ENTERPRISE RISK MANAGEMENTAn Evaluation of Interest Rate Risk Tools and theFuture of Asset Liability ManagementAbstractThis paper identifies, describes, and evaluates the analytical tools that CEOs and corporate boardshave used to understand capital and earnings exposure to interest rate risk. Furthermore, thepaper examines lessons learned as a result of the market turbulence that began in late 2007 andexplores how the nature of financial risk management is evolving. Most banks actively monitorstructural asset and liability risk; however, unlike market or credit risk, there are no standardmetrics to assess asset and liability risk. In fact, practitioners do not even agree on whetherinterest rate risk metrics should be based on earnings sensitivity, market value sensitivity, orthe traditional cash flow gap model. Given this lack of consensus and standardization, it isnot surprising that there is a wide range of Asset Liability Management (ALM) practices andsophistication. Moreover, while no ALM risk management tool is ideal, each has its strengthsand weaknesses. To effectively manage their balance sheet, financial institutions must selectthe risk management tools that are appropriate for the institution’s size, complexity andrisk management objectives in the context of a dynamic regulatory environment. The globalregulatory community has indicated that ALM was not adequate and is forcing banks to improvetheir risk management practices.MODELINGMETHODOLOGYAuthorRobert J. Wyle, CFASenior Director - Product ManagementRob joined Moody’s Analytics from KPMGwhere he was the Director of the NationalALM Practice. Prior to joining KPMG, Robwas Director of Market Risk Management atE*TRADE Financial where he was responsiblefor the daily quantification of interest rate risk.Prior to that, Rob was the ALM ProductManager for SunGard Trading and RiskSystems. Other professional risk managementroles include the Dime Savings Bank and theFederal Home Loan Bank of New York.Contact UsAmericasfirstname.lastname@example.orgEuropeemail@example.comAsia (Excluding Japan)+firstname.lastname@example.orgJapanemail@example.com
2 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSCONTENTS1. INTRODUCTION................................................................................................................................................32. TRADITIONAL INTEREST RATE RISK MANAGEMENT TOOLS.....................................................................52.1 The Periodic Gap Model...................................................................................................................................52.2 Net Interest Income (NII) Simulation and Earnings at Risk (EaR).........................................................72.3 Market Value of Portfolio Equity..................................................................................................................112.4 Value-at-Risk (VaR)..........................................................................................................................................16VAR CASE STUDY ................................................................................................................................................183. ADVANCES IN ALM RISK QUANTIFICATION...............................................................................................194. CONCLUSION................................................................................................................................................. 22FURTHER READING............................................................................................................................................ 23ABOUT MOODY’S ANALYTICS ALM SOLUTION........................................................................................... 23APPENDIX A – VAR RISK FACTOR DECOMPOSITION.................................................................................... 24APPENDIX B – VAR INTEREST RATE RISK DECOMPOSITION....................................................................... 25REFERENCES......................................................................................................................................................... 26
3 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS1. IntroductionALM is defined in several ways. Traditionally, ALM has been associated with the management of structuralbalance sheet interest rate risk (IRR). The traditional definition of IRR is “the exposure of a bank’s financialcondition to adverse movements in interest rates”1. This definition, however, considers only a single riskfactor. Consistent with this definition, the tools for measuring and monitoring IRR have historically beenthe Repricing Gap Model, Net Interest Income (NII) Simulation, and the Sensitivity of Market Value ofPortfolio Equity. At some banks, due to the concentration of skills and cash flow models, the ALM functionis also responsible for performing a variety of other balance sheet management analyses including:»» Liquidity Risk»» Funds Transfer Pricing (FTP)»» Capital Management»» Risk Policy SettingTherefore, depending on the institution, the full scope of ALM can be much broader than just IRR.And given the lack of standardization in the industry, it is not surprising that there is a wide range ofsophistication in ALM.Approaches to ALM can be broadly categorized as simple or sophisticated:Simpler Approaches to ALM»» Periodic Gap Model»» Calculating the impact of parallel and instantaneous interest rate shocks on static earnings or marketvalue using discounted cash flow analysis.Sophisticated Approaches to ALM»» Dynamic simulation of the balance sheet under multiple interest rate scenarios»» Option Adjusted Valuation (OAV)Volatility-based risk metrics, that include Value at Risk (VaR), stochastic Earnings at Risk (EaR), RiskAdjusted Return on Capital (RAROC), or Economic Value Added (EVA)While regulators and practitioners might agree on what ALM risk management tools are available, theydo not necessarily agree on which ALM tools should be used to quantify risk (that is, an earnings-basedsensitivity or market value sensitivity.) Regulators are demanding stronger risk management practices andso banks are increasingly looking at more sophisticated approaches to ALM. This lack of consensus on howto measure ALM risk was a major reason why interest rate risk outside the trading book was not subjectedto an explicit (Pillar 1) capital charge under Basel II but is covered under Pillar 2 instead (BCBS 2005,§762).Lessons LearnedThe industry and regulatory response to the market dislocations that began in late 2007 have sparkedrenewed interest in ALM. Traditional approaches to ALM did not foresee or prevent the credit crisis. Some of the key lessons learned include2:»» The need for effective firm-wide risk identification and analysis: Firms that were better able toshare quantitative and qualitative information across the enterprise were better able to identify thesources for inherent risks sooner. These firms identified risks earlier which gave them more time todevelop firm-wide solutions rather than wait and hope that the lines would make decisions that benefitthe firm’s exposures collectively. Firms that performed less well did not effectively share information,and the lines were left to make decisions in isolation.1 Principles for the Management and Supervision of Interest Rate Risk; Bank of International Settlements; July 20042 Senior Supervisors Group; Observations on Risk Management Practices during the Recent Market Turbulence; March 6, 2008
4 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS»» The consistent application of independent and rigorous valuation practices across the firm:Firms that performed better throughout the credit crisis generally had a more disciplined enterprisevaluation process. These firms developed in-house infrastructure and governance capability to quantifyand share the intrinsic value of complex potentially illiquid securities. These firms identified future riskssooner and had more time to frame enterprise risk management strategies.»» The effective management of funding, liquidity, capital, and the balance sheet: Firms thatimplemented and enforced enterprise risk management control systems for the management ofcapital and liquidity tended to perform better during the credit crisis. For example, firms that alignedtheir Treasury functions more closely with risk management processes provided internal incentives forindividual business lines to control activities that might lead to unexpected losses primarily by chargingthe business lines for contingent liquidity risk.»» Informative and responsive risk measurement and management reporting and practices:Better performing firms tended to look at risk using metrics that were based on different underlyingassumptions and were better able to update their modeling assumptions to better reflect currentmarket