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Risk in the MOLINO

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. Two types of evaluations. Socio-economic analysis Financial analysis. Motivation. Large-scale projects.

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Risk in the MOLINO

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  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

  2. Two types of evaluations • Socio-economic analysis • Financial analysis

  3. Motivation

  4. 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)?

  5. 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

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

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

  8. Empirical analysis • Behavior towards risk and towards equity are interrelated • ANR • Online evaluation of risk with www.RiskDynaMetrics.com  Laboratory experiments about risk sharing • Proposed: risk taking for decision maker: (in)formal interview

  9. Practical issuesImplementation A simple manner to incorporate risk in the MOLINO model

  10. 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.

  11. 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) Q <->VOR (Small, 2005, Econometrica)

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

  13. 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)

  14. Demand estimates over time Demand t

  15. 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

  16. 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 q5% = 100, this means that with a probability of 5 %, the cost will be larger than 100 for the time horizon considered

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

  18. Micro-simulation (Speed) Convergence requires about 7 iterations with an accuracy of 10-2. Without Nash equilibrium, and with 10 origin-destination, this means about 14 hours, for 100 000 iterations.

  19. Questions

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