//Fonction Psi(theta_n,Ztilde_n+1) = Y_n+1.
public double FonctionPSI_(double K, double theta)
{
this.K_ = K;
double ratio1 = 0.0;
double Z = LoiNormal.random_normal_parBoxMuller(rnd);
List <double> ST = new List <double>();
for (int i = 0; i < S0_.Count; i++)
{
ST.Add(S0_[i] * Math.Exp((r_ - Math.Pow(Sigma_[i], 2) / 2) * t_ + Sigma_[i] * Math.Pow(t_, 0.50) * Z));
}
if (this.op_ == type_.Call)
{
ratio1 = Math.Exp(-r_ * t_) * Math.Pow(Math.Max(ST.Average() - K_, 0.0), 2); //attention Math.Exp(-2*r_ * t_)
}
else if (this.op_ == type_.Put)
{
ratio1 = Math.Exp(-r_ * t_) * Math.Pow(Math.Max(K_ - ST.Average(), 0.0), 2); //attention Math.Exp(-2*r_ * t_)
}
else
{
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: " + this.op_);
}
double ratio2 = (theta - Z) * Math.Exp(-0.5 * theta * (2 * Z - theta));
double Psi = ratio1 * ratio2;
return(Psi);
}
/******************************************************************************
* ************ Valeur d'une option basket avec la méthode de réduction *******
**************** de la variance Robbins Monro (Arouna) *******************
* ****************************************************************************/
public double[] MC_RM_ArounaBasketOptionVal(type_ op)
{
//RobbinsMonroAlgorithme RM;
List <double> Payoff_;
double[,] ST_;
double[] res = new double[2]; //Tableau résultat
Payoff_ = new List <double>();
ST_ = new double[NSim_, S0_.Count];
double NormBoxMuller;
switch (op)
{
case type_.Call:
for (int j = 0; j < NSim_; j++)
{
double som = 0;
for (int i = 0; i < S0_.Count; i++)
{
NormBoxMuller = LoiNormal.random_normal_parBoxMuller(rnd);
ST_[j, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * NormBoxMuller);
if (j < NSim_ - 1)
{
ST_[j + 1, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * NormBoxMuller);
}
som += ST_[j, i];
}
j++;
Payoff_.Add(Math.Exp(-r_ * t_) * Math.Max((som / S0_.Count - K_), 0.0));
}
break;
case type_.Put:
for (int j = 0; j < NSim_; j++)
{
double som = 0;
for (int i = 0; i < S0_.Count; i++)
{
NormBoxMuller = LoiNormal.random_normal_parBoxMuller(rnd);
ST_[j, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * NormBoxMuller);
if (j < NSim_ - 1)
{
ST_[j + 1, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * NormBoxMuller);
}
som += ST_[j, i];
}
j++;
Payoff_.Add(Math.Exp(-r_ * t_) * Math.Max((K_ - som / S0_.Count), 0.0));
}
break;
default:
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: " + op);
}
res[0] = Payoff_.Average(); //La valeur de l'option, Esperance des payoff actualisés
res[1] = Math.Sqrt(Variance(Payoff_, res[0], 0, Payoff_.Count)); //L'ecarte type de simulation,
return(res);
}
//Algorithme de robbinsMonro
public double ThetaOptimalRMArouna(double K, double Theta0, int Niter)
{
double x1 = -2;
double x2 = 2;
List <int> rho_ = new List <int>();
List <double> u_ = new List <double>();
List <double> Theta_ = new List <double>();
List <double> y_ = new List <double>();
List <double> gamma_ = new List <double>();
Theta_.Add(Theta0);
double x = LoiNormal.random_normal_parBoxMuller(rnd);
y_.Add(FonctionPSI_(K_, Theta_[0]));
rho_.Add(0);
gamma_.Add(1);
u_.Add(50);
for (int i = 1; i <= Niter + 1; i++)
{
gamma_.Add(1 / (double)(i + 1)); //gamma_n = a/(b+n)
u_.Add(Math.Sqrt((1 / 6) * Math.Log((double)i + 1)) + u_[0]); //U_n = sqrt(1/6*ln(n))+U_0
}
for (int i = 0; i <= Niter; i++)
{
