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Monte Carlo simulation of present value of cash flows

Abstract

This paper is concerned with financial application of Monte Carlo simulation of infinite series. In fact, present value of a cash flow is represented as infinite series. Hence, the Monte Carlo may be applied to approximate the present value. It is seen that the Monte Carlo method is sample approximation of the mean of cash flow computed at a stopping time. The stopping time, first, is considered as an up-crossing time point of sequence of uniform random variables and then it is replaced by up-crossing of macro-economic variables (called as factor) which brings the dependency structure to the problem which is modeled by auto-regressive to any things (ARTA) models. Here, two different Monte Carlo simulation methods are presented. A financial application is also given. Continuous time extensions of results are also proposed. Finally, a concluding remark section is given.

2020 Mathematics Subject Classification:

60E05

Keywords

ARTA, cash flow, cause factor, infinite series, Monte Carlo, present value, stopping time

Full text

Author Details

Reza Habibi

Iran Banking Institute
Central Bank of Iran
Tehran, Iran
e-mail: rezahabibi2681@yahoo.com


References

  1. T. Björk. Arbitrage Theory in Continuous Time, 3rd edt. Oxford Finance Series. Oxford, Oxford University Press, 2009.
  2. S. A. Broverman. Mathematics of investment and credit. ACTEX Academic Series. Winsted, CT, ACTEX Publications, Inc., 2010.
  3. M. C. Cario, B. L. Nelson. Autoregressive to anything: time-series input processes for simulation. Oper. Res. Lett. 19, 2 (1996), 51–58.
  4. D. Conniffe, J. E. Spencer. Approximating the distribution of the maximum partial sum of normal deviates. J. Statist. Plann. Inference 88, 1 (2000), 19–27.
  5. R. H. Myers, D. C. Montgomery. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley-Interscience and Sons, 2002.
  6. P. J. Nahin. Digital dice. Computational solutions to practical probability problems. Princeton Puzzlers. Princeton, NJ, Princeton University Press, 2013.
  7. R. Y. Rubinstein, D. P. Kroese. Simulation and the Monte Carlo Method, 2nd edt. Wiley-Inretscience, 2007.
  8. F. Solomon. Monte Carlo simulation of infinite series. Math. Mag. 64, 3 (1991), 188–196.