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19 results

  • 1 - Common Elements in Validation of Risk Models Used in Financial Institutions

  • Edited by David Lynch, Federal Reserve Board of Governors, Iftekhar Hasan, Fordham University Graduate Schools of Business, Akhtar Siddique, Office of the Comptroller of the Currency
  • Book:
    Validation of Risk Management Models for Financial Institutions
    Published online:
    02 March 2023
    Print publication:
    09 March 2023, pp 1-21
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  • Summary

    This chapter provides a unified discussion of the framework for model validation. It describes how model validation developed over time across various disciplines. It then describes the various approaches that are applied for validation of risk management models at financial institutions.

Validation of Risk Management Models for Financial Institutions
  • Theory and Practice
  • Edited by David Lynch, Iftekhar Hasan, Akhtar Siddique
  • Published online:
    02 March 2023
    Print publication:
    09 March 2023
  • Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007–2011. Since this crisis, there has been an increase in the amount of scrutiny and testing applied to such models, and validation has become an essential part of model risk management at financial institutions. The book covers all of the major risk areas that a financial institution is exposed to and uses models for, including market risk, interest rate risk, retail credit risk, wholesale credit risk, compliance risk, and investment management. The book discusses current practices and pitfalls that model risk users need to be aware of and identifies areas where validation can be advanced in the future. This provides the first unified framework for validating risk management models.

  • Autoregressive Conditional Skewness

  • Campbell R. Harvey, Akhtar Siddique
  • Journal:
    Journal of Financial and Quantitative Analysis / Volume 34 / Issue 4 / December 1999
    Published online by Cambridge University Press:
    06 April 2009, pp. 465-487
    Print publication:
    December 1999

  • We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.