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.
• 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
2
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
•
3
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
•
4
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
5
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
6
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
0.45
99%
Value at
day 99%.
0.4
Risk
0.35
We make firms use a 10 day holding
0.3
period one-tailed for their PRR
0.25
calculation.
0.2
0.15
The firm can use the square root of
0.1
time method.
0.05
Probability Density
•
0
•
Fat tail.
 Losses
Profits 
7
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
8
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
Daily price changes
Daily simulated income - sorted
4
3
3
2
Income (£)
Price change %
4
1
0
-1
-2
2
1
0
-1
-3
-2
Time
-3
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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
10
VaR – Montecarlo Sim
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 variancecovariance matrix.
Market
data
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.
11
General vs Specific
Equity
Interest Rate
FX
Commodities
General (GR)
Specific (SR)
(indices)
(name specific)
(rates)
(credit)
(all FX risk)
(all commodity risk)
• 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):
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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
13
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.
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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
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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.
Basel 2.5
MARKET RISK
INTEREST RATE RISK
EQUITY RISK
FX RISK
Standardised
General
market risk
Specific risk
Standardised
charges for sec
and Re-sec.
COMMODITY RISK
Internal models
General
Market risk
VaR
Stressed VaR
Specific Risk
IDRC
IRC
VaR
Stressed VaR
CRM
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.
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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.
19
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.
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New internal models (1)
21
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.
22
New internal models (3)
Total capital requirement for eligible desks =
ES (modellable risks) + IDR + Non-modellable risks
23
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.
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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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