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Changeset 224

Timestamp:

07/06/08 21:06:38 (3 months ago)

Author:

snovik

Message:

Fix: Monte-carlo (American Option)
New: LinearLeastSquaresRegression? and its test
Change: EquityOption? example is finished
Fix: Matrix methods: column & row

Files:

trunk/QLNet/Examples/EquityOption/EquityOption.cs (modified) (2 diffs)
trunk/QLNet/QLNet/Math/Matrix.cs (modified) (3 diffs)
trunk/QLNet/QLNet/Math/linearleastsquaresregression.cs (modified) (3 diffs)
trunk/QLNet/QLNet/Math/matrixutilities/svd.cs (modified) (13 diffs)
trunk/QLNet/QLNet/Methods/montecarlo/longstaffschwartzpathpricer.cs (modified) (3 diffs)
trunk/QLNet/QLNet/Methods/montecarlo/path.cs (modified) (2 diffs)
trunk/QLNet/QLNet/Methods/montecarlo/sample.cs (modified) (1 diff)
trunk/QLNet/QLNet/Pricingengines/vanilla/mcamericanengine.cs (modified) (4 diffs)
trunk/QLNet/Test2008/T_LinearLeastSquaresRegression.cs (added)
trunk/QLNet/Test2008/Test2008.csproj (modified) (2 diffs)

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trunk/QLNet/Examples/EquityOption/EquityOption.cs

r219	r224
222	222	timeSteps = 1;
223	223	method = "MC (crude)";
224		~~int~~ mcSeed = 42;
	224	ulong mcSeed = 42;
225	225	IPricingEngine mcengine1 = new MakeMCEuropeanEngine<PseudoRandom>(bsmProcess)
226	226	.withSteps(timeSteps)
227	227	.withTolerance(0.02)
228		.withSeed(~~(ulong)~~mcSeed)
	228	.withSeed(mcSeed)
229	229	.value();
230	230	europeanOption.setPricingEngine(mcengine1);
…	…
252	252	// Monte Carlo Method: MC (Longstaff Schwartz)
253	253	method = "MC (Longstaff Schwartz)";
254		//IPricingEngine mcengine3 = new MakeMCAmericanEngine<PseudoRandom>(bsmProcess)
255		// .withSteps(100)
256		// .withAntitheticVariate()
257		// .withCalibrationSamples(4096)
258		// .withTolerance(0.02)
259		// .withSeed(mcSeed)
260		// .value();
261		//americanOption.setPricingEngine(mcengine3);
262		//Console.Write("{0,-" + widths[0] + "}", method);
263		//Console.Write("{0,-" + widths[1] + ":0.000000}", "N/A");
264		//Console.Write("{0,-" + widths[2] + ":0.000000}", "N/A");
265		//Console.WriteLine("{0,-" + widths[3] + ":0.000000}", americanOption.NPV());
	254	IPricingEngine mcengine3 = new MakeMCAmericanEngine<PseudoRandom>(bsmProcess)
	255	.withSteps(100)
	256	.withAntitheticVariate()
	257	.withCalibrationSamples(4096)
	258	.withTolerance(0.02)
	259	.withSeed(mcSeed)
	260	.value();
	261	americanOption.setPricingEngine(mcengine3);
	262	Console.Write("{0,-" + widths[0] + "}", method);
	263	Console.Write("{0,-" + widths[1] + ":0.000000}", "N/A");
	264	Console.Write("{0,-" + widths[2] + ":0.000000}", "N/A");
	265	Console.WriteLine("{0,-" + widths[3] + ":0.000000}", americanOption.NPV());
266	266
267	267	// End test

trunk/QLNet/QLNet/Math/Matrix.cs

r223	r224
28	28	container.
29	29	*/
30		public struct Matrix {
	30	public struct Matrix : ICloneable {
31	31	#region properties
32	32	private int rows_, columns_;
…	…
38	38	public double this[int i, int j] { get { return data_[i, j]; } set { data_[i, j] = value; } }
39	39	public Vector row(int r) {
	40	Vector result = new Vector(columns_);
	41	for (int i = 0; i < columns_; i++)
	42	result[i] = data_[r, i];
	43	return result;
	44	}
	45	public Vector column(int c) {
40	46	Vector result = new Vector(rows_);
41	47	for (int i = 0; i < rows_; i++)
42		~~result[i] = data_[r, i];~~
43		~~return result;~~
44		}
45		~~public Vector column(int c) {~~
46		~~Vector result = new Vector(columns_);~~
47		~~for (int i = 0; i < columns_; i++)~~
48	48	result[i] = data_[i, c];
49	49	return result;
…	…
180	180	this[i1, j1] = t;
181	181	}
	182
	183	// IClonable interface
	184	public object Clone() { return this.MemberwiseClone(); }
182	185	}
183	186	}

