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1

With[{t0 = 0, tend = 1, σ = 0.8, r = .01, S0 = 100, Κ = 110}, data = RandomFunction[GeometricBrownianMotionProcess[0, σ, S0], {t0, tend, 1/12}, 5000]; Exp[-r (tend - t0)] Mean[Max[#, 0] & /@ (data["LastValues"] - Κ)]] (* 27.5607 *) ListLinePlot[data, PlotRange -> All, PlotStyle -> Opacity[0.2]] Comparing this with Black-Scholes: d1[s_, x_, ...


0

It is a physical phenomenon occurring in a very short time( milliseconds). A = 1; \[Phi] = 0.0257275275; \[Omega] = 151.6*10^3; Cj = 17*10^(-9); Cs = 35*10^(-9); Rt = 223.9; I0 = 5*10^-6; L = 15*10^-3; Cd[t_] := Piecewise[{{Cj/Sqrt[1 - A*Cos[\[Omega]*t]/\[Phi]], t < 0}, {Cs*Exp[A*Cos[\[Omega]*t]/\[Phi]], t >= 0}}]; qn = {V'[t] == (j[t] - ...


1

After removing the superfluous θ[0] == 0 from bc, the key to the solution is a subtle change in the use of Piecewise in the definition of Cd: Cd[x_] := Piecewise[{{Cj/Sqrt[1 - x/ϕ], x < 0}}, Cs*Exp[x/ϕ]] eqn = {V'[t] == (j[t] - Id[V[t]])/Cd[V[t]], j'[t] == (V0[t] - Rt*j[t] - V[t])/L}; bc = {V[0] == A, j[0] == 0 (*, \[Theta][0] == 0*)}; pl = ...



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