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Technical Specialist - Market and Counterparty Credit Risk Policy

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1 Technical Specialist - Market and Counterparty Credit Risk Policy
The Market risk framework and the Fundamental Review of the Trading Book Michele Marzano Technical Specialist - Market and Counterparty Credit Risk Policy Bank of England The views expressed in this presentation are my own and do not necessarily reflect the views of the Bank of England.

2 Section 1: overview of the current market Risk Framework
What is market Risk Current Market Risk Framework Response the crisis: Basel 2.5 Section 2: overview of the Fundamental Review of the trading book. Background: the problems with the current framework New trading book boundary New internal models New standardised approach Recap

3 Risks Types of Risk Market Risk is the risk that the value of your position will change due to changes in market risk factors. Credit/Issuer risk is the risk of loss due to: borrower default on any type of debt by failing to make payments which it is obligated to do applies to bonds, loans, off-balance sheet exposures Counterparty risk is the risk that the counterparty to a transaction (e.g. derivatives) could default before the final settlement of the transaction's cash flows. Credit risk assessment: Assessing “creditworthiness” Ratings systems: Rating agencies Bank’s own models

4 Risks – cont’d Types of Risk Liquidity risk can be:
Market liquidity is the risk that a firm cannot sell an asset (bonds, loans, shares, derivatives, currency) because nobody wants to buy it at its assumed value, within the time required Funding liquidity is the risk that liabilities cannot be met when they fall due Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. Security Reconciliation Operations MI Operational risk assessment: No generally agreed metrics Data gathering (i.e. classifying events according to type and severity) Reduction achieved by controls over movement of data, manual operations and adequate reporting

5 Market Risk The five standard market risk factors include:
Equity risk = risk that stock prices will change. Interest rate risk = risk that interest rates will change. Currency risk = risk that foreign exchange rates will change. Commodity risk = risk that commodity prices (i.e. grains, metals, etc.) will change. Credit spread risk = risk that bond rates will change relative to interest rates. Market risk measurement: Nominal position Market value Sensitivity Stress and scenario testing Value at Risk

6 Market Risk – cont’d A single risk-factor is the smallest unit for which risk is calculated. Risk-types are defined as the collection of all risk-factors (single risk-factors or term-structures) across different currencies, indices. Interest rate risk measures Cash-flows and time value of money Yield to maturity, duration and convexity PV01, PVBP, DV01, BPV Credit risk measures Par CDS spread, z-spread, YtM spread Credit Duration and CS01 or CR01 Option risk measures Delta Gamma Vega Rho Theta

7 Value at Risk Value at Risk is an aggregated risk measure used to estimate the risk of a trading portfolio. VaR expresses many different types of risk as a “common currency”. VaR models predict the worst case loss expected over the holding period within the probability set out by the confidence interval. Specifically, the loss one would expect to exceed over a specific holding period to a certain level of confidence: 1 day 99% VaR of £1m tells you that you would expect to lose £1m or more on one day in every hundred or 2-3 times a year. Typically firms use 1 day 95% or 1 day 99%. We make firms use a 10 day holding period one-tailed for their PRR calculation. The firm can use the square root of time method. Fat tail.

8 VaR Methodology Main methods of calculating VaR: Historical simulation
Most frequently used by firms Re-run the market moves that occurred on each of previous N days Calculate p&l on today’s positions for each day’s movements VaR is 5th (say) worst outcome Variance-Covariance Popular with some smaller firms with linear risk portfolios Estimate NxN variance-covariance matrix (N underlying market variables) Assume market variables are distributed multivariately normally Assume p&l responds linearly to market variables Monte Carlo Use is growing, but very IT intensive for complex products Work out covariance matrix and assume normality (or any other distribution) Generate several sets of random correlated market movements from assumed distribution Calculate p&l for each set of movements

9 VaR Methodology Historical Simulation Simple concept
Run portfolio over last N days (e.g. N=500) to calculate distribution of losses that would have occurred on the actual portfolio Assume the past resembles the future

10 VaR - HistSim Historical Simulation Advantages
Avoids making statistical assumptions Captures outliers or tail events Actual correlations and volatilities Gives a “worst case” scenario (built in stress test) Handles non-linear instruments, but dependent on implementation (in practice simplifications/approximations very common) Easy to understand Disadvantages Huge amount of market data required May not respond rapidly to changes in volatility Computationally intensive Statistical assumptions are implicit and can give false comfort (e.g. assumption of constant vol, constant correlation) No explanation of relationship between risk factors “Ghost” effects Credit default data

11 VaR – Montecarlo Sim Monte Carlo simulation
Market data Monte Carlo simulation Like HS, simulation approach Like VCV, uses multivariate (or lognormal) model of underlying market moves Series of market moves randomly generated (“draws”) P&l effects then calculated in similar way to HS Simulate market movements Based on historical data Gives richer simulation of “possible outcomes” Apply to current portfolio Assume the past resembles the future Estimate variance-covariance matrix. Use matrix and random number generator to generate random market moves. Use simulated market moves to calculate random p&l effects on present portfolio. Portfolio data Use simulated distribution of portfolio values to compute VaR.