trunk/QLNet/QLNet/Math/linearleastsquaresregression.cs

r223	r224
31	31	checking their properties.
32	32	*/
	33	public class LinearLeastSquaresRegression : LinearLeastSquaresRegression<double> {
	34	public LinearLeastSquaresRegression(List<double> x, List<double> y, List<Func<double, double>> v)
	35	: base(x,y,v) { }
	36	}
	37
33	38	public class LinearLeastSquaresRegression<ArgumentType> {
34	39	private Vector a_;
…	…
36	41
37	42	public LinearLeastSquaresRegression(List<ArgumentType> x, List<double> y, List<Func<ArgumentType, double>> v) {
38		a_ = new Vector(v.Count~~, 0.0~~);
39		err_ = new Vector(v.Count~~, 0.0~~);
	43	a_ = new Vector(v.Count);
	44	err_ = new Vector(v.Count);
40	45
41	46	if (x.Count != y.Count)
…	…
49	54
50	55	Matrix A = new Matrix(n, m);
51		for (i=0; i<m; ++i)
52		for(int j=0;j<x.Count; j++)
53		A[j,i] = v[i](x[i]);
	56	for (i = 0; i < m; ++i)
	57	x.ForEach((jj, xx) => A[jj, i] = v[i](xx));
54	58
55	59	SVD svd = new SVD(A);
56	60	Matrix V = svd.V();
57	61	Matrix U = svd.U();
58		Vector w = svd.singularValues();
	62	Vector w = svd.singularValues();
59	63	double threshold = n*Const.QL_Epsilon;
60	64

trunk/QLNet/QLNet/Math/matrixutilities/svd.cs

r223	r224
37	37
38	38	public SVD(Matrix M) {
39		~~//using std::swap;~~
40
41	39	Matrix A;
42	40
…	…
54	52	M = V S U^T (idempotence of transposition,
55	53	symmetry of diagonal matrix S)
56
57	54	*/
58	55
59	56	if (M.rows() >= M.columns()) {
60		A = M;
	57	A = (Matrix)M.Clone();
61	58	transpose_ = false;
62	59	} else {
…	…
69	66
70	67	// we're sure that m_ >= n_
71
72	68	s_ = new Vector(n_);
73	69	U_ = new Matrix(m_, n_, 0.0);
…	…
79	75	// Reduce A to bidiagonal form, storing the diagonal elements
80	76	// in s and the super-diagonal elements in e.
81
82	77	int nct = Math.Min(m_ - 1, n_);
83	78	int nrt = Math.Max(0, n_ - 2);
…	…
107	102
108	103	// Apply the transformation.
109
110	104	double t = 0;
111	105	for (i = k; i < m_; i++) {
…	…
120	114	// Place the k-th row of A into e for the
121	115	// subsequent calculation of the row transformation.
122
123	116	e[j] = A[k, j];
124	117	}
125	118	if (k < nct) {
126	119
127		// Place the transformation in U for subsequent back
128		// multiplication.
129
	120	// Place the transformation in U for subsequent back multiplication.
130	121	for (i = k; i < m_; i++) {
131	122	U_[i, k] = A[i, k];
…	…
134	125	if (k < nrt) {
135	126
136		// Compute the k-th row transformation and place the
137		// k-th super-diagonal in e[k].
	127	// Compute the k-th row transformation and place the k-th super-diagonal in e[k].
138	128	// Compute 2-norm without under/overflow.
139	129	e[k] = 0;
…	…
154	144
155	145	// Apply the transformation.
156
157	146	for (i = k + 1; i < m_; i++) {
158	147	work[i] = 0.0;
…	…
171	160	}
172	161
173		// Place the transformation in V for subsequent
174		// back multiplication.
175
	162	// Place the transformation in V for subsequent back multiplication.
176	163	for (i = k + 1; i < n_; i++) {
177	164	V_[i, k] = e[i];
…	…
181	168
182	169	// Set up the final bidiagonal matrix or order n.
183
184	170	if (nct < n_) {
185	171	s_[nct] = A[nct, nct];
…	…
191	177
192	178	// generate U
193
194	179	for (j = nct; j < n_; j++) {
195	180	for (i = 0; i < m_; i++) {
…	…
226	211
227	212	// generate V
228
229	213	for (k = n_ - 1; k >= 0; --k) {
230	214	if ((k < nrt) & (e[k] != 0.0)) {
…	…
269	253	break;
270	254	}
271		if (Math.Abs(e[k]) <= eps * (Math.Abs(s_[k]) +
272		Math.Abs(s_[k + 1]))) {
	255	if (Math.Abs(e[k]) <= eps * (Math.Abs(s_[k]) + Math.Abs(s_[k + 1]))) {
273	256	e[k] = 0.0;
274	257	break;

