Online Local Volatility Calibration by Convex Regularization with Morozov's Principle and Convergence Rates
For quantitative finance practitioners, it provides a theoretically grounded online calibration technique for the Dupire local volatility model, though the contribution is incremental as it extends existing regularization methods to an online setting.
The paper develops an online calibration method for local volatility surfaces from option prices using convex Tikhonov regularization, proving convergence rates with respect to noise and a discrepancy-based parameter choice, validated by numerical tests.
We address the inverse problem of local volatility surface calibration from market given option prices. We integrate the ever-increasing flow of option price information into the well-accepted local volatility model of Dupire. This leads to considering both the local volatility surfaces and their corresponding prices as indexed by the observed underlying stock price as time goes by in appropriate function spaces. The resulting parameter to data map is defined in appropriate Bochner-Sobolev spaces. Under this framework, we prove key regularity properties. This enable us to build a calibration technique that combines online methods with convex Tikhonov regularization tools. Such procedure is used to solve the inverse problem of local volatility identification. As a result, we prove convergence rates with respect to noise and a corresponding discrepancy-based choice for the regularization parameter. We conclude by illustrating the theoretical results by means of numerical tests.