Distributed Financial Risk Assessment
This project aims to implement a distributed framework for financial risk assessment using Monte Carlo simulations to estimate Value at Risk (VaR) and Conditional Value at Risk (CVaR). By leveraging Spark’s distributed computing capabilities, we efficiently parallelize processing of large-scale market data, simulating millions of scenarios to generate robust risk metrics. The framework also incorporates techniques to optimize computational performance and ensures scalability for real-world financial datasets, enabling timely and reliable risk evaluation for decision-making. All computations are run on the NYU dataproc cluster hosted on GCP.