Asset Liability Management, Liquidity & Interest Rate Risk Solutions

Liquidity Risk Analysis
There is also a requirement for analytical tools to model and measure liquidity risk that describe the extent to which the bank can support its future monetary obligations and assure its solvency. The future monetary obligations represent the bank’s liquidity demands, which are primarily the contractual obligations to pay monetary amounts to counterparties to the debt and other financial liability instruments held in the bank’s portfolio. In addition, the future monetary obligations may include fees and expenses the bank is obligated to pay on future dates.

To satisfy these monetary obligations, the bank uses its liquidity resources, which include future monetary receipts, liquid assets, and contingent liquidity resources. Future monetary receipts are primarily the contractual obligations of counterparties to pay monetary amounts on the loans, bonds, and other financial asset instruments held in the bank’s portfolio. Future monetary receipts may also include fees and expenses the bank expects to receive from counterparties on future dates. Liquid assets are financial asset instruments, such as government or corporate bonds with active trading markets, that can be liquidated quickly when required and are not pledged or otherwise encumbered under collateral agreements with counterparties. Contingent liquidity resources are credit commitments by counterparties to fund debt instruments issued by the bank on one or more future dates upon the bank’s demand.

From a cash flow perspective, the bank’s net liquidity requirement is the difference between the bank’s future monetary receipts and the bank’s liquidity demand, i.e. its future monetary obligations. Thus the analysis of the bank’s liquidity risk is an analysis of the net cash flows from financial instruments in its portfolio over a given future time period together with an analysis of the potential funding available from liquid assets held in the bank’s portfolio and any contingent liquidity resources available to the bank.

The KRM solution provides functionality to support the analysis of the net liquidity requirement, liquid assets, and contingent liquidity resources that is required for liquidity risk analysis. The multi-period portfolio simulation functionality in the KRM Analytical Engine projects the future component cash flows for each asset and liability instrument in the bank’s portfolio for each simulation period in the specified simulation calendar under the risk factor vector and yield curve sample paths applied to the simulation. This functionality is discussed in detail above.

The projected future cash flows for each financial instrument produced by the multi-period simulation can be retained in the KRM database, where they are available for further analysis. The future cash flow results are differentiated by their component source (e.g. a principal or interest cash flow), and the results for both assets and liabilities in a portfolio (as well as all derivative instruments) are available. These simulated results are also differentiated by date, scenario, and the specific financial instrument, and they can be aggregated by source, date, scenario, financial instrument identifier, other attributes of each financial instrument, and attributes of other modeling elements, such as counterparty identifier or the geographic location of each counterparty.

The aggregated results of a multi-period portfolio simulation of individual financial instruments can be used as the basis for various aggregate cash flow demand measures, such as cumulative net cash outflow over one or more daily time periods. Cumulative net cash outflow is a measure of the accumulated net cash outflows from the portfolio during a specified interval of consecutive daily periods in a simulation calendar that represents a cash deficiency for the time interval under a given yield curve and risk factor scenario (sample path) when it is positive. Cumulative net cash outflow provides a measure of the illiquidity of a portfolio over a given time interval that may result in insolvency of the investing entity if the cash flow deficiency cannot be covered from available liquid assets and contingent liquidity resources in a timely manner.

Liquid assets and contingent liquidity resources are potential sources of contingent cash inflows that can offset a cash flow deficiency indicated by a cumulative net cash outflow measure. Each liquid asset or contingent liquidity resource can potentially provide a cash inflow needed to offset a positive cumulative net cash outflow, and liquid assets and contingent liquidity resources can be categorized by their available amounts and by a funding delay representing the time required to produce the contingent cash inflow subsequent to a funding request. KRM can model these liquid assets and contingent liquidity resources as individual financial instruments and can determine the funding amounts and timing available for each instrument.

To determine the potential future funding available from liquid assets, the multi-period portfolio simulation functionality in the KRM Analytical Engine simulates the future economic values of each liquid asset instrument during each future simulation period in the simulation calendar using the valuation methods applicable to the instrument and the risk factor vector and yield curve sample path values for the period. These economic values are adjusted by a liquidation factor that defines a haircut appropriate to the potential change in the value of the liquid asset instrument over a liquidation period subsequent to the valuation date.