conditions. These firms were better able to evaluate forward-looking scenarios under changingmarket circumstances and pursue opportunities as they emerged. In contrast, firms that experiencedmore difficulty were dependent on specific risk measures which might have been based onoutdated information.Emerging Trends in ALMThe acknowledgment of industry weaknesses and an atmosphere of strong regulatory reform has signaledthe incentive for change, post 2008 banking crisis.»» Governance: Enterprise risk management governance practices are gaining ground driven both bylessons learned by financial institutions during the credit crisis and as a result of regulatory pressure.»» Liquidity Risk: The measurement, management, and supervision of liquidity risk is now considered tobe as important as capital management.3»» Funds Transfer Pricing: Funds transfer pricing is enjoying a renaissance — particularly with regards toincorporating liquidity costs in product pricing, performance measurement, and new product approval.Prior to the credit crisis, many banks treated liquidity like a free good for transfer pricing purposes. Thisbehavior was reportedly one of the causes for the poor liquidity outcomes during the credit crisis.4»» Data Infrastructure: The inevitability of Basel III compliance is forcing banks to invest in datainfrastructure. The Basel III framework hinges on integrated asset, capital, and funding management.Basel III liquidity data requirements span multiple functional and organizational silos necessitating theimplementation of the enterprise risk management Datamart.»» ALM: Moving Out of the Back Office: ALM is evolving from a back-office risk management costcenter to an integrated front office balance sheet management function. The convergence of marketand credit risk has accelerated post crisis partly due to regulatory pressure. “Firm’s that avoidedsignificant losses appear to have a better ability to integrate exposures across businesses for bothmarket and counterparty risk management.”5To remain competitive, banks must keep up with the latest developments in risk measurement andmanagement. Ultimately, firms that tie risk exposures to capital more effectively will be better able tointegrate risk-taking decisions into their strategic and tactical decision making.63 “The Turner Review: A regulatory response to the global banking crisis”; March 20094 “Liquidity Transfer Pricing: a guide to better practice”; Bank for International Settlements; December 20115 Observations on Risk Management Practices During the Recent Market Turbulence; Senior Supervisors Group; March 20086 Governor Susan Schmidt Bies; Board of Governors of the Federal Reserve; March 29th, 2006
5 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS2. Traditional Interest Rate Risk Management ToolsChanges in interest rates can have adverse effects both on a bank’s earnings and its economic value whichhas given rise to three separate, but complimentary, perspectives for assessing a bank’s structural balancesheet interest rate risk exposure: the Periodic Gap Model, Market Value of Portfolio Equity (MVPE), andNet Interest Income Simulation.72.1 The Periodic Gap ModelGap analysis is a useful asset/liability management tool to manage interest rate risk, make funding decisions,and allocate capital along the yield curve. Interest rate gap models were first produced in the 1950’s whenmain frame computers became more affordable. These models were further refined and used widely throughthe late 1970’s and are still used to this day across Asia, Europe, and at small institutions globally.Gap is simply the post-hedge difference between rate sensitive assets and rate sensitive liabilities,bucketed into the sooner of reprice, maturity, or expected call date. Floating rate instruments areusually bucketed according to their next reprice date only one time per reporting date. Such treatmentis consistent with the cash flow treatment of maturing assets or liabilities and because gap modelsimplicitly assume that each interest rate reset date are treated separately and in sequence. However, thegap treatment of floating rate instruments can be broken down into rate-sensitive and rate-insensitivecomponents in some regions of the world.When periodic gap is zero, net interest income in that period is hedged against changes in interest rates.However, when there is a positive gap, earnings decline as interest rates decrease and rise as interestrates increase. Figure 1 overleaf, shows a typical periodic gap report.8All of the cash flows converge togenerate two risk metrics: periodic gap and cumulative gap. These gap patterns are interpreted in order tounderstand interest rate risk exposures.The periodic gap is the difference between rate-sensitive assets and rate-sensitive liabilities net of hedges.It provides an indication of the direction of earnings relative to changes in interest rates. When viewedover time, periodic gap shows whether or not there are asset/liability mismatches. Lumpy periodicgap patterns indicate that earnings will be volatile. The literature advocating the periodic gap modelrecommends that net interest income be hedged by setting each periodic gap equal to zero. Therefore, inthe course of routine balance sheet management, if funds need to be invested, sold, or borrowed, thengap patterns provide a guide as to what maturities should be purchased, sold, or borrowed in order tosmooth earnings or take bets on the direction of interest rates.Cumulative gap is less useful from a tactical interest rate risk management perspective because anycumulative gap arises as the result of many preceding periodic gaps. Despite the convenience of a singleindex number for interest rate risk, cumulative gap is at best ambiguous. However, from a strategicperspective, cumulative gap provides an indication for how the bank’s capital is invested across the yieldcurve. Thus, given the short-comings of this tool, both tactical and strategic risk management decisionscan be made.7 “Principles for the Management and Supervision of Interest Rates”; Basel Committee on Banking Supervision; Page 6; July 20048 Interest Rate Risk, Comptroller’s Handbook; Comptroller of the Currency (OCC); June 1997
6 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSFigure 1: A Typical Gap Report 1 Mo. 1-3 Mos. 3-6 Mos. 6-12Mos.1-2 Yrs. 2-3 Yrs. 3 Yrs. TotalLoans 100 10 20 45 5 20 30 230Investments 5 5 10 20 20 50 110Other Assets 5 15 20Total Assets 105 15 25 55 25 40 95 360NonmaturityDeposits-65 -30 -50 -145CDs and OtherLiabilities-35 -35 -45 -30 -10 -10 -20 -185Total Liabilities -100 -35 -45 -30 -40 -10 -70 -330Equity -30NetPeriodic Gap5 -20 -20 25 -15 30 25 0Cumulative Gap 5 -15 -35 -10 -25 5 30 0Strengths of the Periodic Gap Model»» The development costs for implementing periodic gap models are small.»» In many instances, gap models are produced in spreadsheets. However, for detailed gap reporting,complex simulation software is needed.»» Periodic gap models are easy and intuitive to understand. (Senior managers who are less familiar withadvanced financial risk management concepts often request Gap models, which are often includedas tables in the annual report or 10-K.) Risk managers can easily set risk-limit policies based on therelative size and sequence of gap patterns.Weaknesses of the Periodic Gap Model»» Sizeable cash flow mismatches can be hidden in gap buckets as short as one month. For example,periodic gap might be positive indicating that earnings should rise when rates go up. However, ifthe majority of the liability cash flows occur at the beginning of the month while the asset cashflows occur towards the end of the month, then income might actually fall when rates go up. Theobvious solution would be to produce gap reports with daily time buckets using powerful cash flowsimulation software. However, even with chunky gap buckets (i.e., daily, monthly, quarterly, semi-annual, and annual) gap reports are difficult to interpret. Therefore, a gap report with numerous dailytime segments might be difficult if not impossible to interpret.»» Gap does not provide a single index number for risk exposure. Therefore, different risk professionalscan have different interpretations of the same gap report.»» There is a fixation on plugging Gaps to zero which restricts ALM risk management choices becausemany different non-zero Gap combinations immunize net interest income as well. A risk managerthat plugs all gap buckets to zero will unnecessarily incur hedging costs that are too high.»» Gap models cannot adequately measure risk associated with options sensitive to interest rate risk.For example, difficulties arise when non-maturing deposit redemptions or prepayments are sensitiveto the spread between contractual and market rates.»» Gap mistakenly assumes that interest rates change by the same amount for all assets and liabilities.As a result, gap does not sufficiently quantify basis risk.
