Пример #1
0
std::vector<std::vector<double> > qlMatrixToVv(const QuantLib::Matrix &m) {
    std::vector<std::vector<double> > vv;
    for(unsigned int r=0; r<m.rows(); ++r) {
        std::vector<double> v;
        for(unsigned int c=0; c<m.columns(); ++c) {
            v.push_back(m[r][c]);
        }
        vv.push_back(v);
    }
    return vv;
}
Пример #2
0
    boost::shared_ptr<VolatilityRateSource> VolatilitySurfaceMoneyness::parallelBump(double spread)
    {
        vector<Date> newDates;
        QuantLib::Matrix volatilitySubset;

        getRolledVariables(anchorDate, newDates, volatilitySubset);
        Matrix spreadMatrix = Matrix(volatilitySubset.rows(), volatilitySubset.columns(), spread);
        Matrix bumpedVolMatrix = volatilitySubset + spreadMatrix;

        return boost::shared_ptr<VolatilityRateSource>(new VolatilitySurfaceMoneyness(anchorDate, 
                                                                             newDates, 
                                                                             strikeDimension,
                                                                             bumpedVolMatrix,
                                                                             interpolatorType,
                                                                             allowExtrapolation));
    }
Пример #3
0
//! YY TODO: Now prefer to keep the algorithm clear, so it is highly inefficient. To improve latter. 
QuantLib::Matrix PCA::doPCA(const QuantLib::Matrix& originalMatrix, size_t reducedRank, bool normalizeDiagonal) // YY TODO: change it to boost::optional<bool>
{
	size_t fullRank = originalMatrix.rows(); 

	assert(fullRank > reducedRank);

	//! decompose the original matrix
	QuantLib::SymmetricSchurDecomposition ssd(originalMatrix);  // here it check if the matrix is squared, symetric.
	QuantLib::Array eigenvalues = ssd.eigenvalues();
	QuantLib::Matrix eigenvectors = ssd.eigenvectors();

	assert(checkEigenvalue(eigenvalues, reducedRank));

	//! construct the reducedRank matrix	   

	QuantLib::Array D(reducedRank);
	for(size_t i=0; i<D.size(); ++i)
	{
		D[i] = std::sqrt(eigenvalues[i]);
	}
	//! normalize the diagonal of the reducedRank matrix.
	QuantLib::Array diagonalNormalizeFactor(fullRank);
	if(normalizeDiagonal)
	{
		for(size_t i=0; i<fullRank; ++i)
		{
			diagonalNormalizeFactor[i] = 0.0;
			for(size_t j=0; j<reducedRank; ++j)
			{
				diagonalNormalizeFactor[i] += eigenvalues[j]*eigenvectors[i][j]*eigenvectors[i][j];
			}
			diagonalNormalizeFactor[i] = sqrt(diagonalNormalizeFactor[i]);
		}
	}
	else
	{
		for(size_t i=0; i<fullRank; ++i)
		{
			diagonalNormalizeFactor[i] = 1.0;
		}
	}

	QuantLib::Matrix U(fullRank,reducedRank,0.0);
	for(size_t i=0; i<fullRank; ++i)
	{
		for(size_t j=0; j<reducedRank; ++j)
		{
			U[i][j] = eigenvectors[i][j]*D[j]/diagonalNormalizeFactor[i];
		}
	}

	return U;
}
Пример #4
0
    void VolatiltiySurfaceRateSource::setInputs(Date anchorDateInput, 
                                                vector<Date> observationDatesInput, 
                                                vector<double> strikeDimensionInput,
                                                QuantLib::Matrix volatilityInput,
                                                SurfaceInterpolatorType interpolatorTypeInput,
                                                bool extrapolateInput)
    {
        if (observationDatesInput.size() == 0 ||
             strikeDimensionInput.size() == 0 ||
             volatilityInput.rows() * volatilityInput.columns() == 0)
        {
            observationDates = vector<Date>();
            strikeDimension = vector<double>();
            volatility = Matrix(0,0);
            return;
        }
        anchorDate = anchorDateInput;
        dayCounter = boost::shared_ptr<DayCounter>(new Actual365Fixed());
        allowExtrapolation = extrapolateInput;

        strikeDimension = strikeDimensionInput;
        observationDates = observationDatesInput;
        if (observationDates[0] > anchorDate) 
        {
            observationDates.insert(observationDates.begin(), anchorDate);
            volatility = QuantLib::Matrix(volatilityInput.rows(), volatilityInput.columns()+1);
            for (size_t i = 0; i < volatilityInput.rows(); ++i) 
            {
                volatility[i][0] = volatilityInput[i][0];
                for (size_t j = 0; j < volatilityInput.columns(); ++j) 
                {
                    volatility[i][j+1] = volatilityInput[i][j];
                }
            }
        }
        else 
        {
            volatility = volatilityInput;
        }
        
        time.clear();
        for (size_t i = 0; i < observationDates.size(); ++i) 
        {
            double yf = dayCounter->yearFraction(anchorDate, observationDates[i]);
            time.push_back(yf);
        }

        if (observationDates.size() > 0) 
        {
            finalDate = observationDates.back();
        }
        else 
        {
         finalDate = Date(01,Jan,1901);
        }
        setInterpolatorType(interpolatorTypeInput);
    }
Пример #5
0
 void matrixToOper(const QuantLib::Matrix &m, OPER &xMatrix) {
     if (m.empty()) {
         xMatrix.xltype = xltypeErr;
         xMatrix.val.err = xlerrNA;
         return;
     }
     xMatrix.val.array.rows = m.rows();
     xMatrix.val.array.columns = m.columns();
     xMatrix.val.array.lparray = new OPER[xMatrix.val.array.rows * xMatrix.val.array.columns]; 
     xMatrix.xltype = xltypeMulti | xlbitDLLFree;
     for (unsigned int i=0; i<m.rows(); ++i)
         for (unsigned int j=0; j<m.columns(); ++j)
             scalarToOper(m[i][j], xMatrix.val.array.lparray[i * m.columns() + j]);
 }