FUZZY REAL OPTION VALUATION MODEL USING TRINOMIAL LATTICE APPROACH AND ITS PROPERTY CONSTRUCTION INVESTMENT APPLICATION

Objective- Decision makers usually use conventional methods in appraising investment projects. However, nowadays, dynamic valuation models about the future of investments also needs to be included in the decision making process. This study aims to show that a property construction investment project, which seems to be unprofitable with conventional methods currently, can be implemented profitably in the future by using a fuzzy real option method with dynamic characteristics. Using fuzzy numbers in addition to the classical fuzzy option theory will expand the model’s scope and enable it to contain more information, thereby making it more appropriate for investment environments with high uncertainty.  In addition, both the standard deviation calculated from expected value of the fuzzy numbers and the historical volatility will be used for the fuzzy real option valuation. Thus, it is aimed to compare the two methods. Finally, it is aimed to transfer expert opinions to the model as well.    Methodology- The project valuation of a property construction investment planned to be made in Turkey has been performed by using Trinomial Fuzzy Real Option method. First, the volatility variable of this model was determined on the basis of Carlsson and Fuller’s proposal of expected values and standard deviations for fuzzy numbers. Next, the historical volatility of house price index used for the volatility variable of the model. Finally, these two methods were compared. The model also includes expert opinions. These expert opinions have been transferred to the model with the aggregation of fuzzy numbers. Findings- According to the valuation conducted with Trinomial Fuzzy Real Options, the property construction investment project, which seems to be unprofitable currently, can be implemented profitably in the future. Due to the transactional nature of fuzzy numbers, volatility value, which is calculated on the basis of standard deviation of cash flows, will increase per annum. On the other hand, the historical volatility is used as a constant for all investment years. In parallel with this approach, the optimum investment year of the model using the standard deviation of cash flows as volatility has been different the model with historical volatility. Conclusion- The idea of using options in investment projects adds both managerial flexibility and uncertainty concepts to the valuation process. In addition to the term volatility, which is used for the concept of uncertainty in the model, the naturally existent uncertainty of fuzzy numbers is also used in the model. Furthermore, it is shown that the investment project, which seems to be unprofitable currently, can be carried out profitably in the future with the managerial flexibility of a delay option. While the volatility, which is calculated on the basis of the standard deviation of cash flows, postpones the optimum investment timing with its increasing value, the historical volatility model gives earlier optimum investment timing. 

___

  • Aranda, F., C., Arango, F., O., Lianos, A., I., C., 2016, Project Valuation of a Distribution Centre of an Auxiliary Rail Freight Terminal: Using Real Options with Fuzzy Logic and Binomial Trees, Journal of Applied Economic Sciences,11, 894-904.
  • Biancardi, M., Villani, G., 2017, A fuzzy approach for R&D compound option valuation, Fuzzy Sets and Systems, 310, 108-121.
  • Black, F., Scholes, M., 1973, The pricing of options and corporate liabilities, Journal of Political Economy, 81, 637-654.
  • Carlsson, C., Fuller, R., 2001, On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems, 122, 315-326.
  • Carlsson, C., Fuller, R., 2003. A fuzzy approach to real option valuation. Fuzzy Sets and Systems, 139, 297–312.
  • Clewlow, L., Strickland, C., 1998, Implementing derivatives models. Chichester: John Wiley & sons, Inc.
  • Cox, J. C., Ross, S. A. Rubinstein, M., 1979, Option pricing: a simplified approach. Journal of Financial Economics, 7, 229–263.
  • Dai, H., Sun, T., Guo, W., 2016, Brownfield Redevelopment Evaluation Based on Fuzzy Real Options, Sustainability, 8, 170.
  • Montsho, O., 2012, Real Options Valuation for South African Nuclear Waste Management Using a Fuzzy Mathematical Approach, Msc. Thesis, Rhodes University Department of Mathematics.
  • Tolga, A. C., Kahraman, C., Demircan, M. L., 2009, A Comparative Fuzzy Real Options Valuation Model using Trinomial Lattice and Black– Scholes Approaches: A Call Center Application, Journal of Multiple Valued Logic & Soft Computing, 16, 135-154.
  • Trigeorgis, L., 1993, Real options and interactions with financial flexibility. Financial Management, 22, 202–224.
  • Ucal, I., Kahraman, C., 2009, Fuzzy real options valuation for oil investments, Technological and Economic Development of Economy, 15, 4, 646-669.
  • You, C. J., Lee, C. K. M., Chen, S. L., Jiao, R. J., 2012, A real option theoretic fuzzy evaluation model for enterprise resource planning investment, Journal of Engineering and Technology Management, 29(1), 47-61.
  • Zadeh, L. A., 1965, Fuzzy sets. Information and Control, 8, 338–353.