MNK Risk Consulting > FI METRICS


Credit risk is relevant to loan portfolio managers, fixed income portfolio managers, investment advisors, and of course risk managers in banks, investment firms, investment funds. Credit risk is also relevant to counterparties trading over-the-counter derivatives.

FI METRICS provides the professionals above with the capability to quantify – know – manage credit risk in a forward looking manner. The application allows for infinite changes on the composition of portfolios; just download the data-input template, fill it in and upload to run the loss simulation, then re-fill-upload-run and so on and so forth…on line and secured.

The FI METRICS application

is based on a portfolio approach to modelling credit default risk that takes into account information relating to the size and maturity of an exposure, any security held as secondary way of repayment, historical recovery rates, the credit quality and systematic risk of obligor/counterparty/issuer;

is capable of handling all types of instruments that give rise to credit exposure, including:

  • fixed income instruments such as Bonds, Treasury Bills and other forms of traded credit
  • Loans, Overdrafts, Credit Cards and other forms of funded credit
  • non-interest bearing receivables (trade finance products) such as financial Letters of Credit
  • Commitments to lend, unutilized Credit Cards, Letters of Guarantee and other forms of non-funded credit
  • market-driven Over The Counter derivative instruments (Swaps, Forwards, etc.) that create two-way credit counterparty risk.

The FI METRICS application does not prescribe the use of any one particular data set over another. One of the key limitations in modelling credit risk is the lack of comprehensive default data. Where a firm has its own information that is judged to be relevant to its portfolio, this can be used as the input into the model. Alternatively, conservative assumptions can be used while default data quality is being improved.

The data required by the application are the minimum necessary.  The data inputs for each obligor/counterparty/issuer in the portfolio are:

  • Credit exposure
  • Credit rating or default probability
  • Maturity remaining
  • Recovery rate

For some of these transaction types, it is necessary to work out the level of exposure in the event of a default:  for example, what is the credit exposure for a performance guarantee, or what is the exposure for an interest rate swap? In such situations our consultants can assist you.  We can sit down with you, discuss your specific business needs and create a customized package combing consultancy services and application subscription services aiming at making the most of the FI METRICS  application capabilities for your portfolio.

The FI METRICS  application considers default occurrences that may result from idiosyncratic (company- or issuer-specific) credit risk factors and systematic credit risk factors. Often, systematic factors, such as the state of the economy or a specific economic sector, that are common across obligors in the same portfolio, may cause the incidence of defaults to be correlated, even though there is no causal link between them, e.g. a recession in the real estate sector leading to several simultaneous credit defaults by construction or real estate development companies. The effects of these common or systematic factors are incorporated into the FI METRICS  application through the use of the correlation (or ‘loading’) parameter which can either be provided as an input by the user or have it quantified automatically by the application itself.

The output of the FI METRICS application comprises of a set of credit risk analytics on the simulated annual loss distribution. These include the Credit Expected Loss, Credit Value-at-Risk (VaR), and Credit Expected Shortfall beyond VaR. At the entire portfolio level as well as at the transaction level. Portfolio Managers can track down ‘weak links’ in their portfolios in terms of risk-based financial performance. The output of the FI METRICS application can be used to determine the level of economic capital required to cover the risk of unexpected credit default losses. Measuring the uncertainty or variability of loss and the relative likelihood of the possible levels of unexpected losses in a portfolio of credit exposures is fundamental to the effective management of credit risk. Economic capital provides a measure of the risk being taken by a firm and has several benefits: it is a more appropriate risk measure than that specified under current regulatory regimes which tend to be less risk sensitive as they typically employ a one-size-fits-all approach; it measures economic risk on a portfolio basis and takes account of diversification and concentration; and, since economic capital reflects the changing risk of a portfolio, it can be used for portfolio management.

Users can also use the application to

  • develop scenario analysis in order to identify the financial impact of low probability but nevertheless plausible credit-default events on selected obligors/issuers/issues;
  • calculate implied default probabilities from credit spreads and develop a dynamic credit risk measurement system – please ask for a free copy of our Greek Bond case study;
  • assess the impact of residual risk as a result of possible decreasing security values;
  • identify High Risk obligors with a significant share in the portfolio’s calculated total unexpected loss; for traded credit risk such information could be used by Portfolio Managers on next portfolio rebalancing for achieving better Sharpe ratios;
  • facilitate the internal capital adequacy assessment process exercises that Risk Managers in banks and investment firms must run at least annually

The methodology underlying the FI METRICS application was developed by Dr M Kyriacou and presented at the annual International Risk Conference held in Luxembourg that was hosted by the European Commission and the European Stability Mechanism (ESM). ESM’s Managing Director along with other prominent regulators, banking professionals and finance academics attended the conference. The proposed methodology was selected for publication to the conference proceedings by the Conference Scientific Committee chaired by a distinguished NYU Stern School professor.