Using stochastic decision trees to quantify environmental risks

Alex will share a step by step case study on how to model environmental risks using stochastic decision trees using free SIPMath. 

The introduction of quantitative risk analysis changed how investment decisions are prepared and discussed at the Board (stochastic stress tests and NPV@risk), how insurance decision are made (loss exceedance curves, claims probability distributions), how performance is measures and businesses are remunerated (risk-adjusted net margins, risk capital charges), how large IT projects are implemented (cost and schedule quant risk analysis), how pricing and accounts receivables is managed (cVaR), how compliance issues are identified and managed (stochastic decision trees), limiting market risk exposure (VaR, limits, stop losses), how HSE risks are quantified and mitigated (stochastic risk models) and so on.

Alex's team applies stochastic analysis to key assumptions affecting project decisions (e.g. macro assumptions / commodity prices, HSE, operating / capital costs, capacity utilization / run rates, impact on environment). Stochastic models more accurately characterize the unpredictable nature of market behaviors and help to reduce bias induced by decision makers. Stochastic models yield a more holistic risk-adjusted forecasts and allow EuroChem to identify and understand the value and risk drivers.