12 General vs Specific Harder to get a SR recognition:
SR models have generally performed worse in volatile conditions Most excessive backtesting exceptions issues relate to SR models. The market risk and specific risk VaR can be aggregated assuming zero correlation (independent validation): General (GR) Specific (SR) Equity (indices) (name specific) Interest Rate (rates) (credit) FX (all FX risk) Commodities (all commodity risk)

13 Approval Process - cont’d
The Board & senior management must be involved Market Risk management should be independent of the trading unit and report directly to senior management VaR model validation Independent from development team and documented Prudent Valuation of positions Valuation adjustments for illiquidity, concentrated positions, model risks, operational risks, administration risk, close out risk need to be considered IT systems System development and change control Internal audit / Annual independent review Accuracy, completeness, consistency

14 Risks not in VaR (RNIV) Systematically identify and measure risks not captured or not captured adequately by the VaR model and hold a self-determined quantity of capital against material exposures. These risks can be significant P&L drivers, and hence require capitalisation: during and after the 2007/8 crisis VaR models performed badly (many backtesting exceptions). VaR type RNIVs = P&L Impact (Percentile Move) Stressed RNIVs = P&L Impact (Stress Scenario Move) Risks not in pricing models: Libor tenor basis Risks not in VaR Engine: Skew, Convexity, Vol of Vol, Cross gamma, Mean reversion, Seasonality Inadequate data (liquidity, lack of history): Correlation, Dividend, Proxy Beyond 99%-tile or 10-day liquidity horizon: FX depeg/repeg risk, Gap risk.

15 Backtesting Compare 1-day VaR forecast with actual p&l outcomes to try to assess model accuracy For one-tailed 99%, on average, 2.5 exceptions every 250 days. If the number of exceptions is high, VaR model could be not good enough and the firm will be subject to capital charge penalty (plus factor). Perform overall and at sub-portfolio level General and Specific risks portfolio Analyse reasons for exceptions Three months of back testing data is needed before model recognition is granted. Inaccurate Model – VaR too small Inaccurate Model – VaR too large

16 Basel 2.5 New capital component for Market risk Stressed VaR (SVaR)
All firms with CAD 2 (VaR)-model Less procyclical Selection of 12 months period during which inputs (relevant market risk factors) experience significant stress Relevance to the firm’s portfolio Review requirement: changes in positioning in trading portfolios need to trigger a review of stressed period selection Incremental Risk Charge (IRC) All firms with specific interest rate risk (~credit spread volatility) in VaR model Default and migration risks Comprehensive Risk Measure (CRM) Firms with correlation trading portfolios (CDOs and basket credit derivatives), alternative to standard rules.

17 Basel 2.5 MARKET RISK INTEREST RATE RISK EQUITY RISK FX RISK COMMODITY RISK Standardised General market risk Specific risk Standardised charges for sec and Re-sec. Internal models General Market risk VaR Stressed VaR Specific Risk IDRC IRC CRM

18 FRTB - Background The financial crisis exposed weaknesses in the market risk framework (which hadn’t materially changed since the market risk amendment in 1996). In 2009 the Basel Committee introduced a set of revisions to the current market risk framework to address the most material issues – Basel 2.5. The fundamental review of the trading book was initiated to deal with all of the identified weaknesses in a coherent manner. The first CP for the fundamental review of the trading book was published in May 2012. A second CP with detailed draft Accord text was published in October 2013, taking into account comments received on CP1.

19 Background The new market risk framework aims to address the weaknesses of the pre-crisis framework: The permeable / subjective nature of the trading book boundary. A range of problems with the internal model approach: Poor capture of varying liquidity of traded positions; Pro-cyclical calibration that did not focus on stressed losses; Lack of capture of tail-risk; No capture of default and migration risk. Lack of risk sensitivity of the standardised approach, meaning a lack of credible threat of removal of model permission.

20 New trading book boundary
Current boundary is largely based on trading intent – difficult to evidence / police in practice. New boundary maintains a link with trading intent, but aims to be less susceptible to arbitrage by providing more guidance and more information for supervisors: Presumptive lists of instruments that are in the trading book (e.g. positions designated as held for trading for accounting purposes, instruments resulting from market making activities); Limit on switching instruments after initial designation; Explicit supervisory authority to re-designate positions; Reporting requirements to support supervision.

21 New internal models (1)

22 New internal models (2) More granular approval process – approval at desk level with P&L attribution and backtesting requirements. Expected shortfall rather than VaR, calibrated to stressed conditions and with varying liquidity horizons. Criteria to determine which risk factors at each trading desk are modellable or non-modellable – separate approach to capitalise each type. Separate default risk model to capture default risk in the trading book (with greater specification on calibration). All securitisations excluded from the internal model approach.

23 ES (modellable risks) + IDR + Non-modellable risks
New internal models (3) Total capital requirement for eligible desks = ES (modellable risks) + IDR + Non-modellable risks

24 New standardised approach
New standardised approach aims to achieve a balance of risk sensitivity and simplicity. Within each risk class capital requirements calculated based on sensitivities with prescribed risk factor shocks and correlations across risk factors. No diversification across risk classes (equity risk, commodity risk, foreign exchange (FX), general interest rate risk (GIRR), credit spread risk (CSR), default risk). Non-delta risk of options capitalised under the scenario matrix approach. Aim is to have an approach that is a credible fall-back to internal models.

25 Recap The new framework is designed to address the following:
The permeable / subjective nature of the trading book boundary. A range of problems with the internal model approach: Poor capture of varying liquidity of traded positions; Pro-cyclical calibration that did not focus on stressed losses; Lack of capture of tail-risk; No capture of default and migration risk. Lack of risk sensitivity of the standardised approach, meaning a lack of credible threat of removal of model permission.

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