Quantitative Financial Analysis Week VI Notes
Quantitative Financial Analysis Week VI Notes BU.230.710.52.SP16
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This 2 page Class Notes was uploaded by Kwan on Thursday April 21, 2016. The Class Notes belongs to BU.230.710.52.SP16 at Johns Hopkins University taught by Stuart Urban in Spring 2016. Since its upload, it has received 33 views. For similar materials see Quantitative Financial Analysis in Finance at Johns Hopkins University.
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Date Created: 04/21/16
Quant VI Thursday, April 21, 2016 13:29 1. Midterm : think-‐-‐> 10 min to alter codes A: Start with lecture: GeneratePaths/Get_call/Get_put ST: log normal (already in codes) 1) T=100/250 (100 days option in years) = Mean(Path([201 401 601 801 1001])) for loop for trajectories (from 10 to 100,000); vector price; disp 1 million (X: nonstop) 2) Expectation in MC-‐ -‐> mean I = (sT > k); No if on vectors! 3) Use get_call, _put 4) For loop: i,j, k SEM == 0: check If on a vector?& k for for loop & strike price? B: 1) Maxes(1)=… Mean(maxes) 4) .* or & (k_lower <sT) < k_upper 2. Lec: Credit Derivatives Assumption: every company defaults (1000 yr?) Default possibility as given 2008: correlated Pricing: V==0 Assumption: every company defaults (1000 yr?) Default possibility as given 2008: correlated Pricing: V==0 Different hazard rate or hazard rate Correlation -‐-‐ Gaussian copula HW4: 3 companies (different cases) Correlation Matrix = L * L (Lower triangular matrix) [1, rho Rho, 1] = [1, rho; Rho, 1] 1) Generate us 2) Generate default times (HW) 3) Get the last payment time: mutually exclusive & exhaustive (4) 4) Is default before maturity (3) 5) PV of payoff (simulated) 6) PV of payments 7) Spread = E[payoff]/E[payment] 3. Lec: VaR 99%=1%: confidence level One-‐sided NB. pos End: PPT 11
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