毕博上海银行咨询Risk Mgmt Capabilities n231-01.doc

n231-01 a framework for market risk measurement and management for the casa de bolsa september 5, 1996 n231-01 a framework for market risk measurement and management for the casa de bolsa september 5, 1996 n231-01 agenda i.introduction ii.market risk measurement iii.general framework for quantifying credit risk iv.implementing var v.beyond var vi.linking var to risk capital vii.var software demonstration - 1 - n231-01 i. introduction introduction . . . - 2 - n231-01 why should banorte be concerned with market risk? many of its assets change in value due to fluctuations in the financial markets most evidence indicates that financial markets follow “random walks” usd-dem exchange raterandom simulation 0200400600800100012001400 1.4 1.6 1.8 2 2.2 time (days) 0200400600800100012001400 1.4 1.5 1.6 1.7 1.8 1.9 2 time (days) need to use statistical techniques to asses likely range of future values accounting based reporting, which assumes a deterministic world, gives a misleading picture of the true economic status introduction . . . - 3 - n231-01 the need for risk management lies in the inherent uncertainties of the banking business and growing external pressures . . . future earnings are uncertain, they are affected by credit write-offs trading losses fluctuations in demand regulators are beginning to require risk management bis guidelines central bank pressures stakeholders increasingly want to see effective risk management owners/shareholders bondholders employees clients . . . risk management is becoming a “core competency” of successful financial services firms introduction . . . - 4 - n231-01 the foundation for effective risk management lies in the consistent measurement of all sources of risk . . . unexpected loss residual earnings volatility business risk volatility due to operational failures, rapid changes in the competitive environment, or other events which reduce the economic value of a business value-at-risk credit riskmarket risk volatility due to variation in credit losses volatility due to changes in the market value of banortes traded assets total earnings volatility risk . . . todays focus is on market risk - 5 - n231-01 ii. market risk measurement market risk measurement . . . - 6 - n231-01 market risk is the possibility that banorte will suffer a loss as a result of unfavorable market movements example 1: fx desk purchases dm150 mm for usd100 mm current positionnew position spot rate$1 = dm1.5spot rate$1=dm1.53 position value$100 mmposition value$98 mm loss to bank$2 mm example 2: option desk buys 10,000 at-the-money call options on a stock trading at $100 current positionnew position spot rate$100strike = stock price$100 volatility30%volatility15% maturity90 daysmaturity90 days position value$66,800position value$37,800 loss to bank$29,000 dollar rises underlying volatility falls market risk measurement . . . - 7 - n231-01 there are two separate components that need to be assessed to evaluate the level of market risk first: how sensitive is the portfolio to a given market move example 1: fx position, each move of one pip in the $-dem rate results in a change in value of $15,000 dm 150,000,000*0.6667=$100,005,000 dm 150,000,000*0.6666=$ 99,990,000 15,000 example 2: option position, each 1% move in the implied volatility results in a change of value of $1933 second: how likely is any given move need to analyze both portfolio sensitivities and the probability of market moves market risk measurement . . . - 8 - n231-01 the emerging industry standard for measuring market risk is “value-at-risk” value-at-risk (var) is defined as the loss to the portfolio due to an adverse market move that is only exceeded 2.5% of the time combines portfolio sensitivity with an estimate of the likelihood of a particular move value-at-risk % price charge 2 . 5 % 0 probability market risk measurement . . . - 9 - n231-01 var has many advantages as the basic measure of market risk conceptually simple relatively easy to explain to all users applicable in a consistent fashion to all traded products relatively easy to compute allowing timely risk reports consistency with measures of other risk types credit operational reflects the true economic risk of a portfolio, including the effects of: hedging diversification - 10 - n231-01 iii. general framework for quantifying credit risk general framework for quantifying credit risk . . . - 11 - n231-01 summary the characteristics of credit risk are: losses are highly skewed the level of credit risk depends on the combination of exposure and the probability of default the measures of credit risk are expected loss (el) and unexpected loss (ul) the calculation of el and ul for single transactions requires estimates of: expected default frequency exposure severity the aggregation of el and ul depends on the diversification of the portfolio el = edf * exposure * severity general framework for quantifying credit risk . . . expected exposure - 12 - n231-01 more sophisticated derivatives require modeling to estimate exposure a swap exposure model has three key components: 102030405060 0.01 0.02 0.03 0.04 0.05 0.06 exposure (fraction of npa) time (months) mle expected exposure interest rate path generator set of interest rate paths weighted by likelihood swap valuation module list of swap prices for each scenario at each point in time, weighted by likelihood statistics module expected exposure maximum likely exposure (mle) variance of exposure as functions of time - 13 - n231-01 iv. implementing var implementing var . . . - 14 - n231-01 incorporating risk measurement tools into the risk management process requires effort in three distinct areas systems integration data identification data extraction data transfer links data mapping measurement software tools market risk calculators credit risk calculators management processes report generation & distribution limit setting and controls capital allocation performance evaluation implementing var . . . - 15 - n231-01 there are four key functions of the risk software the ability to receive and store position data from existing systems the ability to use historical market data to calculate the model parameters risk calculators for all relevant asset types the ability to store and analyze the outputs of the calculators schematic of full system graphical user interface position database client-supplied information owc- supplied information client position database link maintained by client output database risk calculators client users parameter estimation module oliver, wyman & company risk system historical price database parameter database riskmetrics or other direct estimates of model parameters implementing var . . . - 16 - n231-01 the system allows the risk to be viewed at any level in the organization and complements desk level systems us$ denominated products foreign exchange bank cetes/bondes fx ratesinflationinterest rates udi bonos money markets a/l managementtrading floor bank-level var operation-level var desk-level var product-level var risk factors - 17 - n231-01 v. beyond var beyond var . . . - 18 - n231-01 a standard var measurement gives a good picture of overall firm risk but it has several limitations normal distribution of returns most var implementations assume that returns are normally distributed according to the standard normal distribution, this is violated for most markets particularly: fx commodities non-linear price changes options and products with a high level of optionality can display price changes that are not simply proportional to the change in market factors. the risk of these products is not well captured by a var approach trends in markets there is mounting evidence that daily price changes in some markets are not independent a standard var number would over/understate the true risk beyond var . . . - 19 - n231-01 most fx markets show substantial deviations from the normal returns assumption “fat-tails” or leptokurtosis dem - usd spot exchange rate returns over a 1420-day period returns probability density -0.04-0.0200.020.04 0 10 20 30 40 50 60 70 1. apply correction factors to var to take account of the non-normality 2. use historical simulation to capture the response of the portfolio to actual market changes normal distribution actual distribution (leptokurtotic) no. of events 3.5 : 5 no. of events expected: 0.3 probability of this happening by chance: 0.000015 beyond var . . . - 20 - n231-01 the combination of non-linear response and non-normal price moves produces risks that are missed by var . . . example: value of short (written) call position 0 -10 -20 90 100 110 0.1 0.2 0.3 0.4 position value var underlying price volatility 1. include effects in the var estimate 2. stress test the portfolio 3. use a monte carlo or historical simulation to capture non-linear option risks beyond var . . . - 21 - n231-01 the combination of var, stress test and simulations provides risk management with a “risk map” of the trading organization var n$ x mm sensitivity to key factors n$ y mm n$ z mm int rates peso - us$ rate tradingtreasury banorte var n$ a mm sensitivity to key factors n$ b mm n$ c mm int rates peso - us$ rate var n$ d mm sensitivity to key factors n$ e mm n$ f mm int rates peso - us$ rate beyond var . . . - 22 - n231-01 risk management also needs to draw on accounting information to round out its view of the world p/l actual p/l volatility serves as a useful cross check on var var p/l days aged inventory indicates potential liquidity problems cross checks on trader marks introduces a “reality check” to trader supplied prices, useful in illiquid markets - 23 - n231-01 vi. linking var to risk capital linking var to risk capital . . . - 24 - n231-01 to recap we measure the level of market risk on a daily basis using value at risk . . . value-at-risk (var) is defined as the loss to the portfolio due to an adverse market move that is only exceeded 2.5% of the time value-at-risk 2.5%2. returns value at risk bounds the scale of daily losses var is calculated using daily volatilities of returns var 2 linking var to risk capital . . . - 25 - n231-01 but risk capital is set by annual default probabilities default probability risk capital losses risk capital distribution of annual p/l default probabilities can be related to credit grades ratingannual default probability (%.) a0.11 bbb0.30 bb0.81 b1.72 linking var to risk capital . . . - 26 - n231-01 we need a link between the daily picture (measured by var) and the annual view (capital attribution) the trading success of the desk is simulated over the course of the year 0 50150 250 0 4 8 12 trading day desk capital ($ mm) the influence of different management strategies can be incorporated in the model through rules that: adjust limits in response to cumulative gains or losses halt trading altogether if total losses reach preset limits if we run the model many times we can produce a distribution of possible outcomes for the desk over a year risk capital estimates linking var to ris