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/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 15.07.2012
*/
package net.finmath.timeseries.models.parametric;
import java.io.Serializable;
import java.util.HashMap;
import java.util.Map;
import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.PointValuePair;
import net.finmath.optimizer.LevenbergMarquardt;
import net.finmath.optimizer.Optimizer;
import net.finmath.optimizer.SolverException;
import net.finmath.timeseries.HistoricalSimulationModel;
/**
* Log-normal process with GARCH(1,1) volatility.
*
* This class estimate the process
* \[
* \mathrm{d} \log(X) = \sigma(t) \mathrm{d}W(t)
* \]
* where \( \sigma \) is given by a GARCH(1,1) process from time discrete
* realizations \( X_{i} \). That is, given a time series of values \( X_{i} \)
* the GARCH(1,1) volatility of the log-returns \( \log(X_{i+1}/X_{i}) \) is
* estimated.
*
* @author Christian Fries
* @version 1.0
*/
public class GARCH implements HistoricalSimulationModel {
private final double[] values;
private final int windowIndexStart;
private final int windowIndexEnd;
private final int maxIterations = 1000000;
/**
* Create GARCH model estimated form the given time series of values.
*
* @param values Given set of values.
*/
public GARCH(final double[] values) {
this.values = values;
windowIndexStart = 0;
windowIndexEnd = values.length-1;
}
/**
* Create GARCH model estimated form the given time series of values.
*
* @param values Given set of values.
* @param windowIndexStart First index to consider in the given set of values.
* @param windowIndexEnd Last index to consider in the given set of values.
*/
public GARCH(final double[] values, final int windowIndexStart, final int windowIndexEnd) {
this.values = values;
this.windowIndexStart = windowIndexStart;
this.windowIndexEnd = windowIndexEnd;
}
@Override
public GARCH getCloneWithWindow(final int windowIndexStart, final int windowIndexEnd) {
return new GARCH(values, windowIndexStart, windowIndexEnd);
}
/**
* Get log likelihood of the sample time series for given model parameters.
*
* @param omega The parameter ω of the GARCH model.
* @param alpha The parameter α of the GARCH model.
* @param beta The parameter β of the GARCH model.
* @return The log likelihood of the times series under the specified GARCH model.
*/
public double getLogLikelihoodForParameters(final double omega, final double alpha, final double beta)
{
double logLikelihood = 0.0;
final double volScaling = 1.0;
double h = omega / (1.0 - alpha - beta);
for (int i = windowIndexStart+1; i <= windowIndexEnd-1; i++) {
final double eval = volScaling * (Math.log((values[i])/(values[i-1])));
h = (omega + alpha * eval * eval) + beta * h;
final double evalNext = volScaling * (Math.log((values[i+1])/(values[i])));
logLikelihood += - Math.log(h) - evalNext*evalNext / h;
}
logLikelihood += - Math.log(2 * Math.PI) * (windowIndexEnd-windowIndexStart);
logLikelihood *= 0.5;
return logLikelihood;
}
/**
* Returns the last estimate of the time series volatility.
*
* @param omega The parameter ω of the GARCH model.
* @param alpha The parameter α of the GARCH model.
* @param beta The parameter β of the GARCH model.
* @return Last residual, i.e., σ
*/
public double getLastResidualForParameters(final double omega, final double alpha, final double beta) {
final double volScaling = 1.0;
double h = omega / (1.0 - alpha - beta);
for (int i = windowIndexStart+1; i <= windowIndexEnd; i++) {
final double eval = volScaling * (Math.log((values[i])/(values[i-1])));
h = omega + alpha * eval * eval + beta * h;
}
return h;
}
public double[] getSzenarios(final double omega, final double alpha, final double beta) {
final double[] szenarios = new double[windowIndexEnd-windowIndexStart+1-1];
final double volScaling = 1.0;
double h = omega / (1.0 - alpha - beta);
double vol = Math.sqrt(h) * volScaling;
for (int i = windowIndexStart+1; i <= windowIndexEnd; i++) {
szenarios[i-windowIndexStart-1] = Math.log((values[i])/(values[i-1])) / vol;
final double eval = volScaling * (Math.log((values[i])/(values[i-1])));
h = omega + alpha * eval * eval + beta * h;
vol = Math.sqrt(h) * volScaling;
}
java.util.Arrays.sort(szenarios);
return szenarios;
}
public double[] getQuantilPredictionsForParameters(final double omega, final double alpha, final double beta, final double[] quantiles) {
final double[] szenarios = getSzenarios(omega, alpha, beta);
final double volScaling = 1.0;
final double h = omega / (1.0 - alpha - beta);
final double vol = Math.sqrt(h) * volScaling;
final double[] quantileValues = new double[quantiles.length];
for(int i=0; i getBestParameters() {
return getBestParameters(null);
}
/* (non-Javadoc)
* @see net.finmath.timeseries.HistoricalSimulationModel#getBestParameters(java.util.Map)
*/
@Override
public Map getBestParameters(final Map guess) {
// Create the objective function for the solver
class GARCHMaxLikelihoodFunction implements MultivariateFunction, Serializable {
private static final long serialVersionUID = 7072187082052755854L;
@Override
public double value(final double[] variables) {
/*
* Transform variables: The solver variables are in (-\infty, \infty).