Potential future funding may also be available from contingent liquidity resources, such as credit commitments, through exercise of the bank’s rights under the credit commitments. The KRM solution can model these credit commitments, and the KRM Analytical Engine can simulate their drawdown behavior in future simulation periods. A simulated drawdown results in a cash inflow to the portfolio equal to the amount of the drawdown as well as the issuance by the counterparty of a debt instrument with a principal amount equal to the amount of the drawdown. The drawdown simulation is performed using a drawdown model applicable to the credit commitment and the risk factor vector and yield curve sample path values for the period when the drawdown occurs.

Cumulative net cash outflow and other liquidity demand metrics calculated from the simulation results can be compared with the liquid assets and contingent liquidity funding metrics to determine the extent to which these contingent funding sources cover the potential liquidity demand under a given yield curve and risk factor scenario. Banks normally hold sufficient liquidity resources to cover all potential liquidity demands, but this comparison shows the extent to which the contingent funding sources may be consumed to meet projected cumulative net cash outflows.

KRM can also calculate net liquidity demand and liquidity funding metrics when a deterministic chronological risk factor scenario is applied to the multi-period portfolio simulation. Application of the risk factor scenario will modify the risk factor vector and yield curve sample path for the simulation, and the net liquidity demand and liquidity funding amounts under the scenario will generally differ from the amounts simulated in the base case, since different cash flows will occur under the scenario. The ability to apply the chronological risk factor scenario to the multi-period portfolio simulation provides a means of stress testing the net liquidity demand and liquidity funding metrics for a portfolio under specified future market and macroeconomic conditions.

Liquidity risk analysis in KRM does not have to be limited to runoff of a static portfolio. As discussed above, users can model dynamic portfolio strategies where portfolio runoff cash flows can be reinvested in or refinanced by new financial asset or liability instruments, and the KRM Analytical Engine can simulate these dynamic portfolio strategies. This means that the projected future cash inflows and outflows can include cash flows from simulated reinvestment or refinancing transactions. It also means that potential growth in the portfolio over the future analysis period, including additional liquid assets and contingent funding sources, can be incorporated in the liquidity risk analysis.

The discussion above considers cash flow liquidity analysis in the context of a single yield curve and risk factor sample path (scenario), which is a basic form of multi-period portfolio analysis that can be performed by the KRM Analytical Engine. However, the KRM Analytical Engine also can perform a multi-period portfolio simulation using a set of stochastic scenarios (yield curve and risk factor sample paths) obtained from either historical/external sampling or Monte Carlo simulation of a vector stochastic process model, as discussed earlier. Each of the resulting stochastic scenarios can then be used to perform the cash flow simulation process outlined above, and the results of this process can be aggregated to provide a corresponding sample set of cumulative net cash outflow measures for individual financial instruments and the portfolio.

This stochastic simulation provides a sample distribution of potential cumulative net cash outflows over a specified time interval, which can be compared to the available liquid assets and contingent liquidity resources available during that interval to produce a sample distribution of net liquidity resources for the portfolio. Order statistics of the sample distribution of net liquidity resources provides the basis for estimating the likelihood of the portfolio becoming illiquid at a specified confidence level. These statistics are probabilistic liquidity risk metrics for the portfolio.

The KRM Risk Portal can summarize and display the liquidity risk metrics produced by the multi-period portfolio simulations performed by the KRM Analytical Engine. The KRM Risk Portal includes several standard liquidity risk reports, an example of which is shown below. 

 
 

Regulatory Interest Rate and Liquidity Risk Modeling and Analysis

Regulatory interest rate and liquidity risk modeling and analysis is also dealt with in KRM. In particular, KRM addresses analysis of interest rate risk in the banking book (IRRBB), determination of capital adequacy for interest rate and other market risks, analysis of liquidity risk and liquidity ratios under the Basel II and Basel III guidelines, and back-testing of the models used to perform these analyses.

Interest Rate Risk in the Banking Book
The Basel Committee has established guidance for the management of interest rate risk in the banking book in its publication Principles for the Management and Supervision of Interest Rate Risk, which is part of the supervisory guidance in Pillar 2 of Basel II. This guidance requires that several different sources of interest rate risk be considered in the analysis of interest rate risk, including repricing risk, yield curve risk, basis risk, and optionality risk.