rho_.Add(0);
y_.Add(FonctionPSI_(K, Theta_[i]));
double xn = 0;
if (i >= 1)
{
rho_[i] = rho_[i - 1];
if (Math.Abs(Theta_[i - 1] - gamma_[i] * y_[i]) > u_[rho_[i - 1]])
{
rho_[i] = rho_[i] + 1;
}
}
if (rho_[i] % 2 == 0)
{
xn = x1;
}
else
{
xn = x2;
}
if (Math.Abs(Theta_[i] - gamma_[i + 1] * y_[i + 1]) <= u_[rho_[i]])
{
Theta_.Add(Theta_[i] - gamma_[i + 1] * y_[i + 1]);
}
else
{
Theta_.Add(xn);
}
}
return(Theta_[Niter - 1]);
}
//Calcul de la valeur d'une option basket en utilisant l'algorithme de RM pour la réduction de la variance
public double[] MC_RM_BasketOptionVal(double Theta, double K)
{
List <double> Payoff_;
double[,] ST_;
List <double> Z;
double Ztilde = LoiNormal.random_normal_parBoxMuller(rnd) + Theta;
Payoff_ = new List <double>(); //Payoff de l'option à chaque trajectoire
ST_ = new double[NSim_, S0_.Count]; //ST simulées
Z = new List <double>(); //
double[] res = new double[2]; //Tableau résultat
if (this.op_ == type_.Call)
{
for (int j = 0; j < NSim_; j++)
{
double som = 0;
for (int i = 0; i < S0_.Count; i++)
{
Ztilde = LoiNormal.random_normal_parBoxMuller(rnd) + Theta;
ST_[j, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * Ztilde);
som += ST_[j, i];
}
Payoff_.Add(Math.Max((som / S0_.Count - K_), 0.0));
Z.Add(Math.Exp(-r_ * t_) * (Math.Exp(0.5 * Math.Pow(Theta, 2) - Theta * Ztilde)) * Payoff_[j]);
}
}
else if (this.op_ == type_.Put)
{
for (int j = 0; j < NSim_; j++)
{
double som = 0;
for (int i = 0; i < S0_.Count; i++)
{
Ztilde = LoiNormal.random_normal_parBoxMuller(rnd) + Theta;
ST_[j, i] = S0_[i] * Math.Exp((r_ - 0.5 * Math.Pow(Sigma_[i], 2)) * t_ + Sigma_[i] * Math.Sqrt(t_) * Ztilde);
som += ST_[j, i];
}
Payoff_.Add(Math.Max((K_ - som / S0_.Count), 0.0));
Z.Add(Math.Exp(-r_ * t_) * (Math.Exp(0.5 * Math.Pow(Theta, 2) - Theta * Ztilde)) * Payoff_[j]);
}
}
else
{
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: ");
}
res[0] = Z.Average(); //La valeur de l'option, Esperance des payoff actualisés
res[1] = Math.Sqrt(Variance(Z, Z.Average(), 0, Z.Count)); //L'ecarte type de simulation,
return(res);
}
/*********************************************************************************************************
* Fonction return à la valeur d'une option européenne type=Call ou Put
* return à une vecteur de taille 2: première composante de la table contenant la valeur de l'option
* Deuxième composante: l'erreur de la simulation MC
**********************************************************************************************************/
public double[] MCEuropOptionVal(type_ op)
{
List <double> ST_, Payoff_, SquareEsperance;
double mu = (r_ - 0.5 * Math.Pow(Sigma_, 2)) * t_;
double sig = Sigma_ * Math.Sqrt(t_);
double NormBoxMuller; //Variable Normale centrée reduite
ST_ = new List <double>(); //Tableau pour stocker les valeurs de ST simuler
Payoff_ = new List <double>(); //Tableau des PayOff
SquareEsperance = new List <double>(); //Esperance des crées
double[] res = new double[2]; //Tableau résultat
for (int i = 0; i < NSim_; i++)
{
NormBoxMuller = LoiNormal.random_normal_parBoxMuller(rnd);
ST_.Add(S0_ * Math.Exp(mu + sig * NormBoxMuller));
if (i < NSim_ - 1)
{
ST_.Add(S0_ * Math.Exp(mu - (sig * NormBoxMuller)));
}
i++;
}
switch (op)
{
case type_.Call:
for (int i = 0; i < NSim_; i++)
{
Payoff_.Add(Math.Exp(-r_ * t_) * Math.Max((ST_[i] - K_), 0.0));
