Smiling at Evolution

Abstract: We generate a reliable implied volatility surface without arbitrage in space and in time by parameterising a mixture of shifted lognormal densities under constraints and use a Differential Evolution algorithm to calibrate the model's parameters to a finite set of option prices. It is used for marking options not directly visible as well as for computing a proper deterministic local volatility. To do so, we devise an evolutionary algorithm handling constraints in a simple and efficient way. Using some of the improvements made to the DE algorithm and taking advantage of the specific structure of our objective function, we use special operators to help satisfy the equality constraints together with feasibility rules to handle the inequality constraints. Finally, we propose a modified algorithm for solving our optimisation problem under constraints which, after testing on real market data, greatly improves its performances.

[1]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[2]  Zbigniew Michalewicz,et al.  A Note on Usefulness of Geometrical Crossover for Numerical Optimization Problems , 1996, Evolutionary Programming.

[3]  Asa Gray,et al.  The variation of animals and plants under domestication / By Charles Darwin. , 2022 .

[4]  Carlos A. Coello Coello,et al.  Boundary Search for Constrained Numerical Optimization Problems With an Algorithm Inspired by the Ant Colony Metaphor , 2009, IEEE Transactions on Evolutionary Computation.

[5]  Pierre Giot,et al.  Market Models: A Guide to Financial Data Analysis , 2003 .

[6]  Carlos A. Coello Coello,et al.  An Algorithm Based on Differential Evolution for Multi-Objective Problems , 2005 .

[7]  Hans Buehler,et al.  Equity Hybrid Derivatives , 2007 .

[8]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[9]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[10]  A. Oyama,et al.  Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems Eurogen 2005 New Constraint-handling Method for Multi-objective Multi-constraint Evolutionary Optimization and Its Application to Space Plane Design , 2022 .

[11]  Peter Tankov Calibration de Modeles et Couverture de Produits Derives , 2006 .

[12]  Rama Cont,et al.  Calibration of Jump-Diffusion Option Pricing Models: A Robust Non-Parametric Approach , 2002 .

[13]  David M. Kreps,et al.  Martingales and arbitrage in multiperiod securities markets , 1979 .

[14]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[15]  E.,et al.  Estimating Implied Volatility Directly from "Nearest-to-the-Money" Commodity Option Premiums , 1988 .

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[17]  Carlos A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[18]  Toby Daglish,et al.  Volatility surfaces: theory, rules of thumb, and empirical evidence , 2007 .

[19]  Carlos A. Coello Coello,et al.  Simple Feasibility Rules and Differential Evolution for Constrained Optimization , 2004, MICAI.

[20]  Srikrishna Subramanian,et al.  Self-Adaptive Differential Evolution Based Power Economic Dispatch of Generators with Valve-Point Effects and Multiple Fuel Options , 2007 .

[21]  S. Ben Hamida,et al.  Recovering Volatility from Option Prices by Evolutionary Optimization , 2004 .

[22]  D. Bloch A Practical Guide to Implied and Local Volatility , 2010 .

[23]  Hans Buehler,et al.  Equity Hybrid Derivatives: Overhaus/Equity , 2012 .

[24]  Allan M. Malz Estimating the Probability Distribution of the Future Exchange Rate from Option Prices , 1997 .

[25]  Carlos A. Coello Coello,et al.  Modified Differential Evolution for Constrained Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[26]  Shalom Benaim,et al.  An arbitrage-free method for smile extrapolation , 2009 .

[27]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[29]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[30]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[31]  Riccardo Rebonato,et al.  Unconstrained fitting of implied volatility surfaces using a mixture of normals , 2004 .

[32]  C. Coello,et al.  CONSTRAINT-HANDLING USING AN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION TECHNIQUE , 2000 .

[33]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[34]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[35]  C. Darwin The Variation of Animals and Plants under Domestication: DOMESTIC PIGEONS , 1868 .

[36]  John F. Hart,et al.  Computer Approximations , 1978 .

[37]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[38]  William H. Press,et al.  Numerical recipes , 1990 .