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2318Jump-Risk Trading Intuition 8Why do positive and negative jumps change the volatility smile in different ways even if jump variance is the same?数理金融困难essay未尝试面试订阅2319Jump-Risk Trading Intuition 9Why can a jump-risk model still be useful even if it does not fit every strike perfectly?数理金融困难essay未尝试面试订阅2320Jump-Risk Trading Intuition 10Why does the smile effect of jumps often decay with maturity more differently than the smile effect of plain stochastic volatility?数理金融困难essay未尝试面试订阅2333Collateral and Netting Recovery 3After threshold collateralization, a desk observes residual exposure 1.1 on a gross expected exposure of 4.2. Assuming residual = max(EE - threshold, 0) and EE exceeds threshold, what threshold is implied?数理金融简单数值题未尝试免费2334Collateral and Netting Recovery 4A desk approximates residual exposure after threshold and initial margin as max(EE - threshold - IM, 0). If EE = 5, threshold = 1.2, and IM = 0.9, what residual exposure remains?数理金融简单数值题未尝试免费2343Bucketed Exposure Interpretation 3A desk defines effective expected exposure as the running maximum of EE over time buckets. If the EE profile is 1, 0.9, 1.3, 1.1, what is the final effective EE at the last bucket?数理金融中等数值题未尝试面试订阅2344Bucketed Exposure Interpretation 4A simple expected positive exposure proxy averages bucket EE values equally. If bucket EE values are 0.8, 1, 0.7, 1.3, what EPE results?数理金融中等数值题未尝试面试订阅2345Bucketed Exposure Interpretation 5A two-bucket CVA approximation uses total CVA = sum i LGD*DF i*EE i*PD i with LGD = 0.6. Bucket 1 has (DF,EE,PD)=(0.96,1.2,PD 1), bucket 2 has (0.92,1.5,0.012), and total CVA is 0.01332. What PD 1 is implied?数理金融简单数值题未尝试面试订阅2346WWR Scenario Inference 1A two-state WWR scenario table prices CVA as LGD * [pi calm*EE calm*PD calm + pi stress*EE stress*PD stress]. If LGD = 0.6, state weights are 65.00% and 35.00%, exposures are 4 and 10, calm-state PD is 0.01, and total CVA is 0.129675, what stress-state PD is implied?数理金融中等数值题未尝试面试订阅2347WWR Scenario Inference 2A WWR stress table uses CVA = LGD * [pi calm*EE calm*PD calm + pi stress*EE stress*PD stress]. If LGD = 0.55, weights are 70.00% and 30.00%, EE calm = 2, PD calm = 0.015, PD stress = 0.12, and total CVA = 0.1926, what EE stress is implied?数理金融中等数值题未尝试面试订阅2348WWR Scenario Inference 3A two-state WWR approximation uses CVA = LGD * [(1-pi stress)*EE calm*PD calm + pi stress*EE stress*PD stress]. If LGD = 0.6, EE calm = 1.5, EE stress = 7.5, PD calm = 0.012, PD stress = 0.1, and CVA = 0.19656, what stress-state probability pi stress is implied?数理金融中等数值题未尝试面试订阅2349WWR Scenario Inference 4A two-state WWR table gives weighted exposure-default term 80.00%*3*0.02 + 20.00%*9*0.11. If total CVA is 0.2214, what LGD is implied?数理金融中等数值题未尝试面试订阅2350WWR Scenario Inference 5A desk compares a two-state WWR CVA calculation against an independence shortcut. Under WWR, CVA = LGD * sum s pi s*EE s*PD s. Under independence, it uses LGD * E[EE]*E[PD]. If state weights are 75.00% and 25.00%, exposures are 2 and 12, PDs are 0.01 and 0.08, and LGD = 0.6, what uplift ratio WWR CVA / Indep CVA results?数理金融中等数值题未尝试面试订阅2351Wrong-Way-Risk Judgment 1Why does positive exposure-default correlation mechanically raise CVA in a scenario-table view?数理金融中等essay未尝试面试订阅2352Wrong-Way-Risk Judgment 2Why is assuming independence often a dangerous shortcut in counterparty risk?数理金融中等essay未尝试面试订阅2353Wrong-Way-Risk Judgment 3Why is wrong-way risk often more severe for option-like exposures than for nearly linear exposures?数理金融中等essay未尝试面试订阅2354Wrong-Way-Risk Judgment 4Why are wrong-way risk and credit-copula modeling related but not the same problem?数理金融中等essay未尝试面试订阅2355Wrong-Way-Risk Judgment 5Why does wrong-way risk often show up first in stress testing before it shows up in daily pricing formulas?数理金融中等essay未尝试面试订阅2356Wrong-Way-Risk Judgment 6Why can collateral reduce but not fully cure wrong-way risk?数理金融困难essay未尝试面试订阅2357Wrong-Way-Risk Judgment 7Why can diversifying counterparties reduce wrong-way risk even when each single trade still has the same market exposure logic?数理金融困难essay未尝试面试订阅