Oct 18

Basel II: Summary Issues & IRB spreadsheet

by David Harper, CFA, FRM, CIPM

FRM |

basel2challenges_Intro

Learning Outcomes (LO 68.18 to 68.20)

  • Explain the negative bank behaviors that may result with the implementation of Basel II.
  • Explain potential problems with the risk analytics of Basel II.
  • Explain issues regarding the implementation of Pillar 2 (Supervision) and Pillar 3 (Market Discipline) of Basel II.

An Excellent summary of Basel II Issues by New York Fed

About 44 learning outcomes in the FRM reference the Basel Accords. The above finish the set. All three refer to an excellent paper by Marc Saidenberg and Til Schuermann of the New York Fed. This is an excellent overview that relates to touches on issues in the wider field of risk measurement.

Negative bank behaviors? (LO 68.18)

There are at three concerns about Basel II:

  • Will Basel II create perverse incentives for banks in emerging markets?
  • Will a ratings-based system exacerbate the procyclicality effect?
  • What will be the observer effect?

The first problem is the irony that an emerging market bank may find its standardized (basic) capital charge to be lower than its internal-ratings based (IRB) charge, which would discourage migration to the more advanced approach. The second problem should be familiar to the FRM candidate; it is not exclusive to Basel and bank lending is already procyclical. Learning Outcome LO 50.5 asks, "Explain how internal ratings models may create a procyclicality effect." In that context, it refers to the weakness of at-the-point-in-time ratings compared to through-the-cycle ratings. As de Servigny nicely shows, procyclicality is a problem because ratings are not forward-looking. They may reinforce credit contraction at the very time the economy enters an expansion:

"...the credit cycle tends to lag the economic cycle. Credit rationing may result as a consequence of the contraction of the lending activity by banks. This will in turn exacerbate economic downturn. " - de Servigny, Measuring and Managing Credit Risk

Risk analytics (LO 68.19)

Analytical issues include:

  • How are we to evaluate the ratings system? It is more difficult to backtest credit ratings than to backtest market risk. Defaults are rare but the bigger issue may be that defaults are not independent: in a recession, they will tend to correlate.
  • How are discrete, chunky probabilities of default mapped to the continuous IRB function?
  • How is loss given default (LGD) measured? The authors give three methods: market LGD (prices of defaulted bonds), workout LGD (discounted cash flow of post-default bond), and implied market (estimates based on observed prices of presumably related distressed bonds). The problem say the authors is, "we seem to know little about what drives the variability in LGD."
  • Is the ASRF framework adequate to the challenge of capturing portfolio-level (imperfectly correlated) credit exposures. More detail here. The short answer is, of course not. It remains an impressive try.
  • Operational risk is difficult to define and measure. Some are looking to the toolkits found in the insurance industry.
  • Criticism of value at risk (VaR): it is a single number as opposed to a more realistic range; it is not coherent.

 

Pillar 2 & Pillar 3 Issues

About the important roles of the supporting pillars:

  • The Second Pillar (supervisory review) is the main "load-bearing column:" supervision is the flexibility that compensates for the mechanical rules of the First Pillar
  • The Third Pillar aims to leverage capital markets, but "in practical terms there are too many limitations in current accounting conventions and disclosure standards for this pillar to be sufficient on its own"

 

IRB Spreadsheet

The EditGrid Spreadsheet below (Select File > Save As... to upload to MS Excel or another format) plots the capital requirement (K) function under Basel II's internal ratings-based (IRB) approach. Risk-weighted assets (RWA) = K() x 12.5 x exposure at default (EAD), where K() is given by the following equation:

base2irb_function

Assuming, for example, an loss given default (LGD) of 50%, K plots a line as below:

image

The spreadsheet breaks this down. Note:

  • rho is a weighted correlation to the so-called systematic risk factor. It lies between 12% and 24%. This is the single-factor credit model that underlies IRB, and it is based on the Merton model for a firm's default
  • phi is the standard normal cumulative distribution; i.e., =NORMSDIST(). The inverse of phi is the inverse of the same; i.e, =NORMSINV()
  • The final multiplier is the maturity multiplier. The spreadsheet follow's the paper's example and assume three year maturity (M=3). b() is a liner regression, as a function of PD(). The formula creates a higher multiplier for lower PDs, which may seem counterintuitive at first glance.
EditGrid Spreadsheet by bt/admin.

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