The video extracts below are taken from the complete set of e-Learning modules found in the catalog. Optimal MRM invites you to browse the e-Learning catalog and try a complimentary demo module, to experience how we deliver a practical understanding of risk in a rich and interactive manner.
The introduction of Expected Shortfall in the setting of minimum capital for market risk, under Basel’s Fundamental Review of the Trading Book (FRTB), is a fundamental shift away from value-at-risk (VaR), to a measure of trading book loss that captures the risk of markets experiencing severe stress, and becoming illiquid as a result of such stress.
Market Risk Capital | FRTB
Basel’s revised standards for minimum capital requirements for market risk establish more objective criteria for including balance sheet positions, and introduce two methods, the Standardised Approach (SA) and the Internal Models Approach (IMA) for calculating minimum capital for market risk.
Liquidity Risk Management
Liquidity risk in a bank represents the risk of failing to meet liabilities when they come due, because of too little cash or liquid assets on hand (market liquidity), and the inability to fund balance sheet assets in a sustainable way (funding liquidity). Under Basel 3, the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) represent market liquidity risk and funding liquidity risk, respectively.
Under Basel’s capital adequacy requirements (CAR), banks are required to hold a minimum amount of capital relative to risk exposure on their assets. Basel tier 1 capital (CET1) includes common equity and retained earnings. Under basel 3, banks are required to maintain a minimum CET1 ratio of 6% of their Risk-Weighted Assets (RWA).
Stressed VaR was introduced under Basel 2.5, after banks had experienced losses as a result of the 2007 global financial crisis (GFC). The losses that banks had experienced during this period far exceeded the minimum capital that Basel required banks to hold against traded market risk, based on a simple Value-at-Risk (VaR) measure.
Credit Valuation Adjustment
Prior to the introduction of Fair Value Accounting (FVA) rules, the general practice had been to value derivative positions without consideration for the risk of counterparty credit default. The introduction of FVA required banks to adjust their derivative Mark-to-Market (MtM) exposure by the risk of counterparty credit default. This is referred to as Credit Valuation Adjustment or CVA.
Return Risk Optimization
In an optimization exercise, allocations across different asset classes are randomly generated. Each of these allocation sets represents an investment portfolio with an expected return and risk or volatility measure. The optimization exercise generates a cloud of portfolio return vs risk coordinates. Portfolios with the highest return to risk ratio represent the efficient frontier.
Shareholders, investors, and other stakeholders have historically been lulled into a false sense of confidence in risk measures such as Value at Risk (VaR) at 99% confidence. VaR is silent on the possibility of loss arising out of market changes that are bigger than 2.33 standard deviations in a given day. During periods of financial stress, markets can change in a given day well above 2.33 standard deviations. Stress testing provides an estimate of the risk of loss from such changes.
Value at Risk
VaR is one of the most commonly recognized measures of trading portfolio risk in financial organizations. It’s a probabilistic measure of the risk of loss on a portfolio of assets and liabilities over a given time horizon, typically 1-day, and is expressed as the maximum amount of loss that can be expected, at a given confidence level, usually 99%.
Risk sensitivities measure the change in Mark-to-Market or fair value of assets and liabilities, relative to changes in underlying risk factors such as interest rates, credit spreads, equity, commodity, and foreign exchanges prices. The simplest and most commonly used risk sensitivities are known by the Greek symbols delta, gamma, vega, and theta.
Volatility and Correlation
Volatility is represented by the greek symbol sigma (σ). It measures the distribution of changes in value of individual risk factors such as rates, stock prices, commodity prices, and other risk factors. The standard deviation equation is a practical way to measure the historical volatility of any risk factor. Correlation is represented by the greek symbol Rho (ρ). It measures the degree to which changes in different risk factors move in the same or opposite direction.
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