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In this work, we design a machine learning based method, online adaptiveprimal support vector regression (SVR), to model the implied volatility surface(IVS). The algorithm proposed is the first derivation and implementation of anonline primal kernel SVR. It features enhancements that allow efficient onlineadaptive learning by embedding the idea of local fitness and budget maintenanceto dynamically update support vectors upon pattern drifts. For algorithmacceleration, we implement its most computationally intensive parts in a FieldProgrammable Gate Arrays hardware, where a 132x speedup over CPU is achievedduring online prediction. Using intraday tick data from the E-mini S&P 500options market, we show that the Gaussian kernel outperforms the linear kernelin regulating the size of support vectors, and that our empirical IVS algorithmbeats two competing online methods with regards to model complexity andregression errors (the mean absolute percentage error of our algorithm is up to13%). Best results are obtained at the center of the IVS grid due to its largernumber of adjacent support vectors than the edges of the grid. Sensitivityanalysis is also presented to demonstrate how hyper parameters affect the errorrates and model complexity.

Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling