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Almost the same procedure is used in a MonteCarlo calculation, except that for a higher accuracy, the lifetime of the option is divided into smaller steps: increments are accumulated to simulate possible realizations starting from the initial (spot) price of the underlying (lognormal walk with shares in sect.2.1.1) and set the drift equal to the spot rate (riskneutral evolution observed for trees in sect.3.2). After each time step, the arithmetic average from all the possible terminal payoffs is used to estimate the mean price of the option on the expiring date and is discounted back in time to plot the fair value of an option having a lifetime equal to the run time. The VMARKET applet below shows an example with (Volatility=0.4, Drift=0.03), where new increments are generated every trading day (the duration of one step is =1/252=0.00397 year) and are accumulated to forecast two possible realizations of the underlying spot price ( , red dots) starting from an initial 10 EUR (coincides with the StrikePrice). After one step backward in time ( or Time=0.003 displayed on the top of the applet measures the lifetime ranging from to ), the fair value of the option is plotted (in black) together with the terminal payoff (in grey). Running the simulation for 3 months (Time=0.25), the prices obtained using the MonteCarlo method can be crosschecked with the value obtained from the binomial step (4.1.1#tab.1): they are quite different!
The experiments show that the numerical accuracy of the MonteCarlo calculation can be improved by increasing the number of realizations: the values obtained approach those given in (4.1.1#tab.1) without reproducing them exactly. The difference is particularly striking for low values of the underlying , where the binomial step gives options that are worthless, while the MonteCarlo method converges to small but finite values. As you may have guessed, also binomial trees are only an approximation of the true solution, with an accuracy that improves when the number of steps is increasedresulting in a larger number of forecasting prices. As a matter of fact, both methods converge to the same value in the limit of small time steps and a large number of realisations: this value is the same as the one that has first been obtained by Black & Scholes [3] by solving (3.4#eq.4).
Congratulations: you probably solved your first option pricing equation and hopefully even understood what you were doing! Of course, analytical minded persons may say that a formula is more general and provides a better understanding. In this syllabus, we argue the opposite: formulas, just like computers, are only tools to obtain solutions from a certain model of the reality. Analytical and computational tools are both perfectly adequate if they are used in a knowledgable manner: they are often favourably compared to ensure that the solution is not affected by different assumptions made during the derivation of the models.
Before tackling these issues, let us first develop an intuition for the financial parameters and study with experiments how they affect the option payoff before the expiry date.
SYLLABUS Previous: 4.1 Plain vanilla stock Up: 4.1 Plain vanilla stock Next: 4.1.2 Parameters illustrated with
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