Simulating Murex Risk Pipelines in Python: Greeks, VaR, sVaR and Aggregation

Managing risk in today’s fast-paced trading environments is a significant challenge. Think about the sheer volume of data generated daily — positions across thousands of instruments, constantly shifting market prices, complex financial derivatives and an organizational structure spanning multiple portfolios, trading desks and business units. In many institutions I’ve worked with, systems like Murex act as the central nervous system, capturing trades and providing foundational data for risk analysis. However, transforming that raw operational data into meaningful, aggregated risk metrics like sensitivities (the Greeks) and Value at Risk (VaR) requires a robust and well-defined process.

The core task involves not just calculating risk for individual trades but understanding how these risks interact and accumulate across the entire firm. We need to know our Delta exposure to market movements, the Gamma risk associated with sharp price changes, the Vega sensitivity to volatility shifts and ultimately, the potential loss we might face under adverse market conditions (VaR) or specific historical stress events (Stressed VaR or sVaR). Aggregating these figures correctly, respecting diversification effects, up through the hierarchy — from specific portfolios, grouped into trading desks and finally consolidated at the unit level — is crucial for effective capital allocation and limit monitoring. This tutorial simulates exactly that complex data circuit. We will build a Python pipeline that…