7 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS»» Gap cannot measure or manage more than one risk factor simultaneously. That is, gap models cannotquantify risk exposures for market value capitalization, financial leverage, or total return on equitysimultaneously like other ALM risk metrics. In addition, shareholders care more about a bank’s stockprice. Therefore, their preference would be to position the interest rate risk of their equity based ontheir outlook of risk and return. Since gap models tend to focus on earnings more than risk exposuresof capital, these preferences are not addressed.»» Interest cash flows are typically not included in gap reports and are therefore ignored.»» Cash flows of non-interest income and non-interest expense are ignored.Conclusion: The Periodic Gap Model can be UsefulGap can be a useful tool for balance sheet managers, senior managers, and boards of directors dependingon the circumstances of use. For example, Gap is more useful for balance sheets that have few embeddedoptions. Second, Gap provides some means of risk control to banks that have meager financial orinformation technology resources.Third, Gap models are intuitive and easily understood by senior managersand boards of directors with little financial expertise. However, Gap models have many disadvantages.Therefore, a risk management program that benefits from the positive qualities of gap might be strongerwhen supplemented with other risk metrics based on alternative underlying assumptions.2.2 Net Interest Income (NII) Simulation and Earnings at Risk (EaR)From an earnings perspective, the focus of analysis is the impact of changes of interest rates on accrualor reported earnings, usually net interest income. This is the traditional approach to interest rate riskassessment taken by many banks. Variation in earnings is an important focal point for interest rate riskanalysis because reduced earnings or outright losses can threaten the financial stability of an institutionby undermining its capital adequacy and by reducing market confidence.9The simulation model starts with the current balance sheet, including detailed maturity or repricingschedules and the associated rates and yields of those balances, and forecasts the income statement,balance sheet, and cash flow schedules for a series of future time periods (typically 12 - 36 months andeven as long seven years). This is accomplished by simulating the repricings, maturities, rollovers, andnew business originations for all balance sheet activities of the bank. To generate a plausible set offinancial statements, you must make a number of assumptions about important issues, including targetbalances, maturity schedules for new business, yield curve behavior, non-yield curve rate assumptions,and pricing assumptions for new business.9 Principles for the Management and Supervision of Interest Rates; Basel Committee on Banking Supervision; July 2004.
9 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSStrengths of NII»» Net interest Income simulation models produce their results in a dynamic or forward-looking mannerwhereas market value and gap models produce their results statically. Since simulation models areboth forward looking and interactive, they require greater emphasis on assumptions and managerialbehavior. Therefore, simulations are unique in that they help managers anticipate future events,perform assumption-sensitivity analysis and provide a means by which managers can test the effectof different external shocks and strategies on income.»» Unlike the periodic gap model, simulation model results can be unambiguously interpreted. In theexample in Figure 2, it is clear that a parallel and instantaneous 100 basis point increase in interestrates will reduce net interest income by 5%.Weaknesses of NII»» The software, hardware, and personnel requirements necessary to run an ALM model are costly. ALMsoftware models are strategic balance sheet simulation tools for banks. By definition, they requiresignificant setup and maintenance requiring highly specialized and trained personnel and are capableof performing very large quantities of calculations within one compute.»» Simulations depend on assumptions and data analyses that place strenuous demands on theoperator. For example, for every reporting date, the model operator must parameterize the modelwith assumptions for new volumes, new volume contractual characteristics, new volume pricingassumptions, prepayment assumptions, and so on. Some of these assumptions are provided bybusiness units or are extrapolated from empirical data. In addition, assumptions become lessmeaningful as a function of time. Therefore, the simulation results are only as good as the analystoperating the model.»» Since simulation solves problems by trial and error, a thorough examination of current risks can beclumsy, time consuming, and labor intensive. For example, to produce interest rate risk results thatreflect a targeted financial leverage, the operator might reproduce and adjust the results many timesbefore achieving the desired level.»» Simulation models produce large amounts of output that requires great insight and skill to analyze,interpret, and summarize.»» ALM models can be black boxes. The internal structure and calculations of models might not reflectthe bank or institution being modeled. In addition, econometric models and relationships mightbecome invalid with time.»» NII simulation models can be “gamed” depending on the scenarios chosen for quantifying risk and theforecast horizon. For example, if the user leverages interest rate ramp scenarios instead of shocks, theycan time new volumes of say interest rate swaps in order to reduce income volatility. However, such ALMstrategies may reveal no risk where risk is present. For example, let’s assume a bank uses an ALM riskmanagement strategy that short funds originations of fixed rate assets. Assume that the bank originatesa ten year asset and funds it with 50% five year liabilities and 50% 3 month and 1 month funding. Tohedge risk the bank swaps both the asset and the fixed rate liability into floating. If the forecast horizon isless than five years, it is difficult to detect the funding liquidity risk and counter-party credit risk.Stochastic Earnings at Risk (EaR)EaR quantifies the maximum earnings decline over a forecast time horizon, within a given level ofconfidence. The idea behind EaR is similar to Value-at-Risk (VaR) in that they are both volatility-basedmetrics. The major difference is that EaR evaluates earnings sensitivity dynamically over a forecasthorizon whereas VaR evaluates risk at a point in time. While the technology is known and readilyavailable, EaR is rare among industry practitioners representing perhaps only 10% of ALM industry users.Most stochastic analysis is limited to VaR based methodologies or Monte Carlo valuation also known asoption adjusted valuation (OAV).