Repricing risk is concerned with the repricing of interest-bearing instruments at maturity or floating-rate reset dates. As discussed above, repricing risk can be analyzed in part using the repricing maturity gap analysis capabilities available in KRM.

Yield curve risk arises from changes in the income/expense or economic values of financial instruments resulting from changes in the slope and shape of the yield curve used to value an instrument or calculate an instrument’s floating-rate coupon amount. The discussion above shows how the KRM solution can model yield curves, simulate their evolution over time, and apply the resulting yield curve to the future valuation of financial instruments or to the calculation of future floating-rate coupons. These capabilities allow KRM to analyze the yield curve risk associated with a portfolio.

Basis risk arises from imperfect correlation in the adjustment of the rates earned and paid with otherwise similar repricing characteristics, which may result in unexpected changes in the cash flows and earnings spread between assets, liabilities, and off balance sheet instruments. This occurs both because of instruments having different floating-rate reset dates and because the floating-rate indices used to reset the coupon rates may differ across instruments. The KRM solution allows explicit specification of the floating-rate coupon reset timing and frequency, and it permits different floating-rate indices possibly referencing different yield curves to be specified as the source of index rates. This allows the KRM Analytical Engine to simulate future interest rate cash flows and interest income/expense accurately for different financial instruments in a portfolio and to use the results to assess the extent of basis risk for the portfolio.

Optionality risk arises from the non-linear effects of changes in interest rates on embedded interest rate options in financial instruments in a portfolio. Analysis of optionality risk requires that all embedded interest options in each financial instrument are explicitly modeled and that the options be considered in the valuation, cash flow, and income/expense analysis of the instrument. The KRM solution allows all forms of embedded interest rate options to be modeled in the financial instrument template for each instrument. The KRM Analytical Engine uses the embedded option attributes to determine the economic value of the embedded options and to simulate future cash flows and income/expense arising from exercise of the embedded options. This permits the user to assess the effects of optionality risk in a portfolio.

The supervisory guidance also includes the requirement to assess the effect of a standardized interest rate shock on the interest rate risks discussed above. The standardized interest rate shock is an upward or downward 200 basis point parallel rate shock or upward and downward changes in the yield curve consistent with the 1st and 99th percentile of observed interest rate changes using a one-year (240 working days) holding period and a minimum five years of observations. The KRM solution can be used to model these yield curve shocks for each yield curve by setting up yield curve tenor point risk factors for a range of tenors on each yield curve and by defining a risk factor scenario incorporating these tenor point risk factors and specifying the basis point changes for each tenor point in the risk factor scenario. The effects of the risk factor scenario on portfolio valuation, cash flows, and income/expense can then be assessed by performing a portfolio valuation or simulation analysis.

The KRM Risk Portal includes several standard interest rate risk reports, an example of which is shown below. 

 

Capital Adequacy Analysis for Interest Rate and Other Market Risks
The Basel II/III capital adequacy guidelines establish capital requirements for the interest rate and other market risks of interest rate related and equity instruments held in a bank’s trading book. Under Basel II/III, the interest rate and other market risk capital requirements for the trading book are calculated under either the Standardized Measurement (SM) Method or the Internal Models (IM) Approach. The capital requirement for the IM Approach is calculated as a function of the daily Value-at-Risk (VaR) metric of the portfolio at the 99% one tail confidence interval based upon an instantaneous shock equivalent to a 10-day movement in rates or prices. To determine the capital requirement, the higher of the value of the daily VaR metric for the preceding business day and the average of the daily VaR metric values over the preceding 60 business day period is calculated, and this result is multiplied by a factor specified by the bank’s regulator that has a minimum value of 3.

