Solutions

Asset Liability Management

Main Tasks of Asset Liability Management

Asset Liability Management (ALM) is one of the main tasks of banking or investment institutions. The aim is to effectively hold asset and liability portfolios along the time axis and optimize the RORAC = Return/Risk, using various evaluation and strategy approaches. Due to the complexity of sophisticated mathematical models, effective management of finances includes the application of software tools and systems. Our solution in terms of ALM is as follows:

  • Modeling and pricing of various Instrument types.
  • Hierarchical structuring of portfolios in Assets, Liabilities, Off-balance positions and their sub-portfolios, using lists, filters and structures.
  • Generation and evaluation of cash flows (fixed, float, pay-offs).
  • Definition of regular and irregular future periods to be used for ALM analysis.
  • Calculation on position level and aggregation on portfolio level of prices and measures, such as Net Present Value
  • Modified Duration, Convexity, Duration, Dispersion, Internal Rate of Return, etc.
  • Calculation of opportunity rates based on interest rate expressions, which are able to define rates, such as floater, spread, step-up/down, maturity mixed, curve mixed, currency mixed, historic, average, depressed, etc.
  • Definition and usage of future scenarios for market factors (FX, Interest rates, Stock Indexes, yield curves, etc.) and for capital development (liquidity scenarios), including prolongation, increase/decrease of business, losses of defaulted debtors, budget plans, future cash flow obligations, etc. The usage of market and liquidity scenarios enables future assessments and stress tests of assets and liabilities. The involvement of simulated positions in the portfolio is a flexible way of planning and investigating future cash flows.
  • Performing advanced ALM analysis, related to future interest and capital developments and transformations, including:
    • Cash flow analysis and GAP-Analysis for future periods, according to cash flow types, such as capital decrease/increase, interest rate payments, expectation payoffs of stochastic modeled instruments.
    • Interest Income that calculates interest rates, margins and contributions for the asset and the liability side, based on rates of alternative or market relevant business. Gross, Conditional and Structural Margin and Contribution are then obtained between asset and liability side.
    • Fund Transfer Pricing (FTP) that calculates market and contribution of assets and liabilities, based on the opportunity of alternative businesses on different contract maturity. This functionality is known as maturity transformation, i.e. long time loans at high interest rates are refinanced by short time rollover deposits at lower interest rates.
    • Refinancing and reinvestment, including the construction of planned positions to perform refinancing or reinvesting of future cash flows.
    • Replication portfolios used to model the future of financial instruments without knowing the cash flows or payoffs, such as rollovers and non-term liabilities.

Calculation Structure and Data Flow

The calculation structure involves separate handling of the asset and the liability side, where interest rates, opportunity rates, conditional margins and corresponding contributions are calculated in two dimensions:

  • along future periods, as defined by the analytic scheme; for example, daily in the first week, weekly in the first month, monthly in the first year, etc. The results can be aggregated within the analytic scheme, for example, for the first three months.
  • along the sub-portfolio hierarchy, by aggregating on every sub-portfolio level.

Calculation of FTP results, i.e. structural margins and contributions – for assets, for liabilities as well as for the difference between assets and liabilities – is performed in a next step, where the split between assets and liabilities is based on unit interest rates, for example, on three-month Libor, or some Overnight Treasury rate.

Asset and liability modules use input data from external and internal data bases, user input, downloaded market data and internal calculation results for financial instruments. Input data and results are stored into the database for subsequent reporting. The general scheme of the data flow in ALM follows the main steps of data preparation and processing:

  • Import position data and instrument data via standard importer.
  • Access market data (for example, curves, FX rates, indices) from the client’s core data base or via provider supply.
  • Define lists, filters and structures needed for portfolio structuring.
  • Perform configuration of settings, financial calendar, nomenclatures, scenarios, time schedules and opportunity rate definitions.
  • Run position and portfolio calculations, as well as ALM analyses, including aggregation of results and scenarios. Store the results into reporting data base tables.
  • Report the results in different formats:
    • Export to Excel
    • Standard reports: Crystal report, Jasper report, Oracle reports
    • Special reports: OLAP reports using QlikView
    • Regulatory reports: COREP, XML/XBRL output, WEB Browser presentation

Functionality

Cash Flow types
  Capitals
Interest rates   Fixed Stochastic
Fixed Credits with fixed interest rate Credit with amortization option
Floating Credits with floating interest rate Floating certificate of deposit
Stochastic Deposits with future interest rate agreements
  • Accounts
  • Overdrafts
  • Credit lines

ALM deals with Interest Rate and Capital cash flows and Pay-offs in future periods. Depending on the interest rate type, fixed and float cash flows can be considered. A stochastic component is included in case of options, representing expected cash flows and distributions.