10 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSEaR uses a stochastic interest rate term structure model to generate probable interest rate paths tocompute an earnings distribution. Stochastic generation of interest rates is considered to be a morerobust and flexible alternative to the deterministic approach. By simulating many interest rate paths,you can estimate earnings or market value losses over a specified time period and confidence level bycapturing balance sheet growth or yield curve movements.You can use stochastic EaR for a variety of analyses. For example, beyond generating an earningsdistribution based solely on probable interest rate paths, you can construct a rich, multi-factor analysisby quantifying earnings sensitivity that incorporates non-interest rate risk factors like spread risk, index-specific volatility, or even credit risk. Finally, stochastic EaR is also useful in framing hedge strategies thatminimize portfolio risk using non-duration based risk metrics. This approach is more robust because oneof the limitations of duration is that it assumes parallel movements in the term structure of interest rates.Strengths of Stochastic EaR»» Considers multiple risk factors (in addition to interest rate).»» Shows aggregate and component risk factors (that is, in addition to interest rate risk) facilitating riskmanagement and improved hedging decisions.»» Permits risk managers to express the potential earnings decline within a given probability rather thanjust the relative earnings decline.»» Relies on items other than the deterministic base case scenario for NII.Weaknesses of Stochastic EaR»» Stochastic income modeling is complex and heavily reliant on user-defined assumptions and data,typically resulting in increased operations, compliance, and oversight demands.»» Stochastic income modeling and the related assumptions require constant updating andback-testing.»» Stochastic income modeling can be excessively short-term focused and might not capture embeddedoptions risk. The growing complexity of many products and investments heightens the need toconsider interest rate risk from a long-term perspective (that is, fair value). For example, the impactof common options – such as periodic and lifetime interest rate caps on adjustable-rate mortgages,the prepayment option on fixed-rate mortgages, or embedded calls in funding structures – might notbe discernable if the impact of interest rate changes is evaluated over only the short term.»» Evaluating interest rate risk solely from exposure to income streams might be insufficient for bankswith large positions that reprice or mature in the intermediate-term or long term. That is the horizonfor a balance sheet mismatch might be beyond the forecast horizon. Therefore, if a balance sheetmismatch is beyond the forecast horizon, this risk management metric can be gamed.»» Some securities offer relatively high initial coupon rates to investors at the expense of potentiallylower-than-market rates of return beyond the 2-year term point. Failure to consider the valueof potential total cash flows under a range of interest rate paths might leave the bank with aninstrument that underperforms the market or provides a rate of return below the bank’s fundingcosts. Therefore, the fact that NII does not consider the entire life of the balance sheet can lead to ashort sided understanding of structural balance sheet interest rate risk.Conclusion: NII Simulation and Stochastic EaRA risk management framework that only considers a short-term risk perspective or does not adequatelyevaluate embedded options can lead a bank to make naïve risk management decisions. Therefore, thereare clear benefits to using EaR. However, less than 10% of banks use stochastic earnings analysis. Themost common reason for focusing on deterministic analysis is that most management teams do notperceive a favorable cost/benefit relationship. That is, some individuals do not fully understand andknow how to interpret more advanced metrics.
11 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS2.3 Market Value of Portfolio EquityThe sensitivity of the market value of portfolio equity (MVPE) is a useful risk management tool formeasuring and managing interest rate risk. MVPE is defined as the market value of assets minus themarket value of liabilities net of derivatives. The balance sheet is marked-to-market under multipleinterest rate scenarios; typically +/- 300 bps parallel and instantaneous interest rate shocks in 100-bpsincrements. The table in Figure 3 is an example of an MVPE analysis for a medium-sized balance sheetthat has significant embedded options both on the asset side of the balance sheet and the liability sideof the balance sheet.Figure 3: Duration of Equity. A long-term measure of risk in a single index-300 Bps -200 Bps -100 Bps Base Rates +100 Bps +200 Bps +300 BpsAssetsTotal Invest-ments MBS 4,210,438 4,086,497 3,962,398 3,828,600 3,686,892 3,542,992 3,400,794Total Loans 18,930,851 18,700,001 18,449,300 18,143,494 17,761,872 17,307,224 16,830,622Total InterestEarning Assets 23,141,288 22,786,498 22,411,698 21,972,094 21,448,764 20,850,216 20,231,416ORE, OtherAssets 2,116,185 2,355,803 2,554,619 2,673,208 2,733,383 2,760,875 2,760,091Off BalanceSheet Positions (273,704) (227,759) (136,195) 12,233 201,353 419,808 644,944Total Assets 24,983,769 24,914,542 24,830,122 24,657,535 24,383,499 24,030,899 23,636,452Liabilities andStockholders’EquityTotal Deposits 13,649,749 13,398,552 13,189,325 13,037,661 12,921,253 12,815,022 12,710,552Total BorrowedFunds 9,142,738 9,108,658 9,075,009 9,042,338 9,011,119 8,981,450 8,953,361Total InterestBearing Liabili-ties 22,792,487 22,507,210 22,264,334 22,080,000 21,932,372 21,796,472 21,663,913Other Liabilities 485,158 485,158 485,158 485,158 485,158 485,158 485,158Off BalanceSheet Positions 39,881 26,313 13,235 1,215 (9,067) (17,899) (25,923)Total Liabilities 23,317,526 23,018,681 22,762,728 22,566,372 22,408,463 22,263,731 22,123,148StockholdersEquity 1,666,243 1,895,861 2,067,394 2,091,163 1,975,036 1,767,168 1,513,304Total Liabilities StockholdersEquity 24,983,769 24,914,542 24,830,122 24,657,535 24,383,499 24,030,899 23,636,452MVPE Changefrom Base (424,920) (195,302) (23,769) (116,127) (323,995) (577,860)% Change -20.3% -9.3% -1.1% -5.6% -15.5% -27.6%Duration ofEquity (10.6) (4.7) 2.2 8.2 13.1
12 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSKey Facts about Market Value of Portfolio Equity:»» There is generally an inverse relationship between interest rates and changes in market value.»» When duration of equity is negative, there is a positive relationship between interest rates andchanges in market value.»» The changes in market value are not linear nor symmetrical.There are various reasons why the market value profile for the price/yield relationship behaves as itdoes as described in the previous section and illustrated in Figure 4; most notably, the impact of marketvolatility on embedded options. The market value results can be summarized in a number of ways thatprovide useful insights into the risk profile of the balance sheet. Some examples include duration,convexity, and VaR.Figure 4: Market Value of EquityDuration of EquityDuration is a measure for the market value sensitivity of an asset or liability. In this context, durationdoes not simply mean average life but refers to the change in market value for a parallel change inrates. For example, the concept of duration suggests that an asset with duration of 6 will suffer a 6%price decline for every 100 bps increase in rates. Thus, duration is the first derivative of the price yieldrelationship or more formally ∂Price/∂Yield.Just as the concept of duration can refer to the price sensitivity of an individual financial instrument ora portfolio of fixed income instruments, it can also be applied to the net market value of equity for abank’s balance sheet.There are various forms of duration; common ones include:»» Macauley duration»» Modified duration»» Effective duration (most common)Effective duration is more widely used for risk management practices because it incorporates the impactof market volatility on embedded options whereas the other methods do not. Effective duration can thenbe calculated from the base market value calculation and one or more parallel interest rate scenarios:The interpretation of the percent change in MV to a change in rates using effective duration is –D * ∆i.Figure 4Figure 86.66%6.94% 6.97%6.39%5.04%GapAnalysisBetaweightedGap; CrudeSimulationCategory-levelsimulation; Staticcash ﬂow marketvalueProbabilisticEvaluation; Moredetailed simulation;Integration of systmes4.0%4.5%5.0%5.5%6.0%6.5%7.0%7.5%8.0%-300 Bps +200Bps +300Bps+100BpsBase Rates-100Bps-200Bpslate 1960’sIndicators1970’sIndicatorsEstimates1980’sIndicatorsEstimates Measures1990s onwardsAdvanceMeasuresMarket Value of Equity
13 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS»» Note: The price yield relationship for an instrument with embedded options is not linear and mightnot be symmetrical. This is due to the cash flow characteristics of embedded options otherwiseknown as behavior or correlated risk. Behavior refers to circumstances where contractual terms mightbe varied by custom or implication due to:»» The short prepayment option for loans»» The redemption option of non-maturing deposits»» Call and put options»» Caps and FloorsThese risks are said to be correlated risks because they are dependent upon movements of someunderlying variable (that is, interest rates). Therefore, the price/yield relationship of a fixed mortgagewould have a negatively convex profile shown in Figure 5.Duration of EquityFigure 5: The First Derivative of the Price Yield Relationship-300 Bps -200 Bps -100 Bps BaseRates+100 Bps +200 Bps +300 BpsAssetsTotal Investments MBS 3.0 3.3 3.6 3.9 4.0Total Loans 1.3 1.5 1.9 2.4 2.7Total Interest Earning Assets 1.6 1.8 2.2 2.6 2.9ORE, Other Assets (9.3) (6.2) (3.3) (1.6) (0.5)Off Balance Sheet Positions (0.5) (0.9) (1.3) (1.6) (1.7)Total Assets 0.3 0.5 0.9 1.3 1.6LiabilitiesTotal Deposits 1.7 1.4 1.0 0.9 0.8Total Borrowed Funds 0.4 0.4 0.4 0.3 0.3Total InterestBearing Liabilities 1.2 1.0 0.8 0.6 0.6Other Liabilities - - - - -Off Balance Sheet Positions 1.1 1.0 0.9 0.8 0.7Total Liabilities 1.2 1.0 0.8 0.7 0.6Duration (10.6) (4.7) 2.2 8.2 13.1Duration of EquityThe net duration of the market value of equity is a single index for risk across the entire life of thebalance sheet. The greater the magnitude of DOE (in a positive or a negative direction), the greater theinterest rate risk exposure for MVPE. The asset/liability mismatch scaled by the institutions leveragedetermines the duration of a financial institution’s MVPE. The impact of the change in the market valueof assets and liabilities due to changes in interest rates has entirely the opposite impact on MVPE.Therefore, the notation for DOE can be summarized as:
14 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSWhere,DOE = Duration of equityMVA= Market value of assetsMVL= Market value of liabilitiesDA= Duration of assetsDL= Duration of liabilitiesTable 3 is the corresponding MVPE profile for Table5 above. Duration of equity is 2.2 years:In addition, DOE can be restated to illustrate the impact of market value capitalization on the risksensitivity of the balance sheet:For example, if market value capitalization is cut in half, then risk doubles. Conversely, if market valuecapitalization is doubled, then risk declines by 50%.MV Duration MV Duration MV DurationAssets 95 2.0 95 2.0 95 2.0Other Assets 5 0.0 0 0.0 15 0.0100 1.9 95 2.0 110 1.7Liabilities 90 1.5 90 1.5 90 1.5Equity 10 5.50 5 11.00 20 2.75Strengths of Duration of Equity»» Shows magnitude and timing of cash flows»» Provides a single exposure number to manage against»» Identifies which transactions drive exposures (risks off-set because duration is additive based onmarket value, also known as the Taylor series expansion)»» Easily accommodates unusual security types and correlated interest rate risks(that is, effective duration)»» Quantifies more than one target account at a time (that is, economic leverage and price risk)
15 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSWeakness of Duration of Equity»» Assumes all interest rates change in equal amounts»» Only effective for small rate changes (convexity)»» Does not capture future business volumes, managerial decisions, and so on.ConvexityThe convexity of the price/yield relationship for MVPE is not linear. Convexity is clearly visible in thepreceding graph and must be evaluated in order to have a prospective understanding of the price yieldrelationship. There are many interpretations of the concept of convexity. Mathematically, convexity isthe second order derivative of the price/yield relationship (∂Duration/∂Yield). That is, it is the changein price not explained by duration or, said another way, the rate at which duration changes for a givenchange in market rates. The table in Figure 6 contains the corresponding convexity measurements forthe preceding two tables.Convexity of EquityFigure 6: The second derivative of the price yield relationship-300 Bps -200 Bps -100 Bps BaseRates+100 Bps +200 Bps +300 BpsAssetsTotal Investments MBS (0.00) (0.24) (0.21) (0.06) 0.05Total Loans (0.11) (0.30) (0.42) (0.41) (0.13)Total Interest EarningAssets(0.09) (0.29) (0.38) (0.35) (0.10)ORE, Other Assets (1.73) (3.14) (2.19) (1.20) (1.02)Off Balance Sheet Positions - - - - -Total Assets (0.06) (0.36) (0.41) (0.32) (0.17)LiabilitiesTotal Deposits 0.31 0.44 0.27 0.08 0.01Total Borrowed Funds 0.00 0.01 0.02 0.02 0.02Total Interest BearingLiabilities0.19 0.26 0.17 0.05 0.02Other Liabilities - - - - -Off Balance SheetPositions- - - - -Total Liabilities 0.19 0.26 0.17 0.06 0.02Duration (3.06) (7.15) (6.69) (4.65) (2.60)Note: The duration measurements for some line items are positive while others are negative. Negativeconvexity is characteristic of the price yield behavior of short prepayment options or in the money calloptions. A low positive convexity is characteristic of optionless bonds or commercial real estate loanswith yield maintenance agreements. A higher degree of positive convexity can be associated with theshort redemption option for NMDs depending on the corresponding deposit products and relevantbehavior factors.
