Abstract
In this chapter, we will introduce Monte Carlo simulation which is a problem-solving technique. This technique can approximate the probability of certain outcomes by using random variables, called simulations. Monte Carlo simulation is named after the city in Monaco. The primary attractions in this place are casinos having gambling games, like dice, roulette, and slot machines. In these games of chance, there exist random behavior.
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Boyle PP (1977) Options: a Monte Carlo approach. J Financ Econ 4(3):323–338
Boyle P, Broadie M, Glasserman P (1997) Monte Carlo methods for security pricing. J Econ Dyn Control 21(8):1267–1321
Hull JC (2015) Options, futures, and other derivatives. Prentice Hall, Upper Saddle River, NJ
Joy C, Boyle PP, Tan KS (1996) Quasi-Monte Carlo methods in numerical finance. Manage Sci 42(6):926–938
Wilmott P (2013) Paul Wilmott on quantitative finance. Wiley, Chichester
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Appendix 30.1: Excel Code—Share Price Paths
Appendix 30.1: Excel Code—Share Price Paths
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Lee, CF., Lee, J., Chang, JR., Tai, T. (2016). Simulation and Its Application. In: Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses. Springer, Cham. https://doi.org/10.1007/978-3-319-38867-0_30
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DOI: https://doi.org/10.1007/978-3-319-38867-0_30
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