com.opengamma.analytics.financial.model.option.pricing.analytic.LogOptionModel.java Source code

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Here is the source code for com.opengamma.analytics.financial.model.option.pricing.analytic.LogOptionModel.java

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/**
 * Copyright (C) 2009 - present by OpenGamma Inc. and the OpenGamma group of companies
 * 
 * Please see distribution for license.
 */
package com.opengamma.analytics.financial.model.option.pricing.analytic;

import org.apache.commons.lang.Validate;

import com.opengamma.analytics.financial.model.option.definition.LogOptionDefinition;
import com.opengamma.analytics.financial.model.option.definition.StandardOptionDataBundle;
import com.opengamma.analytics.math.function.Function1D;
import com.opengamma.analytics.math.statistics.distribution.NormalDistribution;
import com.opengamma.analytics.math.statistics.distribution.ProbabilityDistribution;

/**
 * 
 * Pricing model for log options.
 * 
 */
public class LogOptionModel extends AnalyticOptionModel<LogOptionDefinition, StandardOptionDataBundle> {
    private final ProbabilityDistribution<Double> _normalProbabilityDistribution = new NormalDistribution(0, 1);

    @Override
    public Function1D<StandardOptionDataBundle, Double> getPricingFunction(final LogOptionDefinition definition) {
        Validate.notNull(definition);
        final Function1D<StandardOptionDataBundle, Double> pricingFunction = new Function1D<StandardOptionDataBundle, Double>() {

            @SuppressWarnings("synthetic-access")
            @Override
            public Double evaluate(final StandardOptionDataBundle data) {
                Validate.notNull(data);
                final double s = data.getSpot();
                final double k = definition.getStrike();
                final double t = definition.getTimeToExpiry(data.getDate());
                final double b = data.getCostOfCarry();
                final double r = data.getInterestRate(t);
                final double sigma = data.getVolatility(t, k);
                final double df = Math.exp(-r * t);
                final double sigmaT = sigma * Math.sqrt(t);
                final double x = (Math.log(s / k) + t * (b - sigma * sigma * 0.5)) / sigmaT;
                return df * sigmaT
                        * (_normalProbabilityDistribution.getPDF(x) + x * _normalProbabilityDistribution.getCDF(x));
            }

        };
        return pricingFunction;
    }

}