zommaij, 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.
11:   vommaldpn – double array
ldp=m.
vommaij, 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.
12:   ifail int64int32nag_int scalar
ifail=0 unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

   ifail=1
On entry, calput=_ was an illegal value.
   ifail=2
Constraint: m1.
   ifail=3
Constraint: n1.
   ifail=4
Constraint: xi_ and xi_.
   ifail=5
Constraint: s_ and s_.
   ifail=6
Constraint: ti_.
   ifail=7
Constraint: sigmav>0.0.
   ifail=8
Constraint: kappa>0.0.
   ifail=9
Constraint: corr1.0.
   ifail=10
Constraint: var00.0.
   ifail=11
Constraint: eta>0.0.
   ifail=12
Constraint: 0.0grisk1.0 and grisk×1.0-grisk×sigmav2kappa2.
   ifail=13
Constraint: r0.0.
   ifail=14
Constraint: q0.0.
   ifail=16
Constraint: ldpm.
W  ifail=17
Quadrature has not converged to the required accuracy. However, the result should be a reasonable approximation.
W  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.
   ifail=-399
Your licence key may have expired or may not have been installed correctly.
   ifail=-999
Dynamic memory allocation failed.

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.

Further Comments

None.

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.
function s30nb_example


fprintf('s30nb example results\n\n');

calput = 'C';
s      = 100.0;
r      = 0.025;
q      = 0.0;
kappa  = 1.5768;
eta    = 0.0398;
var0   = 0.0175;
sigmav = 0.5751;
corr   = -0.5711;
grisk  = 1;
x      = [100.0];
t      = [1];


[p, delta, gamma, vega,  theta, rho, ...
    vanna, charm, speed, zomma, vomma, ifail] = ...
    s30nb(...
          calput, x, s, t, sigmav, kappa, corr, var0, eta, grisk, r, q);

fprintf('\nHeston''s Stochastic Volatility Model\n');
if calput == 'C' || calput == 'c'
  fprintf('European Call :\n');
else
  fprintf('European Put :\n');
end
fprintf(' Spot                   =  %9.4f\n', s);
fprintf(' Volatility of vol      =  %9.4f\n', sigmav);
fprintf(' Mean reversion         =  %9.4f\n', kappa);
fprintf(' Correlation            =  %9.4f\n', corr);
fprintf(' Variance               =  %9.4f\n', var0);
fprintf(' Mean of variance       =  %9.4f\n', eta);
fprintf(' Risk aversion          =  %9.4f\n', grisk);
fprintf(' Rate                   =  %9.4f\n', r);
fprintf(' Dividend               =  %9.4f\n\n', q);

for j=1:1
  fprintf('%8s%9s%9s%9s%9s%9s%9s\n','Strike','Price','Delta','Gamma',...
          'Vega','Theta','Rho');
  for i=1:1
    fprintf('%8.4f%9.4f%9.4f%9.4f%9.4f%9.4f%9.2f\n', x(i), p(i,j), ...
          delta(i,j), gamma(i,j), vega(i,j), theta(i,j), rho(i,j));
  end
  fprintf('\n%26s%9s%9s%9s%9s\n','Vanna','Charm','Speed','Zomma','Vomma');
  for i=1:1
    fprintf('%17s%9.4f%9.4f%9.4f%9.4f%9.2f\n', ' ', vanna(i,j), ...
            charm(i,j), speed(i,j), zomma(i,j), vomma(i,j));
  end
end


s30nb example results


Heston's Stochastic Volatility Model
European Call :
 Spot                   =   100.0000
 Volatility of vol      =     0.5751
 Mean reversion         =     1.5768
 Correlation            =    -0.5711
 Variance               =     0.0175
 Mean of variance       =     0.0398
 Risk aversion          =     1.0000
 Rate                   =     0.0250
 Dividend               =     0.0000

  Strike    Price    Delta    Gamma     Vega    Theta      Rho
100.0000   7.2743   0.6945   0.0251  52.5461  -4.9969    62.17

                     Vanna    Charm    Speed    Zomma    Vomma
                   -0.5643  -0.0321  -0.0023  -0.1976  -321.08

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