Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling
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In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface. The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow online adaptive learning by embedding the idea of local fitness and budget maintenance. To accelerate our algorithm, we implement its most computationally intensive parts in a Field Programmable Gate Arrays hardware. Using intraday tick data from the E-mini S&P 500 options market, we show that our algorithm outperforms two competing methods and the Gaussian kernel is a better choice than the linear kernel. Sensitivity analysis is also presented to demonstrate how hyper parameters affect the error rates and the number of support vectors in our models.
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