Understanding options data processing: From raw data to volatility surfaces

This article provides an extensive view of options data processing, from data collection to volatility surface construction. It explains the complicated process of transforming raw market data into useful intelligence through data scrubbing, instrument mapping, and storage optimization. Furthermore, it discusses the extensive methods for developing volatile surfaces, such as quality control, kernel smoothing, and implied volatility calculation. It also highlights the improvement in terms of performance gains that such strategies provide while also examining applications in trading strategies, risk management, and derivatives pricing. The pipeline design issues and the trade-off between batch and real-time processing requirements are extensively discussed in the system architecture chapter. Cloud-native architectures, alternative data inclusion, and machine learning integration are trending and are likely to have a great impact on options data processing in the future.