1 Risk in the MOLINO André de Palma UCP & Ecole Nationale des Ponts et Chaussée Lætitia Andrieu CERMICS-ENPC Nathalie Picard University of Cergy-Pontoise (UCP) December 9, 2005

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3 Two types of evaluations Socio-economic analysis Financial analysis

4 Motivation

5 Large-scale projects Large amount of failure of large projects: Suez canal, Eurotunnel Sources of uncertainty –Demand uncertainty –Supply uncertainty –Micro and macro shocks Risk (actual/perceived) is not well taken into account in current CBA: High risk should be associated with a high return: computation of financial /economical compensation (monetarization)?

6 Check-list # 1: sources of randomness Demand Production costs Industry structure and regulation Execution time Economic variables (macro-economic, regional) Financial variables Human resources for the management of the project

7 Check-list # 2: sources of randomness 1.Evaluation of secondary infrastructure 2.Accompanying measures 3.VOT, schedule delay costs 4.Value of external costs : accidents, human life, environment costs, etc. 5.Market: regulation, potential entry, etc.

8 Tools to take risk into account 1.Sensitivity analysis 2.Scenarios 3.Capital asset pricing model (CAPM) 4.Our suggestions – based on Monte-Carlo simulations (discussed later) - Confidence interval and - “Value at Risk” & “Conditional Value at Risk”

9 Empirical analysis Behavior towards risk and towards equity are interrelated A NR Online evaluation of risk with  Laboratory experiments about risk sharing Proposed: risk taking for decision maker: (in)formal interview

10 Practical issues Implementation A simple manner to incorporate risk in the MOLINO model

11 Cost variability Use (historical) data base on predicted costs and actual costs. Based on this information, determine the (pdf) probability density function of the cost functions.

12 Short run: travel time variability Deterministic case: demand depends on travel time, endogeneous but deterministic! If travel time varies from day to day (stationary process): assume a mean-variance model (CARA and normal distributions)  VOR (Small, 2005, Econometrica)

13 Medium long run: Demand variability Estimation of demand: where Y represents macroeconomics variables (growth of GNP, price of oil, etc.)  represents random shocks (opening of new markets, technological shock, etc.)

14 Variability of estimated demand Demand depends on parameters and macro variables estimated and predicted with more or less accuracy Autoregressive process, cumulated errors  Variance increases with time (e.g. linearly for Brownian motion)

15 Demand estimates over time t Demand

16 Micro-simulation (Monte Carlo) Generate sets of random parameters and demand values from a joint distribution allowing for correlations and fat tails (e.g. double exponential  extreme risks) Compute the distribution of relevant output variables (such as revenues, benefit, welfare, …) for each set of random parameters and demand values

17 Reminder: Value at risk: VaR Definition: maximum amount of lost acceptable for a project under “normal conditions” For example, if the VaR is 5 % for a critical value of q  = 100, this means that with a probability of 5 %, the cost will be larger than 100 for the time horizon considered

18 Micro-simulation (Results) 1.Eliminate the 2.5% larger values and 2.5% lower values to get a 95% bilateral confidence interval for each output variable 2.Select the  % worst cases to compute the Value at Risk  Implementation envisaged in the MOLINO model

19 Micro-simulation (Speed) Convergence requires about 7 iterations with an accuracy of Without Nash equilibrium, and with 10 origin- destination, this means about 14 hours, for iterations.

20 Questions