16 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSConclusion: Keeping Market Value Sensitivity Risk Measures in PerspectiveMeasures of market value sensitivity for the net equity of a balance sheet are not universally acceptedaround the globe. Consequently, some regulators and financial institutions favor the sensitivity ofearnings as their primary risk metric and do not consider economic value. However, it is important tobear in mind that both give different perspectives of risk because they are based on different underlyingassumptions and, while fundamentally different, they are interrelated. First, earnings sensitivity onlyprovides perspective into risk over the forecast horizon whereas market value is a long-term riskmeasure incorporating all cash flows of the balance sheet through maturity. Therefore, having boththe long-term perspective and the short-term perspective adds value because it strengthens a bank’srisk management framework making it harder to game. For example, what often happens is that banksenter into risk management strategies that maximize short-term earnings without realizing that theyare cannibalizing their net equity into the current earnings stream. The effect is that market value ofequity will decline over time. Second, earnings volatility and market value sensitivity are interrelated.Consistent with fixed income mathematics, if you hedge earnings then market value will becomevolatile. Conversely, if market value sensitivity is perfectly hedged, then earnings will become volatile.Therefore, beyond the individual strengths and weaknesses of both measures of earnings and marketvalue sensitivity, it is important to understand both.2.4 Value-at-Risk (VaR)Regulatory requirements have necessitated holding capital to support trading activities since the 1990’s.The requirement to quantify market risk for the trading portfolios using VaR was specified in the BaselMarket Risk Amendment. Value-at-Risk is a statistical estimate of the maximum potential loss of afinancial transaction or inventory of transactions over a specified time horizon for a given confidencelevel, based upon an adverse move in market factors. It provides a common measure of risk acrossdiverse financial instruments and markets in terms of potential loss per unit of currency.Although non-trading activities like management of the structural interest rate risk of the balance sheet,which arises primarily from banking activities and the management of the investment portfolio, do notrequire regulatory capital to support market risk or VaR measurement, some of the tools used to adjustthe exposure are often commonly used in non-trading areas.
17 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSHow Market Risk is Measured and ManagedFigure 7: The Trading and Banking Books are Each Modeled and Managed SeparatelySources ofMarket Risk»» Interest Rate–– Maturity–– Yield-Curve Risk–– Basis Risk–– Options Risk»» Exchange Rate»» Commodity»» EquityModels Used for Market RiskValue-at-Risk»» Variance/Co-Variance»» Historical Simulation»» Monte-Carlo SimulationModels Used for ALM»» NII Simulation»» Economic-Value-of-Equity»» GAPInternal Controls»» Board SeniorManagement oversight»» Policies, Procedures Limits»» Risk Measurement,Monitoring Information Systems»» Internal Controls AuditTrading BookBanking BookCommon uses of VaR analysis for the banking book include determining the component of economiccapital for structural balance sheet interest rate risk. This form of analysis has application to capitalallocation and Risk Adjusted Return on Capital. The properties of VaR can also be used to prepare multi-factor analysis that can isolate the risk contribution of individual factors over multiple holding periods.
18 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSVaR Case StudyTheVaR analysis in the attached appendices (pages 22-23) was performed by calculating themaximum loss resulting from disturbances in key stress factors related to the assets, liabilities, andoff balance sheet items of a Bank. After the computation results were summarized, the one standarddeviation (STD) loss arising from a one STD factor disturbance is simply:PriceVolatility = Factor Duration * FactorVolatility10By applying the PythagoreanTheorem to the constituent price movements, the net portfolio loss iscalculated as:Price Volatility =where n is the total of risk factors.The results for an example of this type of analysis are summarized in Appendices A and B (pages 22-23).The stress factors that contribute most to the expected loss are interest rate risk, mortgage spreadrisk, and deposit retention (see Appendix A). The largest factor is interest rate risk contributing 51%to the total maximum loss within a one-month interval.This risk factor grows in significance over timeexplaining 80% of the six-monthVAR.The second largest contributor is mortgage spread risk which accounts for 29% of the one-monthVAR and 6% of the six-monthVAR. Deposit retention uncertainty contributes roughly 17% to the one-monthVAR.The interest rate risk factor is composed of short rate risk, curve slope (yield curve twist risk), and amixed affect.The larger of the three is yield curve twist risk accounting for 82% of the one monthvariance based loss for this factor.This risk factor decreases in importance to 57% of the six monthvariance based loss.This trend is explained by the relationship between short rates and the curve slope.That is, these two factors are chosen because they are independent in the very short term. However,over the long term, they become more highly correlated.Therefore, curve twist risk tends to decreaseand a mixed effect grows as interest rate risk created by broad movements of the term structure growsin prominence.The relatively large curve slope component of thisVaR calculation is caused by (A) the term structuremodel specification, namely the factor selection with a higher volatility of the slope factor and a lowervolatility of the short rate; (B) the allocation of the Bank’s assets to the intermediate sector fundedin part by liabilities in the short sector of the term structure which causes them to be more sensitiveto the slope factor; and (C) the potential imperfection of this institution’s hedging program that wastraditionally based on the conventional single-factor rate model.One of the most interesting outcomes of this analysis was that mortgage spread risk (basis risk)contributes such a large portion to the Bank’s overall risk profile. None of this Bank’s interest raterisk controls adequately quantified or hedged this risk. From the factor attribution analysis, it is clearthat this factor diminishes in significance over time.Therefore, this item is a short-term problem withgreatest implications for the warehouse, the pipeline, and the mortgage servicing rights portfolio.10 For purposes of this example, we are making the assumption that the various risk factors of the balance sheet are independent. However,in reality, they have correlation. For a more robust analysis, correlation has to be accounted for and factors can be orthogonalized.
