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Does Basel II Pillar 3 Risk Exposure Data help to Identify Risky Banks?

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  • Ralf Sabiwalsky

Abstract

Basel II Pillar 3 reports provide information about banks' exposure towards a number of risk factors, such as corporate credit risk and interest rate risk. Previous studies nd that the quality of such information is likely to be weak. We analyze the marginal contribution of pillar 3 exposure data to the quality of equity volatility forecasts for individual banks. Our method uses (local in time) measures of risk factor risk using a multivariate stochastic volatility model for ve risk factors, and uses measures of bank sensitivity with respect to these risk factors. We use two sets of sensitivity measures. One takes into account pillar 3 information, and the other one does not. Generally, we generate volatility forecasts as if no market prices of equity were available for the bank the forecast is made for. We do this for banks for which such data is, in fact, available so that we can conduct ex post - tests of the quality of volatility forecasts. We nd that (1) pillar 3 information allows for a better-than-random ranking of banks according to their risk, but (2) pillar 3 exposure data does not help reduce volatility forecast error magnitude.

Suggested Citation

  • Ralf Sabiwalsky, 2012. "Does Basel II Pillar 3 Risk Exposure Data help to Identify Risky Banks?," SFB 649 Discussion Papers SFB649DP2012-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2012-008
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    References listed on IDEAS

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    More about this item

    Keywords

    Risk Reporting; Stochastic Volatility; Risk Factors;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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