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finmath lib is a Mathematical Finance Library in Java.
It provides algorithms and methodologies related to mathematical finance.
/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 07.03.2015
*/
package net.finmath.marketdata.model.curves;
import java.time.LocalDate;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import net.finmath.marketdata.model.AnalyticModel;
import net.finmath.marketdata.model.curves.CurveInterpolation.ExtrapolationMethod;
import net.finmath.marketdata.model.curves.CurveInterpolation.InterpolationEntity;
import net.finmath.marketdata.model.curves.CurveInterpolation.InterpolationMethod;
import net.finmath.time.daycount.DayCountConvention;
import net.finmath.time.daycount.DayCountConvention_ACT_365;
/**
* The curve returns a value depending on the month of the time argument, that is,
* a call getValue(model, time) will map time to a 30/360 value using
* the day and month only and delegate the call to a given base curve.
*
* The value returned then is baseCurve.getValue(model, season)
* where
* season = (month-1) / 12.0 + (day-1) / (double)numberOfDays / 12.0;
*
* The base curve has to be constructed according to this time convention (e.g.,
* as a piecewise constant curve with values at i / 12 for i=1,...,12 using
* {@link CurveInterpolation.InterpolationMethod} with PIECEWISE_CONSTANT_RIGHTPOINT.
*
* @author Christian Fries
* @version 1.0
*/
public class SeasonalCurve extends AbstractCurve implements Curve {
private static final long serialVersionUID = 4021745191829488593L;
private Curve baseCurve;
/**
* A builder (following the builder pattern) for SeasonalCurve objects.
* Allows to successively construct a curve object by adding points to its base points.
*
* @author Christian Fries
*/
public static class Builder extends CurveInterpolation.Builder implements CurveBuilder {
private SeasonalCurve curve = null;
/**
* Create a CurveBuilder from a given seasonalCurve.
*
* @param seasonalCurve The seasonal curve from which to copy the fixed part upon build().
* @throws CloneNotSupportedException Thrown, when the base curve could not be cloned.
*/
public Builder(final SeasonalCurve seasonalCurve) throws CloneNotSupportedException {
super((CurveInterpolation)(seasonalCurve.baseCurve));
curve = seasonalCurve;
}
@Override
public Curve build() throws CloneNotSupportedException {
final SeasonalCurve buildCurve = curve.clone();
buildCurve.baseCurve = super.build();
curve = null;
return buildCurve;
}
}
/**
* Create a monthly seasonality adjustment curve by estimating historic log-returns from monthly index fixings.
*
* @param name The name of this curve.
* @param referenceDate The reference date for this curve (i.e. t=0).
* @param indexFixings A Map<Date, Double> of consecutive monthly index fixings.
* @param numberOfYearsToAverage The number of years over which monthly log returns should be averaged.
*/
public SeasonalCurve(final String name, final LocalDate referenceDate, final Map indexFixings, final int numberOfYearsToAverage) {
super(name, referenceDate);
final double[] seasonalAdjustmentCalculated = SeasonalCurve.computeSeasonalAdjustments(referenceDate, indexFixings, numberOfYearsToAverage);
final double[] seasonTimes = new double[12];
final double[] seasonValue = new double[12];
double seasonValueCummulated = 1.0;
for(int j=0; j<12; j++) {
seasonValueCummulated *= Math.exp(seasonalAdjustmentCalculated[j]/12.0);
seasonTimes[j] = j/12.0;
seasonValue[j] = seasonValueCummulated;
}
baseCurve = new CurveInterpolation(name + "-seasonal-base", referenceDate, InterpolationMethod.PIECEWISE_CONSTANT_LEFTPOINT, ExtrapolationMethod.CONSTANT, InterpolationEntity.VALUE, seasonTimes, seasonValue);
}
/**
* @param name The name of this curve.
* @param referenceDate The reference date for this curve (i.e. t=0).
* @param baseCurve The base curve, i.e., the discount curve used to calculate the seasonal adjustment factors.
