///<summary>
/// The main function to calculate the adjusted forward.
///</summary>
///<param name="futuresPrice"> Vector of futures prices.</param>
///<param name="volatility"> Vector of futures</param>
///<param name="correlation"> Correlation between forwards. Can be a
/// scalar input or a correlation matrix.</param>
///<param name="shift"> The shift parameters from the BGM
/// calibration.</param>
///<param name="coverage"> The vector of time intervals between model
/// time nodes.</param>
///<param name="timeNodes"> The model time nodes</param>
///<returns> optOut, an array OptimisationOuput types, with one element
/// for each time node.</returns>
public static OptimisationOutput[] CalculateCashForward(double[] futuresPrice,
double[,] volatility,
double[, ,] correlation,
double[] shift,
double[] coverage,
double[] timeNodes)
{
var terminalNode = futuresPrice.Length - 1;
var optOut = new OptimisationOutput[terminalNode + 1];
var cashForward = new double[terminalNode + 1];
const double tolerance = 0.000000001;
const int maxIterations = 100;
const int approximationOrder = 3;
Del delObjective = Difference;
// This assumes that if vol is input as a column vector (for
// terminal node greater than 1) then the vol is meant to be
// constant for each time node.
if (volatility.GetLength(1) == 1 &&
volatility.GetLength(0) == terminalNode + 1 && terminalNode > 1)
{
volatility = Utilities.Transpose(volatility);
}
for (var j = 1; j <= terminalNode; j++)
{
var fixingNode = j;
double initialValue = j > 1 ? cashForward[j - 1] : futuresPrice[j];
var targetPrice = futuresPrice[j];
optOut[j] = Optimiser.Secant(delObjective, initialValue,
tolerance, maxIterations, cashForward,
approximationOrder, fixingNode, volatility,
correlation, shift, coverage, timeNodes,
targetPrice);
cashForward[j] = optOut[j].X;
}
return(optOut);
}
public static OptimisationOutput Secant(Del delF,
double initialValue,
double tolerance,
int maxIterations,
params object[] list)
{
// Finds the root of the function F using the secant method.
var x = initialValue;
var xMinus = 1.005 * x;
var count = 0;
while (Math.Abs(delF(x, list)) > tolerance && count < maxIterations)
{
var xPlus = x - (x - xMinus) * delF(x, list) / (delF(x, list) - delF(xMinus, list));
xMinus = x;
x = xPlus;
count = count + 1;
}
var result = new OptimisationOutput(count, x, delF(x, list));
return(result);
}
