/// <summary>
/// Simulate a realization of the stochastic process driven by the noise matrix noise.
/// </summary>
/// <remarks>
/// This function is called once for realization.
/// </remarks>
/// <param name="dates">
/// The dates (in years fractions) at which the process must be simulated.
/// </param>
/// <param name="noise">The matrix of IID normal realizations.</param>
/// <param name="outDynamic">Where the dynamic should be written.</param>
public void Simulate(double[] dates, IReadOnlyMatrixSlice noise, IMatrixSlice outDynamic)
{
switch (OperatingMode)
{
case OperatingMode.TranslateHistoricalRealizationsForward:
for (int i = 0; i < dates.Length; i++)
{
int dateIndex;
if (!this.simulationDateIndexes.TryGetValue(dates[i], out dateIndex))
{
dateIndex = 0;
}
Tuple <DateTime, Vector> value = this.fileContent[dateIndex];
for (int j = 0; j < value.Item2.Length - 1; j++)
{
outDynamic[i, j] = value.Item2[j];
}
}
break;
case OperatingMode.Bootstrap:
this.bootstrap.Simulate(dates, outDynamic);
break;
}
}
/// <summary>
/// Handles the conversion, after the simulation, from y to the rate r.
/// </summary>
/// <param name="dates">The parameter is not used.</param>
/// <param name="outDynamic">The input and output components of the transformation.</param>
public void Transform(double[] dates, IMatrixSlice outDynamic)
{
for (int j = 0; j < dates.Length; j++)
{
outDynamic[j, 0] = Math.Pow(outDynamic[j, 0] + this.alphaT0[j], 2);
}
}
public void Simulate(double[] Dates, IReadOnlyMatrixSlice Noise, IMatrixSlice OutDynamic)
{
OutDynamic[0, 0] = spot.fV();
for (int i = 1; i < Dates.Length; i++)
{
OutDynamic[i, 0] = a * OutDynamic[i - 1, 0] + b + c * Noise[i - 1, 0];
}
}
/// <summary>
/// Calculates the probabilities for one scenario.
/// </summary>
/// <param name="dates">Dates (in Year fractions).</param>
/// <param name="outDynamic">Input and output matrix.</param>
public void Transform(double[] dates, IMatrixSlice outDynamic)
{
for (int j = 0; j < dates.Length; j++)
{
// Truncate to zero the first two components.
outDynamic[j, 0] = Math.Max(0.0000000001, outDynamic[j, 0]);
outDynamic[j, 1] = Math.Max(0.0000000001, outDynamic[j, 1]);
}
}
/// <summary>
/// Handles the conversion, after the simulation, from y to the short rate r.
/// </summary>
/// <param name="dates">Simulation dates.</param>
/// <param name="outDynamic">The input and output components of the transformation.</param>
public virtual void Transform(double[] dates, IMatrixSlice outDynamic)
{
#if TFORWARDFORMULATION
for (int j = 0; j < dates.Length; j++)
{
outDynamic[j, 0] = outDynamic[j, 0] + this.alphaT[j];
}
#endif
}
public void Simulate(double[] Dates, IReadOnlyMatrixSlice Noise, IMatrixSlice OutDynamic)
{
OutDynamic[0, 0] = x0[0];
for (int i = 1; i < Dates.Length; i++)
{
double dt = Dates[i] - Dates[i - 1];
double rdt = Math.Sqrt(dt);
double th = theta(Dates[i], dt);
OutDynamic[i, 0] = OutDynamic[i - 1, 0] + (th - alpha1Temp * OutDynamic[i - 1, 0]) * dt + sigma1Temp * Noise[i - 1, 0] * rdt;
}
}
/// <summary>
/// Manage the simulation of the variance gamma process
/// </summary>
/// <param name="Dates">Simulation dates</param>
/// <param name="Noise">Gaussian noise for a single path</param>
/// <param name="OutDynamic">Single path process realization</param>
public void Simulate(double[] Dates, IReadOnlyMatrixSlice Noise, IMatrixSlice OutDynamic)
{
int steps = OutDynamic.R;
double GammaNoise, dt;
OutDynamic[0, 0] = this.s0.fV();
for (int i = 1; i < steps; i++)
{
dt = Dates[i] - Dates[i - 1];
this.gamma = new Fairmat.Statistics.Gamma(dt / this.nu.fV(), 1.0 / this.nu.fV());
GammaNoise = this.gamma.Draw(Engine.Generator);
OutDynamic[i, 0] = OutDynamic[i - 1, 0] * Math.Exp(this.drift * dt +
this.theta.fV() * GammaNoise + this.sigma.fV() * Math.Sqrt(GammaNoise) * Noise[i - 1, 0]);
}
}
/// <summary>
/// Simulate a realization of the stochastic process by bootstrapping historical data.
/// </summary>
/// <remarks>
/// This function is called once for realization.
/// </remarks>
/// <param name="dates">
/// The dates (in years fractions) at which the process must be simulated.
/// </param>
/// <param name="outDynamic">Where the dynamic should be written.</param>
internal void Simulate(double[] dates, IMatrixSlice outDynamic)
{
// Number of components.
int components = this.returns[0].Length;
for (int c = 0; c < components; c++)
outDynamic[0, c] = 1;
// Assumes equispaced dates, otherwise we can normalize returns.
for (int i = 1; i < dates.Length; i++)
{
double dt = dates[i] - dates[i - 1];
// Selects a vector of returns.
double u = Engine.Generator.Uniform();
int z = (int)(u * this.returns.Length);
for (int c = 0; c < components; c++)
outDynamic[i, c] = outDynamic[i - 1, c] * Math.Exp(this.returns[z][c] * dt);
}
}
/// <summary>
/// Simulate a realization of the stochastic process by bootstrapping historical data.