*/
public SeasonalCurve(final String name, final LocalDate referenceDate, final Curve baseCurve) {
super(name, referenceDate);
this.baseCurve = baseCurve;
}
@Override
public double[] getParameter() {
return baseCurve.getParameter();
}
@Override
public void setParameter(final double[] parameter) {
baseCurve.setParameter(parameter);
}
@Override
public double getValue(final AnalyticModel model, final double time) {
final LocalDate calendar = getReferenceDate().plusDays((int) Math.round(time*365));
final int month = calendar.getMonthValue(); // Note: month = 1,2,3,...,12
final int day = calendar.getDayOfMonth(); // Note: day = 1,2,3,...,numberOfDays
final int numberOfDays = calendar.lengthOfMonth();
final double season = (month-1) / 12.0 + (day-1) / (double)numberOfDays / 12.0;
return baseCurve.getValue(model, season);
}
@Override
public Curve getCloneForParameter(final double[] value) throws CloneNotSupportedException {
final SeasonalCurve newCurve = clone();
newCurve.baseCurve = baseCurve.getCloneForParameter(value);
return newCurve;
}
@Override
public SeasonalCurve clone() throws CloneNotSupportedException {
return new SeasonalCurve(this.getName(), this.getReferenceDate(), (Curve) baseCurve.clone());
}
@Override
public Builder getCloneBuilder() throws CloneNotSupportedException {
return new Builder(this);
}
public static double[] computeSeasonalAdjustments(final LocalDate referenceDate, final Map indexFixings, final int numberOfYearsToAverage) {
final DayCountConvention modelDcc = new DayCountConvention_ACT_365(); // Not needed: remove
final double[] fixingTimes = new double[indexFixings.size()]; // Not needed: remove
final double[] realizedCPIValues = new double[indexFixings.size()];
int i = 0;
final List fixingDates = new ArrayList<>(indexFixings.keySet());
Collections.sort(fixingDates);
for(final LocalDate fixingDate : fixingDates) {
fixingTimes[i] = modelDcc.getDaycountFraction(referenceDate, fixingDate);
realizedCPIValues[i] = indexFixings.get(fixingDate).doubleValue();
i++;
}
final LocalDate lastMonth = fixingDates.get(fixingDates.size()-1);
return computeSeasonalAdjustments(realizedCPIValues, lastMonth.getMonthValue(), numberOfYearsToAverage);
}
/**
* Computes annualized seasonal adjustments from given monthly realized CPI values.
*
* @param realizedCPIValues An array of consecutive monthly CPI values (minimum size is 12*numberOfYearsToAverage))
* @param lastMonth The index of the last month in the sequence of realizedCPIValues (corresponding to the enums in {@link java.time.Month}).
* @param numberOfYearsToAverage The number of years to go back in the array of realizedCPIValues.
* @return Array of annualized seasonal adjustments, where [0] corresponds to the adjustment for from December to January.
*/
public static double[] computeSeasonalAdjustments(final double[] realizedCPIValues, final int lastMonth, final int numberOfYearsToAverage) {
/*
* Cacluate average log returns
*/
final double[] averageLogReturn = new double[12];
Arrays.fill(averageLogReturn, 0.0);
for(int arrayIndex = 0; arrayIndex < 12*numberOfYearsToAverage; arrayIndex++){
final int month = (((((lastMonth-1 - arrayIndex) % 12) + 12) % 12));
final double logReturn = Math.log(realizedCPIValues[realizedCPIValues.length - 1 - arrayIndex] / realizedCPIValues[realizedCPIValues.length - 2 - arrayIndex]);
averageLogReturn[month] += logReturn/numberOfYearsToAverage;
}
/*
* Normalize
*/
double sum = 0.0;
for(int index = 0; index < averageLogReturn.length; index++){
sum += averageLogReturn[index];
}
final double averageSeasonal = sum / averageLogReturn.length;
final double[] seasonalAdjustments = new double[averageLogReturn.length];
for(int index = 0; index < seasonalAdjustments.length; index++){
seasonalAdjustments[index] = averageLogReturn[index] - averageSeasonal;
}
// Annualize seasonal adjustments
for(int index = 0; index < seasonalAdjustments.length; index++){
seasonalAdjustments[index] = seasonalAdjustments[index] * 12;
}
return seasonalAdjustments;
}
}