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

  • Weber, Florian
  • Schmid, Thomas
  • Pietz, Matthäus
  • Kaserer, Christoph

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.

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Paper provided by Center for Entrepreneurial and Financial Studies (CEFS), Technische Universität München in its series CEFS Working Paper Series with number 2010-03.

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Date of creation: 2010
Date of revision:
Handle: RePEc:zbw:cefswp:201003
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  1. 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.
  2. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  3. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  4. 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.
  5. 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(01), pages 37-60, March.
  6. 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.
  7. 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.
  8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  9. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
  10. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
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