Cloud-Native Financial Econometrics: A Serverless Machine Learning Architecture for Dynamic Haircut Determination and Empirical Asset Pricing in Shadow Banking

The micro-structure of shadow banking relies critically on the joint determination of haircuts and interest rates for collateralized loans. Traditional macroeconomic models analyzing these financial mechanisms frequently rely on static, low-frequency historical datasets, which fail to capture the high-velocity liquidity shocks that characterize modern derivative and bond markets. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for empirical asset pricing. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests high-frequency market tick data, option-implied consumption metrics, and collateral asset volatility. The system translates these inputs into a dynamic, algorithmic calculation of optimal haircuts and risk premiums. Preliminary architectural evaluations demonstrate that decoupling the econometric inference engine from legacy batch-processing environments significantly accelerates the detection of systemic shadow banking risks, providing financial economists with a deterministic, highly scalable technological foundation for modeling complex financial intermediation.