/// </summary>
/// <remarks>
/// This function is called once for realization.
/// </remarks>
/// <param name="dates">
/// The dates (in years fractions) at which the process must be simulated.
/// </param>
/// <param name="outDynamic">Where the dynamic should be written.</param>
internal void Simulate(double[] dates, IMatrixSlice outDynamic)
{
// Number of components.
int components = this.returns[0].Length;
for (int c = 0; c < components; c++)
{
outDynamic[0, c] = 1;
}
// Assumes equispaced dates, otherwise we can normalize returns.
for (int i = 1; i < dates.Length; i++)
{
double dt = dates[i] - dates[i - 1];
// Selects a vector of returns.
double u = Engine.Generator.Uniform();
int z = (int)(u * this.returns.Length);
for (int c = 0; c < components; c++)
{
outDynamic[i, c] = outDynamic[i - 1, c] * Math.Exp(this.returns[z][c] * dt);
}
}
}
/// <summary>
/// Simulate a realization of the stochastic process driven by the noise matrix noise.
/// </summary>
/// <remarks>
/// This function is called once for realization.
/// </remarks>
/// <param name="dates">
/// The dates (in years fractions) at which the process must be simulated.
/// </param>
/// <param name="noise">The matrix of IID normal realizations.</param>
/// <param name="outDynamic">Where the dynamic should be written.</param>
public void Simulate(double[] dates, IReadOnlyMatrixSlice noise, IMatrixSlice outDynamic)
{
switch (OperatingMode)
{
case OperatingMode.TranslateHistoricalRealizationsForward:
for (int i = 0; i < dates.Length; i++)
{
int dateIndex;
if (!this.simulationDateIndexes.TryGetValue(dates[i], out dateIndex))
dateIndex = 0;
Tuple<DateTime, Vector> value = this.fileContent[dateIndex];
for (int j = 0; j < value.Item2.Length - 1; j++)
{
outDynamic[i, j] = value.Item2[j];
}
}
break;
case OperatingMode.Bootstrap:
this.bootstrap.Simulate(dates, outDynamic);
break;
}
}
/// <summary>
/// Handles the conversion, after the simulation, from y to the rate r.
/// </summary>
/// <param name="dates">The parameter is not used.</param>
/// <param name="outDynamic">The input and output components of the transformation.</param>
public void Transform(double[] dates, IMatrixSlice outDynamic)
{
for (int j = 0; j < dates.Length; j++)
{
outDynamic[j, 0] = Math.Pow(outDynamic[j, 0] + this.alphaT0[j], 2);
}
}
/// <summary>
/// Calculates the probabilities for one scenario.
/// </summary>
/// <param name="dates">Dates (in Year fractions).</param>
/// <param name="outDynamic">Input and output matrix.</param>
public void Transform(double[] dates, IMatrixSlice outDynamic)
{
for (int j = 0; j < dates.Length; j++)
{
// Truncate to zero the first two components.
outDynamic[j, 0] = Math.Max(0.0000000001, outDynamic[j, 0]);
outDynamic[j, 1] = Math.Max(0.0000000001, outDynamic[j, 1]);
}
}
/// <summary>
/// Manage the simulation of the variance gamma process
/// </summary>
/// <param name="Dates">Simulation dates</param>
/// <param name="Noise">Gaussian noise for a single path</param>
/// <param name="OutDynamic">Single path process realization</param>
public void Simulate(double[] Dates, IReadOnlyMatrixSlice Noise, IMatrixSlice OutDynamic)
{
int steps = OutDynamic.R;
double GammaNoise, dt;
OutDynamic[0, 0] = this.s0.fV();
for (int i = 1; i < steps; i++)
{
dt = Dates[i] - Dates[i - 1];
this.gamma = new Fairmat.Statistics.Gamma(dt / this.nu.fV(), 1.0 / this.nu.fV());
GammaNoise = this.gamma.Draw(Engine.Generator);
OutDynamic[i, 0] = OutDynamic[i - 1, 0] * Math.Exp(this.drift * dt +
this.theta.fV() * GammaNoise + this.sigma.fV() * Math.Sqrt(GammaNoise) * Noise[i - 1, 0]);
}
}
public void Simulate(double[] Dates, IReadOnlyMatrixSlice Noise, IMatrixSlice OutDynamic)
{
OutDynamic[0, 0] = x0[0];
for (int i = 1; i < Dates.Length; i++)
{
double dt= Dates[i]-Dates[i-1];
double rdt=Math.Sqrt(dt);
double th = theta(Dates[i], dt);
OutDynamic[i, 0] = OutDynamic[i-1, 0]+(th - alpha1Temp * OutDynamic[i - 1, 0]) * dt + sigma1Temp * Noise[i - 1, 0] * rdt;
}
}
/// <summary>
/// Handles the conversion, after the simulation, from y to the short rate r.
/// </summary>
/// <param name="dates">Simulation dates.</param>
/// <param name="outDynamic">The input and output components of the transformation.</param>
public virtual void Transform(double[] dates, IMatrixSlice outDynamic)
{
#if TFORWARDFORMULATION
for (int j = 0; j < dates.Length; j++)
{
outDynamic[j, 0] = outDynamic[j, 0] + this.alphaT[j];
}
#endif
}
