σμimplied vol · KKmax(S−K,0)udβρᵢⱼfat tailsPnL · drawdownyield · TdSₜ = μSₜ dt + σSₜ dWₜC = S·N(d₁) − K·e⁻ʳᵀ·N(d₂)σₚ² = wᵀΣwd₁ = [ln(S/K) + (r + σ²⁄2)T] ⁄ σ√T∂V/∂t + ½σ²S²·∂²V/∂S² + rS·∂V/∂S − rV = 0Sharpe = (Rₚ − R_f) ⁄ σₚVaR₉₉ = μ − 2.33σρ = Cov(X,Y) ⁄ σₓσᵧE[Rₚ] = Σ wᵢ·E[Rᵢ]φ(x) = e^(−x²/2) ⁄ √2πE[Rᵢ] = R_f + βᵢ(E[Rₘ] − R_f)dXₜ = θ(μ − Xₜ)dt + σ dWₜσ²ₜ = ω + α·ε²ₜ₋₁ + β·σ²ₜ₋₁C − P = S − K·e⁻ʳᵀΔ = ∂V/∂S Γ = ∂²V/∂S²p = (e^(rΔt) − d) ⁄ (u − d)rₜ = ln(Pₜ ⁄ Pₜ₋₁)E[Xₜ₊₁ | ℱₜ] = Xₜf* = (bp − q) ⁄ bz = (x − μ) ⁄ σP(N=k) = λᵏe^(−λ) ⁄ k!Θ = −∂V/∂Tν = S·φ(d₁)·√Tmin wᵀΣw s.t. wᵀμ = μ*∫Σ∂∞λ
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σμimplied vol · KKmax(S−K,0)udβρᵢⱼfat tailsPnL · drawdownyield · TdSₜ = μSₜ dt + σSₜ dWₜC = S·N(d₁) − K·e⁻ʳᵀ·N(d₂)σₚ² = wᵀΣwd₁ = [ln(S/K) + (r + σ²⁄2)T] ⁄ σ√T∂V/∂t + ½σ²S²·∂²V/∂S² + rS·∂V/∂S − rV = 0Sharpe = (Rₚ − R_f) ⁄ σₚVaR₉₉ = μ − 2.33σρ = Cov(X,Y) ⁄ σₓσᵧE[Rₚ] = Σ wᵢ·E[Rᵢ]φ(x) = e^(−x²/2) ⁄ √2πE[Rᵢ] = R_f + βᵢ(E[Rₘ] − R_f)dXₜ = θ(μ − Xₜ)dt + σ dWₜσ²ₜ = ω + α·ε²ₜ₋₁ + β·σ²ₜ₋₁C − P = S − K·e⁻ʳᵀΔ = ∂V/∂S Γ = ∂²V/∂S²p = (e^(rΔt) − d) ⁄ (u − d)rₜ = ln(Pₜ ⁄ Pₜ₋₁)E[Xₜ₊₁ | ℱₜ] = Xₜf* = (bp − q) ⁄ bz = (x − μ) ⁄ σP(N=k) = λᵏe^(−λ) ⁄ k!Θ = −∂V/∂Tν = S·φ(d₁)·√Tmin wᵀΣw s.t. wᵀμ = μ*∫Σ∂∞λ
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Stochastic Calculus skill›Module 3 · Pricing and measures›Topic 3.2·~1 hr 5 minAdvanced

Feynman-Kac and the Black-Scholes PDE

Skill positionModule 3 of 5 · Topic 2 of 2
3.2 Feynman-Kac and the Black-Scholes PDE
—Lab score— / 100Total XP—Streak—

Two ways to the same price

There are two ways to price an option. One is to average the payoff over random paths, as in the last topic. The other is to solve a partial differential equation (PDE), an equation that ties how the price changes with time to how it changes with the stock price. The Feynman-Kac theorem says the two are the same: the average of a payoff over random paths is the solution of a PDE. For a stock with dS=rS dt+σS dWdS=rS\,dt+\sigma S\,dWdS=rSdt+σSdW under QQQ, the option value V(t,S)V(t,S)V(t,S) solves the Black-Scholes PDE, with the payoff ggg as the end condition.

∂V∂t+12σ2S2∂2V∂S2+rS∂V∂S−rV=0,V(T,S)=g(S)\frac{\partial V}{\partial t}+\frac12\sigma^{2}S^{2}\frac{\partial^{2}V}{\partial S^{2}}+rS\frac{\partial V}{\partial S}-rV=0,\qquad V(T,S)=g(S)∂t∂V​+21​σ2S2∂S2∂2V​+rS∂S∂V​−rV=0,V(T,S)=g(S)(1)

Read the PDE like a sentence. ∂V/∂t\partial V/\partial t∂V/∂t is time decay. The term with ∂2V/∂S2\partial^{2}V/\partial S^{2}∂2V/∂S2 is the reward for curvature: randomness adds value to an option, and this term exists because (dW)2=dt(dW)^{2}=dt(dW)2=dt. The term with ∂V/∂S\partial V/\partial S∂V/∂S is the drift of the stock and −rV-rV−rV is discounting. For a European call the solution is the Black-Scholes formula below, where NNN is the standard normal distribution function. A put follows from put-call parity.

