Unit 6 - Tail Risk, Systemic Risk and FRTB

• Limitations of the normal distribution as the basis for probabilistic modeling
• Quantifying the exposure and severity of “outliers” and tail risk
• Systemic Risk indicators
• Money market indicators indicating credit/liquidity stresses – LIBOR/OIS spread, TED spread
• “haircuts” in repo markets, CDS rates for banks, sovereign CDS rates
• Foreign exchange indicators – cross-currency basis swap spreads, abnormal FX carry trade behavior, EM FX volatility
• Market stress indicators – VIX, bid/ask spread, market microstructure measures
• Swap spreads – LIBOR/OIS, EURIBOR/EONIA
• Examination of network theory and contagion risk

• Contrast Basel III treatment of market risk with the FRTB
• New methodology for the Standardized Approach (SA) and Internal Models Approach (IMA)
• Expected Shortfall (ES) replaces Value at Risk (VAR)- designed to better capture tail risk
• Liquidity horizons – rationale and calculation methods
• P&L Attribution Test
• Concerns about FRTB implementation
• IMA validation testing
• Full revaluations and short cuts e.g. Taylor series for option pricing
• Focuses on sensitivities of risk positions in the trading book
• Example of calculating delta risk charge for simple FX portfolio
• Vega Risk Charge, Curvature Risk Charge, Default Risk Charge

• Basic concepts of stress testing – base case versus adverse (worst case) scenarios
• Explanation of how adverse scenarios may be constructed and simulated
• Distinguishing between macro/global factors and local/micro factors
• Macro factors – establishing associations with broad macro-economic variables
• Associating probabilities to risk factors – quantitative and qualitative approaches
• Mapping qualitative and descriptive data to numerical values
• Model-based simulations of adverse case scenarios
• Data deficiencies and estimation of outlier scenarios
• Just how extreme can adverse scenarios become?

Stress testing within the European banking system – the Asset Quality Review programme

• Simulations – randomized market scenarios expressing risk factors
• Modeling methods – contingency scenarios
• Monte Carlo simulations – how to conduct them
• Explanation of Principal Components Analysis
• Sizes of historical samples – are they sufficiently large to include a wide variety of conditions?