19 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSStrengths of VaR»» Provides “common” denominator upon which to manage risk»» Attaches probability to magnitude of loss»» Allows for decomposition of risksWeaknesses of VaR»» Susceptibility to assumptions»» Provides “likely” picture, but not extreme event results because the underlying assumption behindsome forms of VaR is a normal distribution»» Computational and resource requirements can be highVaR ConclusionVaR technology is somewhat mature since these models have been a regulatory requirementused to quantify regulatory capital to support trading activities since the 1990’s. The regulatoryrequirements impose similar standards on all large banks as to the quantification of trading risk usingVaR supplemented by stress scenarios. Meeting the regulatory requirement assures a reasonable levelof quality. While non-trading activities (that is, management of structural balance sheet interest raterisk or management of the investment portfolio) do not require regulatory capital to support marketrisk and therefore do not require VaR measurement, this volatility-based risk measure does offer moreinsight into the nature of structural balance sheet interest rate risk. Therefore, rather than a replacementfor other ALM risk management tools, VaR enhances the universe of ALM risk management tools since itviews the balance sheet from different underlying assumptions.3. Advances in ALM Risk QuantificationPrior to the credit crisis, ALM was focused almost entirely on a single risk factor: interest rate risk. Thisframework was inadequate because it did not predict capital and earnings exposure to market volatility.The global response to these events has imposed new regulatory requirements and has quickened the paceof innovation to create new tools and methods to more effectively assess and manage emerging risks.In many ways, both Basel III and Dodd Frank are forcing banks to implement a more holistic datainfrastructure and risk management framework. For example, Basel III imposes a strengthening of thecapital risk coverage to include:»» An integrated management of market and counterparty credit risk»» The assessment of risk due to deterioration in counterparty’s credit rating (the Credit ValuationAdjustment (CVA))»» The strengthening of capital requirements for counterparty credit exposures arising from banks’derivatives, repo, and securities financing transactions»» The elevation of counterparty credit risk management standards by including an explicit wrong-wayrisk Pillar I capital chargeIn addition, Comprehensive Capital Analysis and Review(CCAR) and Basel III require a more holistic viewwhen performing stress testing. All of these factors as well as many other parallel regulatory initiativesare overwhelming financial institutions compliance and IT teams. The industry response to theserequirements and lessons learned are starting to take shape. Both the Enterprise Risk Management(ERM) Platform and the integration of risk across the risk taxonomy are emerging as regulatorydeadlines near.
20 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS3.1 The Necessity of an ERM Data PlatformThe first step towards refactoring the legacy data architecture to meet imminent regulatory andrisk management requirements is to build a centralized risk and finance datamart. In the past, riskmanagement was done in silos. Basel III is now breaking down the walls of those silos. For example,there are now common data elements between Risk, Finance, and Compliance11:10»» Liquidity ratios require asset/liability cash flows as well as counterparty details. For example:–– Securities issuers asset classes, ratings information, and standardized risk weight for LCR buffer–– For NSFR, identify fully secured mortgage positions – eligible for 35% risk weight»» For leverage ratio, netting is allowed for OTC derivatives and REPO transactions but follows the Basel“Current Exposure Approach” used for credit risk RWA. The leverage ratio requires information fromthe risk system»» Accurate collateral and guarantee information optimizes regulatory capital, and new information isrequired for Basel III (for example, inception ratings when hedging securitizations)»» Regulators are even starting to implement consistency checks between risk and finance regulatoryreports (FRY 9C vs. FRY 14)»» A holistic view is now required when performing stress testing or when assessing regulatory costs fornew trades at origination»» Consolidating at a financial group level requires information about concentration risk, including directand indirect risks coming from collateral issuers, guarantors, or funds underlying the assets. (This is asignificant challenge for many institutions.)»» New regulatory requirements are challenging for many financial institutions that are still organizedinto silos. For example:–– Liquidity risk was traditionally managed by finance/treasury teams in charge of ALM. Risk andcompliance teams must be involved.–– Holistic stress testing is very challenging for many institutions. In addition, regulators challengestress test results more routinely now.In sum, the ALM function is evolving from a silo-based back office risk management function to moreof an integrated IRR, credit, and liquidity front-office balance sheet management function. A centralizedrisk and finance datamart is a required and fundamental element to navigate the regulatory risk thatwill dominate the attention of the global financial services industry over the next decade. Furthermore,metrics that are used for risk management decisions will be more consistent with regulatory reportingif the underlying data has the same source. A good rule of thumb is to focus on data and data qualityand invest in a centralized ERM system that meets most of the Basel III and Balance Sheet managementanalytics and reporting requirements. Lastly, the DataMart must interface with capital markets data,pricing sources, the channels of origination, the channels of distribution (i.e., securitization), andperform holistic stress testing.3.2 Advanced Analytics That Tie Risk Exposure to CapitalALM tools have evolved since the invention of the mainframe computer which made the first crude Gapreports possible. Then, gradually, as credit markets and financial instruments became more complexand computing resources became more powerful and less expensive, IRR measurement became moresophisticated. Simple gap reports gave way to more complex simulation models which eventually leadto Multi-path Probabilistic distributions of earnings and value. Today, we are on the verge of a new erain the quantification of IRR.11“Regulatory Capital and Liquidity Risk Compliance”; Moody’s Analytics Risk Practitioners Conference; October 2012; Pierre Etienne Chabanel andRobert J Wyle
