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We will consider a stochastic variable, which follows the standard Brownian motion with drift 0.055 and diffusion 0.3.
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![PathPlot(Y(t), t = 0 .. 2, timesteps = 50, replications = 10, thickness = 3, color = red .. blue, axes = BOXED, gridlines = true, tickmarks = [10, 10])](/support/helpjp/helpview.aspx?si=8869/file05983/math34.png)
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Here ae sample paths for
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![PathPlot(exp(Y(t)), t = 0 .. 2, timesteps = 50, replications = 10, thickness = 3, color = red .. blue, axes = BOXED, gridlines = true, tickmarks = [10, 10])](/support/helpjp/helpview.aspx?si=8869/file05983/math53.png)
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You can compute the expected value of any expression involving
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Consider another stochastic process.
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So 
define the same stochastic process.
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Note that the previous value is the expected payoff of a European call option with strike price 1 maturing in 3 years. In order to compute the current option price you have to discount this expected value at the risk-free rate (which is the drift parameter of
).
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Compare this with the analytic price obtained using the Black-Scholes formula.
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Try to compute some market sensitivities of the option price.
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So
is the standard Wiener process. Using tools from the Malliavin Calculus you can show that for any payoff function
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| (1.12) |
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Here are multiple stocks.
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| (1.18) |
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This is the correlation structure.
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| (1.20) |
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| (1.21) |
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| (1.22) |
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| (1.23) |