Community Q&A
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What are the trade reporting requirements for OTC derivatives under Dodd-Frank and EMIR, and what role do swap data repositories play?
Trade reporting mandates require all OTC derivative transactions to be reported to registered repositories. Under Dodd-Frank, reporting is one-sided based on a hierarchy, while EMIR requires dual-sided reporting by both counterparties within T+1.
How does central clearing of OTC derivatives work, and what role does a CCP play in reducing counterparty risk?
Central clearing interposes a CCP between the original buyer and seller of an OTC derivative. The CCP manages risk through initial margin, variation margin, and a structured default waterfall. While clearing reduces bilateral counterparty risk, it concentrates systemic risk in the CCP.
What is 'significant risk transfer' in securitization and why does it matter for capital relief?
Significant Risk Transfer is the regulatory test determining whether a bank securitization achieves capital relief. The bank must demonstrate that genuine risk has been transferred to investors through quantitative loss-sharing tests and qualitative checks for implicit support.
How do heating degree days and cooling degree days work in weather derivatives?
Weather derivatives allow businesses to hedge revenue exposure to temperature fluctuations. Heating Degree Days (HDD) measure cold relative to a 65°F baseline, while Cooling Degree Days (CDD) measure heat above it. Contracts typically cover a cumulative period.
Why is backtesting expected shortfall (ES) so much harder than backtesting VaR?
The shift from VaR to expected shortfall (ES) in the FRTB introduced a significant practical challenge: ES is much harder to backtest than VaR. This tension is a key topic in FRM Part II.
How does Black's model for options on futures differ from the standard Black-Scholes model?
Black's model (1976) is essentially Black-Scholes adapted for options where the underlying is a futures contract rather than a spot asset. The key simplification is that the futures price already incorporates the cost of carry.
What are the financial stability implications of Central Bank Digital Currencies (CBDCs), and what risks should banks prepare for?
Central Bank Digital Currencies introduce significant financial stability risks, primarily through deposit disintermediation — households shifting funds from commercial bank deposits to risk-free central bank digital money. This raises funding costs, amplifies digital bank run risk, and alters monetary policy transmission.
How does ISDA SIMM calculate initial margin for non-cleared derivatives, and what are the key risk buckets?
ISDA SIMM is the industry-standard sensitivity-based method for calculating initial margin on bilateral non-cleared OTC derivatives. It computes delta, vega, and curvature sensitivities across six risk classes, applies prescribed risk weights, and aggregates using a correlation-based framework.
How do AIC and BIC work for comparing risk models, and when would they give different recommendations?
AIC and BIC both balance goodness-of-fit against model complexity, but BIC penalizes additional parameters more heavily, especially for large samples. AIC tends to prefer slightly more complex models while BIC favors parsimony.
What is asset-backed commercial paper (ABCP) and what liquidity risks do ABCP conduits face?
Asset-backed commercial paper (ABCP) is short-term debt typically maturing in 30-90 days, issued by a special purpose vehicle called a conduit and backed by longer-term financial assets. The fundamental risk lies in the maturity transformation between short-term funding and long-term assets.
How does Extreme Value Theory (EVT) improve tail risk estimation, and what is the Peaks-over-Threshold approach?
EVT models only the extreme tail of the return distribution using the Generalized Pareto Distribution. The Peaks-over-Threshold method fits exceedances above a high threshold, providing theoretically justified tail risk estimates far more accurate than Normal or Student-t.
What determines loss given default (LGD), and how do workout LGD and market LGD differ?
LGD depends on seniority, collateral, industry, economic conditions, and jurisdiction. Workout LGD tracks actual recovery cash flows over years, while market LGD uses post-default trading prices for quick estimation.
What is volatility clustering and how do you test for ARCH effects in financial returns?
Volatility clustering means large price moves tend to follow large moves. The Engle LM test detects ARCH effects by regressing squared residuals on their lags — a significant test statistic means volatility is time-varying.
How do cross-currency swaps actually work, and why is the notional exchanged unlike regular interest rate swaps?
Cross-currency swaps are fundamentally different from plain IRS because they involve two different currencies, so netting the notional makes no sense. At inception, parties exchange notionals at the spot rate and re-exchange them at maturity at the original rate.
How is factor-based VaR used in stress testing and scenario analysis?
Factor-based stress testing applies extreme scenario shocks to portfolio factor sensitivities, producing estimated P&L impacts. Historical scenarios replay actual crises, while hypothetical scenarios design specific shocks. Multi-factor consistency is critical — factor correlations typically spike during stress.
What is wrong-way risk and how do you measure it?
Wrong-way risk occurs when exposure to a counterparty increases simultaneously with their default probability. Specific WWR involves a direct causal link; general WWR arises from broad economic factors. It is measured through exposure-default correlation, alpha multipliers, or conditional exposure analysis.
What is cointegration and how is it used in pairs trading?
Cointegration means two non-stationary series have a stationary linear combination — they share a long-term equilibrium. Unlike correlation (short-term co-movement), cointegration implies the spread is mean-reverting, enabling pairs trading strategies.
How are credit-linked notes (CLNs) structured and who benefits from them?
A credit-linked note (CLN) is a funded credit derivative where the investor buys a bond with an embedded CDS. The investor receives enhanced coupons but absorbs credit losses if the reference entity defaults. CLNs reduce counterparty risk compared to unfunded CDS.
How are recovery rates modeled in practice and what factors affect LGD estimation?
Recovery rate modeling is crucial because Loss Given Default directly scales expected and unexpected credit losses. Key factors include seniority of the claim, industry sector, economic conditions at default, and the legal framework of the jurisdiction.
What is model calibration in risk management and how do you avoid overfitting?
Model calibration adjusts parameters to match current market data, while estimation fits to historical data. Overfitting occurs when the model memorizes noise rather than capturing real patterns, leading to poor out-of-sample performance. Key defenses include parsimony, cross-validation, and economic constraints.
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