French Rail Projects Rely on @RISK

French Rail PPP Projects Rely on Traffic and Revenue Risk Analysis using @RISKFrance is the largest country in Western Europe and the European Union (EU), and, with a population of roughly 67 million, it’s the second-most populous country in the EU. Naturally, building and upgrading rail systems across the country is logistically challenging, requires large amounts of financing, and often involves complicated partnerships between public and private stakeholders.

Recently, a number of long-distance, high-speed rail projects were analyzed using @RISK to fully weigh the challenges each project faced. “@RISK allowed us to take all the risks into account and to assess the whole ‘Value at Risk’ for the project financier,” says Dr. Lionel Clément, who helped spear-head the analysis. Clément is an Associate at Transae, a consultancy in transport economics. The project financer is Réseau Ferré de France (RFF), a state-owned company that owns and manages the French railway sector.

To evaluate the risk of a potential rail-line project, Clément and his colleague Jean-Eric Morain ran calculations that compared the projects at hand with a reference situation (no rail-line project) over a theoretical period of 50 years. They relied on historical data and expert feedback on many existing French HSLs. The simulation showed the cash flow patterns over this theoretical 50 years, consolidating the project’s Value at Risk into one distribution that included all the different risks involved in the project, such as capital expenditures, operating costs, traffic and revenue risks, general and over inflation risks (linked to each budget item), as well as contractual and qualitative risks, such as interface and contractual risks.

After running the simulation, Clément and Morain had a Value at Risk curve for the project with a 5% discount rate for the next 50 years.

Clément says that @RISK enabled the efficient analysis of huge amounts of varied data: “@RISK allows us to handle a huge quantity of data inputs, to calibrate lots of risk factors, to deal with correlations, and to conduct a large number of tests to stabilize the complex model developed for each of Transae’s high speed rail projects.”

Read the complete case study here.

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