SquareEsperance.Add(Math.Pow(Payoff_[i], 2));
}
break;
case type_.Put:
for (int i = 0; i < NSim_; i++)
{
Payoff_.Add(Math.Exp(-r_ * t_) * Math.Max((K_ - ST_[i]), 0.0));
SquareEsperance.Add(Math.Pow(Payoff_[i], 2));
}
break;
default:
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: " + op);
}
res[0] = Payoff_.Average(); //La valeur de l'option, Esperance des payoff actualisés
res[1] = Math.Sqrt(Variance(Payoff_, res[0], 0, Payoff_.Count)); //L'ecarte type de simulation,
return(res);
}
//Calcul de la valeur d'une option européenne en utilisant l'algorithme de RM pour la réduction de la variance
public double[] MCEurValeurISLemairePages(double Theta, double K)
{
double[] PrixSimul = new double[NSim_];
double Ztilde = LoiNormal.random_normal_parBoxMuller(rnd) + Theta; //Variable Normale: moyenne =tetha* , variance = 1;
double mu = (r_ - 0.5 * Math.Pow(Sigma_, 2)) * T_;
double sig = Sigma_ * Math.Sqrt(T_);
List <double> Payoff_ = new List <double>();
List <double> Z = new List <double>();;
List <double> NormalVar = new List <double>();
List <double> Zcarre = new List <double>();
double[] res = new double[2];
this.K_ = K;
for (int i = 0; i < NSim_; i++)
{
Ztilde = LoiNormal.random_normal_parBoxMuller(rnd) + Theta;
PrixSimul[i] = S0_ * Math.Exp(mu + sig * Ztilde);
NormalVar.Add(Math.Pow(Ztilde, 2));
if (this.op_ == type_.Call)
{
Payoff_.Add(Math.Max(PrixSimul[i] - K_, 0.0));
}
else if (this.op_ == type_.Put)
{
Payoff_.Add(Math.Max(K_ - PrixSimul[i], 0.0));
}
else
{
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: " + this.op_);
}
Z.Add(Math.Exp(-r_ * T_) * (Math.Exp(0.5 * Math.Pow(Theta, 2) - Theta * Ztilde)) * Payoff_[i]);
Zcarre.Add((Math.Pow(Z[i], 2)));
}
res[0] = Z.Average(); //La moyenne des PayOff actulisés
res[1] = Math.Sqrt(Variance(Z, Z.Average(), 0, Z.Count)); //L'ecarte type de simulation,
return(res);
}
public double ThetaOptimalPL(double K, double Theta0, double lamda, int Niter)
{
List <double> Theta_ = new List <double>();
List <double> gamma_ = new List <double>();
List <double> y_ = new List <double>();
double Z = LoiNormal.random_normal_parBoxMuller(rnd);
this.K_ = K;
Theta_.Add(Theta0);
gamma_.Add(1);
y_.Add(Math.Exp(-2 * lamda * Theta_[0]) * FonctionFCaree(K_, Theta_[0]) * (2 * Theta_[0] - Z));
for (int i = 1; i <= Niter + 1; i++)
{
gamma_.Add(1 / (double)(i + 1)); //gamma_n = a/(b+n)
}
for (int i = 0; i < Niter; i++)
{
Z = LoiNormal.random_normal_parBoxMuller(rnd);
Theta_.Add(Theta_[i] - gamma_[i + 1] * y_[i]);
y_.Add(Math.Exp(-2 * lamda * Theta_[i + 1]) * FonctionFCaree(K_, Theta_[i + 1]) * (2 * Theta_[i + 1] - Z));
}
return(Theta_[Niter - 1]);
}
//Fonction F^2(Xn+1 - theta)
public double FonctionFCaree(double K, double theta)
{
this.K_ = K;
double ratio1 = 0.0;
double Z = LoiNormal.random_normal_parBoxMuller(rnd) - theta;
double ST = S0_ * Math.Exp((r_ - Math.Pow(Sigma_, 2) / 2) * T_ + Sigma_ * Math.Pow(T_, 0.50) * Z);
if (this.op_ == type_.Call)
{
ratio1 = Math.Pow(Math.Max(ST - K_, 0.0), 2);
}
else if (this.op_ == type_.Put)
{
ratio1 = Math.Pow(Math.Max(K_ - ST, 0.0), 2);
}
else
{
throw new InvalidOperationException("Impossible de traiter le type d'option entrer!!!!: " + this.op_);
}
double ratio2 = 1;
double Psi = ratio1 * ratio2;
return(Psi);
}
static void Main(string[] args)
{
Random rnd = new Random();
BSEurOption Eur;