Market Environment

This module provides means to manage the market environment that is used by all modules:

  • Instrument prices and dividends
  • Yield curves, credit spread curves and indexes
  • FX rates
  • Stock indexes
  • IB rates
  • Implied volatility
  • Bond future baskets
  • Life table for insurance instruments
  • Application of multiple markets and providers
Market and Liquidity scenarios

ALM analyses can be performed under scenario and stress test conditions. Market scenarios define supposed changes in market variables, such as interest rates, exchange rates, prices and indexes of market environment.
Liquidity scenarios include the definition of future developments, reinvestments or refinancing strategies that represent expectations of future changes in cash flows, prolongation of instruments and payments, increase/decrease of business volume, expected and unexpected losses at debtor bankrupts, etc. Liquidity scenarios can represent budgets and financing plans. The gap between the current portfolio’s future without scenarios and expectations of the portfolio’s future behavior is represented in the analysis, via synthetic planned positions at assumed future market conditions. Original portfolios are calculated together with synthetic positions. The calculated results are then used to make decisions about future behavior at the present time point.
Every ALM scenario can combine market and liquidity scenarios.

ALM Analyses

ALM analyses provides means to represent the future cash flow disposition and detects any gaps or investment efficiencies in the presence of different market scenarios. Different future behavioral changes (Growth, Defaults of large customers, deposits increase, etc.) may be activated in cash flow scenarios in order to optimize the asset and liability management.

Analysis Functionality
  • Portfolio Evaluation:
  • Current, future and historic calculation
  • Calculation of net values, interest rate of return, key features of portfolio cash flows
  • Credit spread risks, Margin and Contribution calculation
  • Expected Loss, Credit/Deposit Value Adjustment (CVA/DVA)
  • Multiple market and liquidity scenarios applicable
  • Cash Flow Analysis:
  • Cash-flow disposition
  • Cash-flow GAP analysis
  • Performed on assets and liabilities, Gap-Analysis, cumulated Gaps, position contributions, re-pricing of capital or interest rates
  • Multiple market and liquidity scenarios applicable
  • Interest Income Analysis:
  • Interest income calculation
  • Fund transfer pricing
  • Liquidity Value at Risk
  • Income Gap analysis
  • Performed on assets and liabilities, GAP, aggregation to sub-portfolio levels, average rate, margins and contributions, pay-offs, refinancing and reinvestment
  • Multiple market and liquidity scenarios applicable
Supporting Modules: Definition of opportunity rates, Interest rate expressions, Market and liquidity scenarios, Analysis by periods and time schemes, Static and dynamic portfolio structures and sub-portfolios

Future Developments and Extensions

The models of our risk management system can easily be customized and its functionality is enhanced in a flexible way, by adding new model scripts. Its implementation is based on a well-known artificial intelligence tool - the expert system shell with inference engine. Scripts are used to model the Windows GUI, using model variables to define the business logic, expressed in rules. Models are interpreted by the expert system and the inference engine runs rules on model variables. Changes in models are handled online, which means that the changes are activated immediately after editing and reloading the model. Additional modules are currently being added:

  • Development of advanced strategies to generate proposals for automatic Gap covering.
  • Extending the generated XML-based COREP reports and session protocols.
  • Implementation of a spread analysis module, that calculates credit, liquidity and yield spreads, based on expected
  • Credit losses and the deduction of market prices related to theoretical prices.
  • Implementation of an extended audit trail that should record changes of input data and system parameters.

Interfaces and Connectors

The ALM module inherits the features of Risk Framework Interfaces and Connectors, including:

  • Importing data from external sources using flat files.
  • COREP reporting for regulatory results, Crystal reporting tool.
  • Export and Import Interfaces to MS Windows directly and via Clip Board.

FAQ

1. What types of market data is needed for ALM Analyses?
Portfolio instruments determine which market data is needed in the portfolio assessments. In general, the calculation of instruments, positions and portfolios uses market curves, FX rates, prices, indexes etc.
2. How is actual market data supported?
Market data can be downloaded from official providers or manually prepared and saved on the server or the local database. Users bear the responsibility for the validity of market data. Additionally, market data can be imported via standard importers, as well as via XML-imports.
3. How can a portfolio structure be defined, in order to correspond to the bank’s balance sheet?
A bank’s balance sheet can be represented via a hierarchical portfolio structure 1 to 1, using sub-portfolios. A filter definition is assigned to every sub-portfolio level, determining positions that belong to the sub-portfolio. An ALM balance tree structure includes Asset and Liabilities at the first sub portfolio level, performing the analyses for both sides of the balance sheet.