C=S N(d1)−Ke−rTN(d2),d1=ln⁡(S/K)+(r+σ2/2)TσT,d2=d1−σT,C−P=S−Ke−rTC=S\,N(d_1)-Ke^{-rT}N(d_2),\quad d_1=\frac{\ln(S/K)+(r+\sigma^{2}/2)T}{\sigma\sqrt T},\quad d_2=d_1-\sigma\sqrt T,\qquad C-P=S-Ke^{-rT}C=SN(d1​)−Ke−rTN(d2​),d1​=σT​ln(S/K)+(r+σ2/2)T​,d2​=d1​−σT​,C−P=S−Ke−rT(2)
An at-the-money callTake S=K=100S=K=100S=K=100, r=5%r=5\%r=5%, σ=20%\sigma=20\%σ=20% and T=1T=1T=1. Then d1=(0+0.07)/0.2=0.35d_1=(0+0.07)/0.2=0.35d1​=(0+0.07)/0.2=0.35 and d2=0.15d_2=0.15d2​=0.15, with N(d1)=0.6368N(d_1)=0.6368N(d1​)=0.6368 and N(d2)=0.5596N(d_2)=0.5596N(d2​)=0.5596. The price is C=100×0.6368−95.12×0.5596≈10.45C=100\times0.6368-95.12\times0.5596\approx10.45C=100×0.6368−95.12×0.5596≈10.45. The number N(d1)=0.6368N(d_1)=0.6368N(d1​)=0.6368 is also the call's delta, the shares you hold to hedge it.
  • Feynman-Kac: the average payoff over random paths solves a PDE, and the PDE solves for that average.
  • Black-Scholes PDE terms: time decay, reward for curvature, drift and discounting.
  • Put-call parity: C−P=S−Ke−rTC-P=S-Ke^{-rT}C−P=S−Ke−rT, so one price gives the other.

Lab · what's graded

  • Computing d115 pts
  • Pricing the call15 pts
  • Put-call parity15 pts
  • Testing a function in the PDE15 pts
  • Which functions solve the PDE15 pts
  • Read the PDE10 pts
  • The Feynman-Kac idea15 pts
0/ 7 answered
Tasks
1Numeric answer15 ptsNot answered

For S=K=100S=K=100S=K=100, r=5%r=5\%r=5%, σ=20%\sigma=20\%σ=20% and T=1T=1T=1, what is d1=ln⁡(S/K)+(r+σ2/2)TσTd_1=\dfrac{\ln(S/K)+(r+\sigma^{2}/2)T}{\sigma\sqrt T}d1​=σT​ln(S/K)+(r+σ2/2)T​?

d=d =d=accepted: ± 0.005
2Numeric answer15 ptsNot answered

Use N(d1)=0.6368N(d_1)=0.6368N(d1​)=0.6368 and N(d2)=0.5596N(d_2)=0.5596N(d2​)=0.5596 with S=K=100S=K=100S=K=100, r=5%r=5\%r=5% and T=1T=1T=1. What is C=S N(d1)−Ke−rTN(d2)C=S\,N(d_1)-Ke^{-rT}N(d_2)C=SN(d1​)−Ke−rTN(d2​)? Give two decimals.

c=c =c=accepted: ± 0.03
3Numeric answer15 ptsNot answered

A call is worth C=12C=12C=12 with S=K=100S=K=100S=K=100, r=5%r=5\%r=5% and T=1T=1T=1. Use C−P=S−Ke−rTC-P=S-Ke^{-rT}C−P=S−Ke−rT to find the put value PPP. Give two decimals.

p=p =p=accepted: ± 0.02
4Numeric answer15 ptsNot answered

Put V=S2V=S^{2}V=S2 into the left side of the PDE, ∂tV+12σ2S2∂SSV+rS ∂SV−rV\partial_t V+\tfrac12\sigma^{2}S^{2}\partial_{SS}V+rS\,\partial_S V-rV∂t​V+21​σ2S2∂SS​V+rS∂S​V−rV. What number do you get at S=10S=10S=10, σ=0.2\sigma=0.2σ=0.2 and r=0.05r=0.05r=0.05? (It is not 000, so S2S^{2}S2 is not a price.)

v=v =v=accepted: ± 0.01
5Choice15 ptsNot answered

Which of these are prices that satisfy the Black-Scholes PDE? Pick all that apply.

6Match10 ptsNot answered

Match each term of the PDE with its meaning.

Pick the partner of each item on the left.

∂V/∂t\partial V/\partial t∂V/∂t
12σ2S2 ∂2V/∂S2\tfrac12\sigma^{2}S^{2}\,\partial^{2}V/\partial S^{2}21​σ2S2∂2V/∂S2
rS ∂V/∂SrS\,\partial V/\partial SrS∂V/∂S
−rV-rV−rV
7Explain in words15 ptsNot answered

In one or two sentences, explain why pricing by averaging over random paths and pricing with the Black-Scholes PDE give the same number.

Math modeKeyboard or LaTeX inputTask 1 of 7 · Ctrl ⏎ to check

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