RobbinsMonroAlgorithme RM_Put;
RobbinsMonroAlgorithme RM_Call;
MCEuropOption MCEur;
RMAlgoPagesLemaire PL_Call;
RMAlgoPagesLemaire PL_Put;
double S0 = 100;
double K = 95;
double r = 0.05;
int T = 1;
double sigma = 0.15;
double theta0 = 1.5;
double lamda = 30;
long Nsim = 2000;
int Ntier = 500000;
Eur = new BSEurOption(S0, K, r, T, sigma);
RM_Put = new RobbinsMonroAlgorithme(S0, K, T, sigma, r, Nsim, type_.Put);
RM_Call = new RobbinsMonroAlgorithme(S0, K, T, sigma, r, Nsim, type_.Call);
PL_Call = new RMAlgoPagesLemaire(S0, K, T, sigma, r, Nsim, type_.Call);
PL_Put = new RMAlgoPagesLemaire(S0, K, T, sigma, r, Nsim, type_.Put);
MCEur = new MCEuropOption(S0, K, T, sigma, r, Nsim);
double z = LoiNormal.random_normal_parBoxMuller(rnd);
double thetaOptArouna;
double thetaPL;
/***********************************************
* ********** Valeur d'un call Européen ********
* **********************************************/
Console.WriteLine("============================================================");
Console.WriteLine("====================Call Européen===========================");
Console.WriteLine("============================================================");
Console.WriteLine("Valeur exacte d'un Call (avec formules de BS)= " + Eur.callBlackScholes());
Console.WriteLine("\nValeur d'un Call avec réduction de la variance par var ANti= " + MCEur.MCEuropOptionVal(type_.Call)[0]);
Console.WriteLine("\nVariance IS(Variables Antithetique)= " + MCEur.MCEuropOptionVal(type_.Call)[1]);
thetaOptArouna = RM_Call.ThetaOptimalRMArouna(K, theta0, Ntier);
Console.WriteLine("\nTheta optimal avec Robbins Monro [Arouna]= " + thetaOptArouna);
Console.WriteLine("\nValeur d'un Call avec réduction de la variance IS= " + +RM_Call.MCEurValeurImportanceSampling(thetaOptArouna, K)[0]);
Console.WriteLine("\nVariance IS(Anouna RM algo)= " + +RM_Call.MCEurValeurImportanceSampling(thetaOptArouna, K)[1]);
thetaPL = PL_Call.ThetaOptimalPL(105, 0.3, 30, 10);
Console.WriteLine("\nTheta RMPagesLemaire = " + thetaPL);
Console.WriteLine("\nValeur d'un Call avec (Algo RM PagesLemaire) = " + PL_Call.MCEurValeurISLemairePages(thetaPL, K)[0]);
Console.WriteLine("\nVariance IS(PagesLemaire RM algo) = " + PL_Call.MCEurValeurISLemairePages(thetaPL, K)[1]);
/***************************************
********* Test options basket *********
***************************************/
Console.WriteLine("\n\n============================================================");
Console.WriteLine("====================Options Basket===========================");
Console.WriteLine("============================================================");
List <double> S0_ = new List <double>();
List <double> Sigma_ = new List <double>();
for (int i = 0; i < 3; i++)
{
S0_.Add(94 + i * 4);
Sigma_.Add(0.25 + i * 0.02);
}
MCBasketOption bsket = new MCBasketOption(S0_, K, T, Sigma_, r, Nsim);
MCRMBasketOption rmbask = new MCRMBasketOption(S0_, K, T, Sigma_, r, Nsim, type_.Call);
Console.WriteLine("\n\n prix Basket Call MC_ Var Anti= " + bsket.MC_AntitBasketOptionVal(type_.Call)[0]);
Console.WriteLine("\n\n prix Basket Put MC_ Var Anti= " + bsket.MC_AntitBasketOptionVal(type_.Put)[0]);
double thetarmBasket = rmbask.ThetaOptimalRMArouna(105, 1.5, 100000);
Console.WriteLine("\n\n Theta optimal avec RM de l'option Basket = " + thetarmBasket);
Console.ReadLine();
}