trunk/QLNet/QLNet/Methods/montecarlo/longstaffschwartzpathpricer.cs

r223	r224
35	35	reproducing results available in web/literature
36	36	*/
37		public class LongstaffSchwartzPathPricer<PathType> : PathPricer<PathType> where PathType : Path {
	37	public class LongstaffSchwartzPathPricer<PathType> : PathPricer<PathType> where PathType : Path, ICloneable {
38	38	protected bool calibrationPhase_;
39	39	protected EarlyExercisePathPricer<PathType> pathPricer_;
…	…
42	42	protected List<double> dF_;
43	43
44		protected List<PathType> paths_;
	44	protected List<PathType> paths_ = new List<PathType>();
45	45	protected List<Func<double, double>> v_;
46	46
…	…
63	63	if (calibrationPhase_) {
64	64	// store paths for the calibration
65		paths_.Add(~~path~~);
	65	paths_.Add((PathType)path.Clone());
66	66	// result doesn't matter
67	67	return 0.0;

trunk/QLNet/QLNet/Methods/montecarlo/path.cs

r192	r224
28	28	\note the path includes the initial asset value as its first point.
29	29	*/
30		public class Path {
	30	public class Path : ICloneable {
31	31	private TimeGrid timeGrid_;
32	32	private Vector values_;
…	…
66	66	//! time grid
67	67	public TimeGrid timeGrid() { return timeGrid_; }
	68
	69	// ICloneable interface
	70	public object Clone() {
	71	Path temp = (Path)this.MemberwiseClone();
	72	temp.values_ = new Vector(this.values_);
	73	return temp;
	74	}
68	75	}
69	76	}

trunk/QLNet/QLNet/Methods/montecarlo/sample.cs

r192	r224
25	25	//! weighted sample
26	26	/! \ingroup mcarlo /
27		public struct Sample<T> : ICloneable {
	27	public struct Sample<T> {
	28	public T value;
	29	public double weight;
	30
28	31	public Sample(T value_, double weight_) {
29	32	value = value_;
30	33	weight = weight_;
31	34	}
32		~~public T value;~~
33		~~public double weight;~~
34
35		~~public object Clone() { return this.MemberwiseClone(); }~~
36	35	}
37	36	}

trunk/QLNet/QLNet/Pricingengines/vanilla/mcamericanengine.cs

r223	r224
124	124
125	125	// the payoff gives an additional value
126		v_.Add(~~payoff.value~~);
	126	v_.Add(this.payoff);
127	127
128	128	StrikedTypePayoff strikePayoff = payoff_ as StrikedTypePayoff;
…	…
137	137	public double value(Path path, int t) { return payoff(state(path, t)); }
138	138	public List<Func<double, double>> basisSystem() { return v_; }
139
140		protected double payoff(double state) { return state/scalingValue_; }
	139	protected double payoff(double state) { return payoff_.value(state / scalingValue_); }
141	140	}
142	141
…	…
144	143	//! Monte Carlo American engine factory
145	144	//template <class RNG = PseudoRandom, class S = Statistics>
	145	public class MakeMCAmericanEngine<RNG> : MakeMCAmericanEngine<RNG, Statistics>
	146	where RNG : IRSG, new() {
	147	public MakeMCAmericanEngine(GeneralizedBlackScholesProcess process) : base(process) { }
	148	}
	149
146	150	public class MakeMCAmericanEngine<RNG, S>
147	151	where RNG : IRSG, new()
…	…
204	208	return this;
205	209	}
206		~~//public MakeMCAmericanEngine withAntitheticVariate(bool b = true); b) {~~
	210	public MakeMCAmericanEngine<RNG, S> withAntitheticVariate() { return withAntitheticVariate(true); }
207	211	public MakeMCAmericanEngine<RNG, S> withAntitheticVariate(bool b) {
208	212	antithetic_ = b;

trunk/QLNet/Test2008/Test2008.csproj

r204	r224
3	3	<Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
4	4	<Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
5		<ProductVersion>9.0.~~21022~~</ProductVersion>
	5	<ProductVersion>9.0.30428</ProductVersion>
6	6	<SchemaVersion>2.0</SchemaVersion>
7	7	<ProjectGuid>{2EF8B45B-940A-4B77-8776-E8CE0036961E}</ProjectGuid>
…	…
41	41	</ItemGroup>
42	42	<ItemGroup>
	43	<Compile Include="T_LinearLeastSquaresRegression.cs" />
43	44	<Compile Include="T_Optimizers.cs" />
44	45	<Compile Include="Properties\AssemblyInfo.cs" />

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Changeset 224

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