Generating pairwise correlated GBM data
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Generating pairwise correlated GBM data
 
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Re: Generating pairwise correlated GBM data
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Re: Generating pairwise correlated GBM data
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Re: Generating pairwise correlated GBM data
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Thanks Luigi,
but there is IMO an error either in the Higham algo itself, or its implementation in QuantLib. Here's short demo. As can be seen, the output matrix is not a correlation matrix, ie. a bug: /* test.cpp Written by U.Mutlu, 2016-02-19-Tu Testing finding nearest correlation matrix using Higham algorithm --> gives wrong result: the output is far from being a correlation matrix Compile: g++ -Wall -O2 -std=c++11 test.cpp -lQuantLib Output: Input matrix: rows=3 cols=3 det=-0.0120000000 1.0000000000 0.9000000000 0.7000000000 0.9000000000 1.0000000000 0.3000000000 0.7000000000 0.3000000000 1.0000000000 Output matrix: rows=3 cols=3 det=0.0000000000 1.0000000000 0.0000000000 0.0000000000 0.8945757928 0.4469162685 0.0000000000 0.6966210765 -0.7174430809 0.0000000000 */ #include <ql/math/matrix.hpp> #include <ql/math/matrixutilities/pseudosqrt.hpp> #include <cstdio> #include <string> using namespace QuantLib; using namespace std; void print_matrix(const Matrix& AM, const string AsTitle = "") { const size_t N = AM.columns(); const string sFmt = " %13.10f"; // " %43.40f"; printf("%s: rows=%zu cols=%zu det=%.10f\n", AsTitle.c_str(), AM.rows(), AM.columns(), determinant(AM)); for (size_t i = 0; i < N; ++i, printf("\n")) for (size_t j = 0; j < N; ++j) printf(sFmt.c_str(), AM[i][j]); } int main() { const size_t N = 3; Matrix M(N, N); M[0][0] = 1.0; M[0][1] = 0.9; M[0][2] = 0.7; M[1][0] = 0.9; M[1][1] = 1.0; M[1][2] = 0.3; M[2][0] = 0.7; M[2][1] = 0.3; M[2][2] = 1.0; Matrix R = pseudoSqrt(M, SalvagingAlgorithm::Higham); print_matrix(M, "Input matrix"), printf("\n"); print_matrix(R, "Output matrix"); return 0; } Platform: linux Compiler: GNU C++ version 4.9.2 STL : GNU libstdc++ version 20141220 Boost : 1.55.0 Luigi Ballabio wrote on 02/19/2016 03:43 PM: > Hello, > you can use the StochasticProcessArray to take a number of 1-D > processes and their correlation matrix and build the corresponding > multi-dimensional process, that can then be passed to > the MultiPathGenerator class to generate the prices. > > By default, the StochasticProcessArray class fixes a defective correlation > matrix using the spectral salvaging algorithm. You can lookup > the pseudoSqrt function in QuantLib to see its implementation. The function > can use a few other algorithms, but at this time you can't choose them when > you instantiate StochasticProcessArray. > > You can look at QuantLib/test-suite/pathgenerator.cpp for examples. > > Hope this helps, > Luigi > > > On Thu, Feb 18, 2016 at 11:24 AM U.Mutlu <[hidden email]> wrote: > >> Hi, >> >> 1) Is it possible to generate correlated price data in quantlib >> by using a correlation matrix? (needed for simulations) >> >> 2) Sometimes there can be a problem with the correlation matrix, >> especially if it is not positive definite, ie. a defective correlation >> matrix. >> Here's an example for a defective correlation matrix: >> ( >> >> http://blogs.sas.com/content/iml/2012/09/12/when-is-a-correlation-matrix-not-a-correlation-matrix.html >> ) >> 1.0 0.6 0.9 >> 0.6 1.0 0.9 >> 0.9 0.9 1.0 >> In such cases one has to find a replacement matrix (Nearest Correlation >> Matrix >> Problem). >> Anybody know of C/C++/C#/Java code or library for computing/finding the >> nearest correlation matrix? >> ------------------------------------------------------------------------------ Site24x7 APM Insight: Get Deep Visibility into Application Performance APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month Monitor end-to-end web transactions and take corrective actions now Troubleshoot faster and improve end-user experience. Signup Now! http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 _______________________________________________ QuantLib-users mailing list [hidden email] https://lists.sourceforge.net/lists/listinfo/quantlib-users |
test.cpp (1K)
Re: Generating pairwise correlated GBM data
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Re: Generating pairwise correlated GBM data
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