# Calibration of Dupire local volatility model using genetic algorithm of optimization

• Author(s)
• DOI
http://dx.doi.org/10.21511/nfmte.7.2018.01
• Article Info
Volume 7 2018, Issue #1, pp. 1-20
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The problem of calibration of local volatility model of Dupire has been formalized. It uses genetic algorithm as alternative to regularization approach with further application of gradient descent algorithm. Components that solve Dupire’s partial differential equation that represents dynamics of underlying asset’s price within Dupire model have been built. This price depends in particular on values of volatility parameters. Local volatility is parametrized in two dimensions (by Dupire model): time to maturity of the option and strike price (execution price). On maturity axis linear interpolation is used while on strike axis we use B-Splines. Genetic operators of mutation and selection are then applied to parameters of B-Splines. Resulting parameters allow us to obtain the values of local volatility both in knot points and intermediate points using interpolation techniques. Then we solve Dupire equation and calculate model values of option prices. To calculate cost function we simulate market values of option prices using classic Black-Scholes model. An experimental research to compare simulated market volatility and volatility obtained by means of calibration of Dupire model has been conducted. The goal is to estimate the precision of the approach and its usability in practice. To estimate the precision of obtained results we use a measure based on average deviation of modeled local volatility from values used to simulate market prices of the options. The research has shown that the approach to calibration using genetic algorithm of optimization requires some additional manipulations to achieve convergence. In particular it requires non-uniform discretization of the space of model parameters as well as usage of de Boor interpolation. Value 0.07 turns out to be the most efficient mutation parameter. Using this parameter leads to quicker convergence. It has been proved that the algorithm allows precise calibration of local volatility surface from option prices.

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• JEL Classification (Paper profile tab)
C15, C61, G12
• References
10
• Tables
0
• Figures
10
• Figure 1. Dupire price surface
• Figure 2. Discrepancy surface solver
• Figure 3. Basis functions on uniform knots
• Figure 4. Spline evaluated at intermediate points
• Figure 5. Program workflows
• Figure 6. Local volatility surface
• Figure 7. Local volatility surface: view K, σ
• Figure 8. Local volatility surface: Histogram over all values
• Figure 9. Local volatility surface: Histogram over index of K ∈ [20; 30]
• Figure 10. Results of the calibration
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