The VaR metric calculations under the IM Approach require modeling of the interest rate and other market risk factors relevant to the financial instruments in the portfolio. For interest rate risk, a set of tenor point risk factors for at least six tenor points must be modeled for each yield curve, and different sets of tenor point risk factors must be modeled for yield curves denominated in different currencies. In addition, tenor point yield spread curve risk factors must be modeled for different categories of financial instruments to capture the spread risk arising from less than perfectly correlated movements in rates associated with those categories. If the portfolio contains financial instruments denominated in different currencies, spot FX rates between the bank’s functional currency denomination and each foreign currency denomination must also be modeled. In addition, interest and spot FX rate volatility surfaces for various embedded options or explicit option instruments held in the bank’s portfolio must also be modeled.

The KRM solution supports the interest rate and other market risk capital adequacy requirement calculations under the IM Approach. In particular, the KRM solution allows multiple tenor point risk factors to be modeled for each yield curve and each yield spread curve, and it permits multiple yield curves to be defined in one or more currency denominations. It also allows spot FX risk factors for various pairs of currencies and other market risk factors to be defined. Additionally, volatility surfaces for the forms of embedded options and explicit option instruments available in KRM can be modeled for different exercise rates, exercise tenors, and payment tenors, and these volatilities can be modeled as risk factors. The tenor point, FX rate, and volatility risk factors can be specified as components of the risk factor vector applicable to the portfolio and can be used for analysis of the portfolio. As discussed earlier, the KRM Analytical Engine can calculate the VaR metric for a portfolio at a user-specified confidence level over a user-specified analysis horizon using the risk factor vector with the required tenor point and spot FX rate risk factors, and the resulting VaR metric value can be retained in the KRM Database for the valuation date. The daily VaR metric results over a 60 business day period can be retained in the KRM Database, and these results can be averaged and compared with the previous day’s VaR metric result. The higher of these two values can be multiplied by the regulatory factor to determine the interest rate and other market risk capital requirement for the portfolio.

The KRM Risk Portal includes several standard VAR reports, an example of which is shown below. 

 

Analysis of Liquidity Risk
The Basel Committee has recently established guidelines for liquidity risk that are contained in the document Basel III: International Framework for Liquidity Risk Measurement, Standards, and Monitoring. These guidelines include two minimum standards for funding liquidity that promote short-term resilience of a bank’s liquidity risk profile by assuring sufficient high-quality liquid assets to survive a stress scenario as well as longer-term resilience achieved through more stable funding sources. The short-term objective is reflected in a liquidity coverage ratio (LCR) for the bank’s portfolio, while the longer-term objective is incorporated in a net stable funding ratio (NSFR) for the portfolio.

The LCR is defined as the ratio of the value of the stock of unencumbered, high-quality liquid assets to the total net cash outflows of the portfolio during the next 30 calendar days under a significantly severe liquidity stress scenario specified by the bank’s supervisor. Under the standard, this ratio must equal or exceed 100% for the bank to be considered to have adequate liquidity.

The value of the stock of unencumbered, high-quality liquid assets applicable to the LCR is the current market value of those assets less any market value haircuts applicable to the assets. The LCR classifies high-quality liquid assets as either Level 1 or Level 2 assets. Level 1 assets include cash, government securities, and other instruments that can be liquidated with no market value haircut. Level 2 assets include marketable securities, such as corporate bonds, that under stressful conditions would likely require a market value haircut for liquidation. The LCR calculation applies a 15% market value haircut to Level 2 assets.

The total net cash outflows of the portfolio for the subsequent 30 calendar days applicable to the LCR is the total expected cash outflows minus the smaller of total expected cash inflows and 75% of the total expected cash outflows. Total expected cash outflows includes retail deposit runoff, stable deposit runoff for deposits covered by an effective deposit insurance scheme and for deposits not covered by an effective deposit insurance scheme, retail fixed-term deposits runoff, unsecured wholesale funding runoff for funding provided by various categories of depositors that is callable within 30 calendar days or having a contractual maturity date within this horizon, secured funding runoff, derivatives payables, drawdowns on the undrawn amount of credit and liquidity commitments of various types where the counterparty is the beneficiary net of any high-quality, liquid assets the beneficiary has posted as collateral under a collateral agreement covering the commitment, funding provided by contingent liability instruments, such as credit guarantees, where the counterparty is the beneficiary, and other contractual outflows.

