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Simulation-based valuation of project finance: does model complexity really matter?

Author

Listed:
  • Weber, Florian
  • Schmid, Thomas
  • Pietz, Matthäus
  • Kaserer, Christoph
Abstract
This paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice. First, we present a simulation-based project finance valuation model. Second, we vary several model aspects in order to analyze their impact on the valuation result. For forecast complexity, we apply different volatility and correlation forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model. Regarding simulation complexity, the number of Monte Carlo iterations, the equity valuation method, and the time resolution are varied. We find that the applied volatility forecasting models have a strong influence on the expected net present value distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and equity valuation are both crucial when specifying a valuation model for project finance. For the number of Monte Carlo iterations, we demonstrate that 100,000 iterations are sufficient to obtain reliable results.

Suggested Citation

  • Weber, Florian & Schmid, Thomas & Pietz, Matthäus & Kaserer, Christoph, 2010. "Simulation-based valuation of project finance: does model complexity really matter?," CEFS Working Paper Series 2010-03, Technische Universität München (TUM), Center for Entrepreneurial and Financial Studies (CEFS).
    • Handle: RePEc:zbw:cefswp:201003
    as

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    References listed on IDEAS

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    3. Stefano Gatti & Alvaro Rigamonti & Francesco Saita & Mauro Senati, 2007. "Measuring Value‐at‐Risk in Project Finance Transactions," European Financial Management, European Financial Management Association, vol. 13(1), pages 135-158, January.
    4. Peter W. Glynn & Ward Whitt, 1992. "The Asymptotic Efficiency of Simulation Estimators," Operations Research, INFORMS, vol. 40(3), pages 505-520, June.
    5. Benjamin C. Esty, 2004. "Why Study Large Projects? An Introduction to Research on Project Finance," European Financial Management, European Financial Management Association, vol. 10(2), pages 213-224, June.
    6. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. repec:dau:papers:123456789/607 is not listed on IDEAS
    9. Spinney, Peter J & Watkins, G Campbell, 1996. "Monte Carlo simulation techniques and electric utility resource decisions," Energy Policy, Elsevier, vol. 24(2), pages 155-163, February.
    10. Chemmanur, Thomas J. & John, Kose, 1996. "Optimal Incorporation, Structure of Debt Contracts, and Limited-Recourse Project Financing," Journal of Financial Intermediation, Elsevier, vol. 5(4), pages 372-408, October.
    11. Esty, Benjamin C. & Megginson, William L., 2003. "Creditor Rights, Enforcement, and Debt Ownership Structure: Evidence from the Global Syndicated Loan Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(1), pages 37-60, March.
    12. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    13. Helyette Geman, 2005. "Commodities and Commodity Derivatives. Modeling and Pricing for Agriculturals, Metals and Energy," Post-Print halshs-00144182, HAL.
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    More about this item

    Keywords

    Project Finance; Investment Valuation; Stochastic Modeling; Monte Carlo Simulation; Forecasting; Model Complexity;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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