21 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSTimeline of IRR Measurement EvolutionSource: New York Institute of Finance, 1997Market risk and credit risk have been converging for the last decade. There are numerous examplesof credit risk factors that are standard features of risk management and pre-trade analytics systems(for example, Intex, Moody’s Analytics Structured Analytics and Valuation, the Andrew Davidson,and Company Loan Dynamics module). The credit crisis and regulatory response has accelerated theevolution of integrated analytics across the taxonomy of risk types. In addition, the new technologiesthat are being created are being applied to traditional problems like funds transfer pricing, linking paywith performance, capital allocation, or defining risk appetite.The future requirement in Asset and Liability Management will be stress testing on an integrated IRR,credit, and liquidity risk platform. Regulatory requirements are forcing banks to evaluate a more holisticstress testing regime based on severe macro economic scenarios. Understanding the joint dynamicsbetween credit and interest rate risk is critical if banking organizations want to stay competitive.The empirical evidence suggests that at the aggregate level, there is significant negativecontemporaneous correlation between changes in short interest rates and default rates, and significantpositive contemporaneous correlations between the changes in the slopes of term structure and defaultrates. Over time, changes in interest rates and default rates show negative correlations. In addition, thecorrelations seem stronger around financial crises.1211Therefore, understanding the relationship betweencredit and interest rate risk is critical to many applications in finance, from valuation of credit andinterest rate-sensitive instruments to risk management and stress testing.Given the lessons learned as a result of the credit crisis, there are a number of applications for an ERManalytical platform with integrated market and credit analytics across the various disciplines of Assetand Liability Management risk functions:»» Analytics based on the same market data (rates, prices, volatilities, FX), transaction-level bank data,behavior models, new volume assumptions, and cash flow engine provide more consistency. That is,silo-based risk management based on different underlying data, analytics, and assumptions are likelyto produce results that imply different risk quantification answers. For example, if the IRR profile andthe FTP models are different, the incentives implied through transfer pricing can be inconsistent.12 “The Relationship Between Default Risk and Interest Rates: An Empirical Study”; Andrew Kaplin et al; Moody’s Analytics; October 2009Figure 85.04%GapAnalysisBetaweightedGap; CrudeSimulationCategory-levelsimulation; Staticcash ﬂow marketvalueProbabilisticEvaluation; Moredetailed simulation;Integration of systmes4.0%4.5%5.0%-300 Bps +200Bps +300Bps+100BpsBase Rates-100Bps-200Bpslate 1960’sIndicators1970’sIndicatorsEstimates1980’sIndicatorsEstimates Measures1990s onwardsAdvanceMeasures
22 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICS»» Principle 4 from the Basel Liquidity Risk Working Group publication “Principles for Sound LiquidityRisk Management and Supervision” states that “a bank should incorporate liquidity costs, benefitsand risks in the internal pricing, performance measurement and new product approval process forall significant business activities (both on- and off-balance sheet), thereby aligning the risk-takingincentives of individual business lines with the liquidity risk exposures their activities create for thebank as a whole.” A robust method to compute the contingent liquidity risk transfer price involvesthe evaluation of the joint dynamics of interest rate risk and credit risk.1312»» The facilitation of capital management for concepts like capital allocation provision for loan andlease losses all require computations of economic capital that benefit from the evaluation of the jointdynamics of interest rate and credit risk.4. ConclusionThe nature of risk management is evolving rapidly in the wake of the credit crisis and the correspondingregulatory response. Regulatory pressure to integrate across the taxonomy of risk types is forcing banksto improve their ERM practices and invest in centralized data infrastructure and software. In addition,practices that more closely associate Treasury processes (that is, liquidity risk management, capitalmanagement, and balance sheet management) with risk practices across the enterprise are being re-evaluated. More effective risk assessment and risk reporting processes are required.As it was in the past, no individual risk metric is ideal. Rather, they all have strengths and weaknesses.Institutions use those tools that quantify risk consistent with the complexity of the balance sheet.However, viewing risk using metrics based on different underlying assumptions can provide insight intoevolving market conditions. In particular, firms that were able to quickly adjust forward-looking scenarioanalysis or integrate measures of market risk and counterparty credit risk into their positions acrossbusinesses were better able to assess evolving market conditions. Thus the tying of risk to losses incapital and earnings exposures and explain those losses will be a major determinant for banks that wantto remain competitive.13 See “Implementing High Value Funds Transfer Pricing Systems”; Wyle and Tzaig; 2011
23 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSFurther ReadingAdditional materials are available to download at www.moodysanalytics.com/riskconfidence»» The New Path to Shareholder Value – Robert J. Wyle, CFA. World Finance Magazine, May-June 2012.»» Implementing High Value Funds Transfer Pricing Systems - Robert J. Wyle, CFA, Yaakov Tsaig, Ph.D.September 2011.»» Risk Integration: New Top-down Approaches and Correlation Calibration – Nan Chen, Andrew Kaplin,Amnon Levy and Yashan Wang. January 2010.»» The Relationship Between Default Risk and Interest Rates: An Empirical Study - Andrew Kaplin,Amnon Levy, Shisheng Qu, Danni Wang, Yashan Wang and Jing Zhang. October 2009.About Moody’s Analytics ALM SolutionRiskConfidence™ is Moody’s Analytics enterprise asset and liability management (ALM) softwaresolution. It goes beyond the traditional ALM feature set by creating a high value front office balancesheet management function. The Solution integrates ALM, liquidity risk management, credit, andregulatory reporting functions onto a single platform with a common data source and single enginestrategy. For more information, please visit www.moodysanalytics.com/riskconfidence
24 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSAppendix A – VaR Risk Factor DecompositionMonthly VarianceQuarterly VarianceSemi-Annual VarianceInterest RatesDeposits RetentionMortgage Spread COFI RatesVolatility AssumptionPrepayment Error28.5%0.4%16.6%3.1%0.0%0.0%6.1%0.5%11.2%2.5%79.8%11.7%0.5%15.3%3.1%69.4%51.3%0.1%
25 MARCH 2013 AN EVALUATION OF INTEREST RATE RISK TOOLS AND THE FUTURE OF ASSET LIABILITY MANAGEMENTMOODY’S ANALYTICSAppendix B – VaR Interest Rate Risk DecompositionQuarterly VarianceSemi-Annual VarianceShort rate Mixed EffectCurve slope13.60%15.87%26.74%57.39%17.77%68.62%81.60%