| 1 | /* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */ |
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| 2 | |
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| 3 | /* |
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| 4 | Copyright (C) 2005 Klaus Spanderen |
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| 5 | Copyright (C) 2005 StatPro Italia srl |
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| 6 | |
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| 7 | This file is part of QuantLib, a free-software/open-source library |
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| 8 | for financial quantitative analysts and developers - http://quantlib.org/ |
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| 9 | |
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| 10 | QuantLib is free software: you can redistribute it and/or modify it |
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| 11 | under the terms of the QuantLib license. You should have received a |
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| 12 | copy of the license along with this program; if not, please email |
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| 13 | <quantlib-dev@lists.sf.net>. The license is also available online at |
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| 14 | <http://quantlib.org/license.shtml>. |
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| 15 | |
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| 16 | This program is distributed in the hope that it will be useful, but WITHOUT |
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| 17 | ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS |
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| 18 | FOR A PARTICULAR PURPOSE. See the license for more details. |
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| 19 | */ |
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| 20 | |
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| 21 | #include <ql/processes/stochasticprocessarray.hpp> |
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| 22 | #include <ql/math/matrixutilities/pseudosqrt.hpp> |
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| 23 | |
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| 24 | namespace QuantLib { |
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| 25 | |
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| 26 | StochasticProcessArray::StochasticProcessArray( |
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| 27 | const std::vector<ext::shared_ptr<StochasticProcess1D> >& processes, |
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| 28 | const Matrix& correlation) |
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| 29 | : processes_(processes), |
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| 30 | sqrtCorrelation_(pseudoSqrt(correlation,SalvagingAlgorithm::Spectral)) { |
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| 31 | |
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| 32 | QL_REQUIRE(!processes.empty(), "no processes given" ); |
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| 33 | QL_REQUIRE(correlation.rows() == processes.size(), |
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| 34 | "mismatch between number of processes " |
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| 35 | "and size of correlation matrix" ); |
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| 36 | for (auto& process : processes_) { |
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| 37 | QL_REQUIRE(process, "null 1-D stochastic process" ); |
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| 38 | registerWith(h: process); |
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| 39 | } |
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| 40 | } |
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| 41 | |
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| 42 | Size StochasticProcessArray::size() const { |
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| 43 | return processes_.size(); |
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| 44 | } |
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| 45 | |
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| 46 | Array StochasticProcessArray::initialValues() const { |
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| 47 | Array tmp(size()); |
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| 48 | for (Size i=0; i<size(); ++i) |
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| 49 | tmp[i] = processes_[i]->x0(); |
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| 50 | return tmp; |
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| 51 | } |
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| 52 | |
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| 53 | Array StochasticProcessArray::drift(Time t, |
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| 54 | const Array& x) const { |
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| 55 | Array tmp(size()); |
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| 56 | for (Size i=0; i<size(); ++i) |
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| 57 | tmp[i] = processes_[i]->drift(t, x: x[i]); |
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| 58 | return tmp; |
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| 59 | } |
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| 60 | |
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| 61 | Matrix StochasticProcessArray::diffusion(Time t, |
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| 62 | const Array& x) const { |
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| 63 | Matrix tmp = sqrtCorrelation_; |
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| 64 | for (Size i=0; i<size(); ++i) { |
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| 65 | Real sigma = processes_[i]->diffusion(t, x: x[i]); |
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| 66 | std::transform(first: tmp.row_begin(i), last: tmp.row_end(i), |
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| 67 | result: tmp.row_begin(i), |
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| 68 | unary_op: [=](Real x) -> Real { return x * sigma; }); |
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| 69 | } |
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| 70 | return tmp; |
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| 71 | } |
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| 72 | |
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| 73 | Array StochasticProcessArray::expectation(Time t0, |
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| 74 | const Array& x0, |
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| 75 | Time dt) const { |
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| 76 | Array tmp(size()); |
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| 77 | for (Size i=0; i<size(); ++i) |
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| 78 | tmp[i] = processes_[i]->expectation(t0, x0: x0[i], dt); |
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| 79 | return tmp; |
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| 80 | } |
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| 81 | |
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| 82 | Matrix StochasticProcessArray::stdDeviation(Time t0, |
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| 83 | const Array& x0, |
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| 84 | Time dt) const { |
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| 85 | Matrix tmp = sqrtCorrelation_; |
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| 86 | for (Size i=0; i<size(); ++i) { |
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| 87 | Real sigma = processes_[i]->stdDeviation(t0, x0: x0[i], dt); |
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| 88 | std::transform(first: tmp.row_begin(i), last: tmp.row_end(i), |
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| 89 | result: tmp.row_begin(i), |
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| 90 | unary_op: [=](Real x) -> Real { return x * sigma; }); |
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| 91 | } |
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| 92 | return tmp; |
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| 93 | } |
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| 94 | |
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| 95 | Matrix StochasticProcessArray::covariance(Time t0, |
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| 96 | const Array& x0, |
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| 97 | Time dt) const { |
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| 98 | Matrix tmp = stdDeviation(t0, x0, dt); |
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| 99 | return tmp*transpose(m: tmp); |
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| 100 | } |
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| 101 | |
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| 102 | Array StochasticProcessArray::evolve( |
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| 103 | Time t0, const Array& x0, Time dt, const Array& dw) const { |
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| 104 | const Array dz = sqrtCorrelation_ * dw; |
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| 105 | |
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| 106 | Array tmp(size()); |
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| 107 | for (Size i=0; i<size(); ++i) |
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| 108 | tmp[i] = processes_[i]->evolve(t0, x0: x0[i], dt, dw: dz[i]); |
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| 109 | return tmp; |
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| 110 | } |
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| 111 | |
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| 112 | Array StochasticProcessArray::apply(const Array& x0, |
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| 113 | const Array& dx) const { |
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| 114 | Array tmp(size()); |
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| 115 | for (Size i=0; i<size(); ++i) |
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| 116 | tmp[i] = processes_[i]->apply(x0: x0[i],dx: dx[i]); |
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| 117 | return tmp; |
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| 118 | } |
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| 119 | |
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| 120 | Time StochasticProcessArray::time(const Date& d) const { |
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| 121 | return processes_[0]->time(d); |
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| 122 | } |
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| 123 | |
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| 124 | const ext::shared_ptr<StochasticProcess1D>& |
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| 125 | StochasticProcessArray::process(Size i) const { |
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| 126 | return processes_[i]; |
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| 127 | } |
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| 128 | |
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| 129 | Matrix StochasticProcessArray::correlation() const { |
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| 130 | return sqrtCorrelation_ * transpose(m: sqrtCorrelation_); |
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| 131 | } |
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| 132 | |
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| 133 | } |
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| 134 | |
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