2017/06/06 by Yaxiong Zeng, Zeng, Yaxiong, Diego Klabjan +1 · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Monetary Policy and Economic Impact #Stochastic processes and financial applications #cs.LG #q-fin.CP #stat.ML
paper · pdf · doi:10.48550/arxiv.1706.01833
34 Pages
openalex publication_date 2017/06/06 · arxiv created 2018/06/07 · arxiv updated 2018/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface (IVS). The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow efficient online adaptive learning by embedding the idea of local fitness and budget maintenance to dynamically update support vectors upon pattern drifts. For algorithm acceleration, we implement its most computationally intensive parts in a Field Programmable Gate Arrays hardware, where a 132x speedup over CPU is achieved during online prediction. Using intraday tick data from the E-mini S&P 500 options market, we show that the Gaussian kernel outperforms the linear kernel in regulating the size of support vectors, and that our empirical IVS algorithm beats two competing online methods with regards to model complexity and regression errors (the mean absolute percentage error of our algorithm is up to 13%). Best results are obtained at the center of the IVS grid due to its larger number of adjacent support vectors than the edges of the grid. Sensitivity analysis is also presented to demonstrate how hyper parameters affect the error rates and model complexity.