The total expected cash inflows of the portfolio for the subsequent 30 calendar days applicable to the LCR includes maturing reverse repurchase or securities lending agreement cash flows where the agreement is secured by Level 1 collateral, contractual inflows from retail and small business customers, contractual inflows from wholesale customers and counterparties, derivatives receivables, and other contractual inflows. Total expected cash inflows excludes portfolio cash inflows from unencumbered, high-quality liquid assets.

The liquidity stress scenario used for the LCR entails a combined idiosyncratic and market-wide shock that results in:

  1. The run-off of a proportion of retail deposits;
  2. A partial loss of unsecured wholesale funding capacity;
  3. A partial loss of secured, short-term financing with certain collateral and counterparties;
  4. Additional contractual outflows that would arise from a downgrade in the bank’s public credit rating by up to and including three notches, including collateral posting requirements;
  5. Increases in market volatilities that impact the quality of collateral or potential future exposure of derivative positions and thus require larger collateral haircuts or additional collateral, or lead to other liquidity needs;
  6. Unscheduled draws on committed but unused credit and liquidity facilities that the bank has provided to its clients; and
  7. The potential need for the bank to buy back debt or honor non-contractual obligations in the interest of mitigating reputational risk.

For purposes of calculating the LCR, this liquidity stress scenario is translated into haircuts for each of the categories of expected cash outflows and of expected cash inflows outlined above. Thus calculation of the LCR does not require specific risk factor and yield curve scenarios to determine the expected cash outflows and inflows. Instead the runoff amounts for each category of expected cash outflows and inflows are determined by regulatory haircuts assigned to those categories.

The KRM solution supports calculation of the LCR. It allows high-quality, liquid asset instruments to be categorized as high-quality, liquid positions of various types and as Level 1 or Level 2 assets, and it permits each instrument to be either designated as unencumbered or encumbered or alternatively identifies any collateral agreement encumbering the instrument and excludes the instrument. The KRM Analytical Engine can calculate the current market value of each asset instrument as discussed earlier in respect to portfolio valuation, and any market value haircuts can be applied to Level 2 asset current market values. To determine the expected cash outflows and expected cash inflows for a portfolio, the KRM solution models the financial instruments in each category of expected cash outflows and inflows, including the notional balances of each instrument, and assigns the regulatory haircut for the category to the instrument. KRM then calculates the expected cash outflows and inflows as the product of the notional balance of each instrument and the regulatory haircut for the instrument and sums the resulting expected cash outflows and inflows across all instruments in the portfolio.

The NSFR is defined as the ratio of the available amount of stable funding to the required amount of stable funding. Available stable funding is the total amount of a bank’s capital, preferred stock with maturity equal to or greater than one year, liabilities with effective maturities of one year or greater, sticky’ non-maturity and term deposits with maturities of less than one year, and ‘sticky’ wholesale funding sources with maturities of less than one year. The amount of available stable funding from each source is the carrying amount of the source multiplied by a factor related to the stability of the source. The stability factor ranges from 100% for capital to 50% for somewhat stable funding sources like unsecured wholesale funding to 0% for sources not considered stable.

Required stable funding is calculated as the sum of the value of the assets held and funded by the bank multiplied by a monetization factor for each asset category related to the relative market liquidity of that category and the portion of asset value that could not be monetized through sale or use as collateral under a long-term collateral agreement. The monetization factor ranges from 0% for cash and short-term instruments to 20% for unencumbered corporate bonds, to 85% for unencumbered retail loans and 100% for assets that are difficult to monetize.

Under the standard, the NSFR ratio must equal or exceed 100% for the bank to be considered to have adequate liquidity.

The KRM solution supports calculation of the NSFR. It allows stable funding source instruments to be categorized according to the funding stability of each instrument and models the amount of funding provided by the instrument and the stability factor for the instrument. To determine the available stable funding, KRM multiplies the funding amount by the stability factor for each instrument and sums the resulting amounts across all instruments in the portfolio. The KRM solution also allows assets and off balance sheet instruments requiring potential funding to be categorized according to the relative market liquidity of each instrument and models the funding requirement for the instrument. To determine the funding requirement, KRM multiplies the value of the instrument by the monetization factor associated with the instrument and then sums the resulting amounts across all instruments in the portfolio.

Sample LCR and NSFR reports are shown below.