* We transform the variable to the admissible domain for GARCH, that is
* omega > 0, 0 < alpha < 1, 0 < beta < (1-alpha), displacement > lowerBoundDisplacement
*/
final double omega = Math.exp(variables[0]);
final double mucorr = Math.exp(-Math.exp(-variables[1]));
final double muema = Math.exp(-Math.exp(-variables[2]));
final double beta = mucorr * muema;
final double alpha = mucorr - beta;
double logLikelihood = getLogLikelihoodForParameters(omega,alpha,beta);
// Penalty to prevent solver from hitting the bounds
logLikelihood -= Math.max(1E-30-omega,0)/1E-30;
logLikelihood -= Math.max(1E-30-alpha,0)/1E-30;
logLikelihood -= Math.max((alpha-1)+1E-30,0)/1E-30;
logLikelihood -= Math.max(1E-30-beta,0)/1E-30;
logLikelihood -= Math.max((beta-1)+1E-30,0)/1E-30;
return logLikelihood;
}
}
final GARCHMaxLikelihoodFunction objectiveFunction = new GARCHMaxLikelihoodFunction();
// Create a guess for the solver
double guessOmega = 1.0;
double guessAlpha = 0.2;
double guessBeta = 0.2;
if(guess != null) {
// A guess was provided, use that one
guessOmega = (Double)guess.get("Omega");
guessAlpha = (Double)guess.get("Alpha");
guessBeta = (Double)guess.get("Beta");
}
// Constrain guess to admissible range
guessOmega = restrictToOpenSet(guessOmega, 0.0, Double.MAX_VALUE);
guessAlpha = restrictToOpenSet(guessAlpha, 0.0, 1.0);
guessBeta = restrictToOpenSet(guessBeta, 0.0, 1.0-guessAlpha);
final double guessMucorr = guessAlpha + guessBeta;
final double guessMuema = guessBeta / (guessAlpha+guessBeta);
// Transform guess to solver coordinates
final double[] guessParameters = new double[3];
guessParameters[0] = Math.log(guessOmega);
guessParameters[1] = -Math.log(-Math.log(guessMucorr));
guessParameters[2] = -Math.log(-Math.log(guessMuema));
// Seek optimal parameter configuration
final Optimizer lm = new LevenbergMarquardt(guessParameters, new double[] { 1000.0 }, maxIterations, 2) {
private static final long serialVersionUID = 611999941537812214L;
@Override
public void setValues(final double[] arg0, final double[] arg1) {
arg1[0] = objectiveFunction.value(arg0);
}
};
double[] bestParameters = null;
final boolean isUseLM = false;
if(isUseLM) {
try {
lm.run();
} catch (final SolverException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
bestParameters = lm.getBestFitParameters();
}
else {
final org.apache.commons.math3.optimization.direct.CMAESOptimizer optimizer2 = new org.apache.commons.math3.optimization.direct.CMAESOptimizer();
try {
final PointValuePair result = optimizer2.optimize(
maxIterations,
objectiveFunction,
GoalType.MAXIMIZE,
guessParameters
);
bestParameters = result.getPoint();
} catch(final org.apache.commons.math3.exception.MathIllegalStateException e) {
System.out.println("Solver failed");
bestParameters = guessParameters;
}
}
// Transform parameters to GARCH parameters
final double omega = Math.exp(bestParameters[0]);
final double mucorr = Math.exp(-Math.exp(-bestParameters[1]));
final double muema = Math.exp(-Math.exp(-bestParameters[2]));
final double beta = mucorr * muema;
final double alpha = mucorr - beta;
final double[] quantiles = {0.01, 0.05, 0.5};
final double[] quantileValues = this.getQuantilPredictionsForParameters(omega, alpha, beta, quantiles);
final Map results = new HashMap<>();
results.put("Omega", omega);
results.put("Alpha", alpha);
results.put("Beta", beta);
results.put("Szenarios", this.getSzenarios(omega, alpha, beta));
results.put("Likelihood", this.getLogLikelihoodForParameters(omega, alpha, beta));
results.put("Vol", Math.sqrt(this.getLastResidualForParameters(omega, alpha, beta)));
results.put("Quantile=1%", quantileValues[0]);
results.put("Quantile=5%", quantileValues[1]);
results.put("Quantile=50%", quantileValues[2]);
// System.out.println(results.get("Likelihood") + "\t" + Arrays.toString(bestParameters));
return results;
}
private static double restrictToOpenSet(double value, final double lowerBond, final double upperBound) {
value = Math.max(value, lowerBond * (1.0+Math.signum(lowerBond)*1E-15) + 1E-15);
value = Math.min(value, upperBound * (1.0-Math.signum(upperBound)*1E-15) - 1E-15);
return value;
}
}
