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An empirical investigation of the quality of value‐at‐risk disclosure in Australia

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  • Angus Campbell
  • Daniel R. Smith

Abstract

We study the level and quality of value‐at‐risk (VaR) disclosure at Australian banks. We find that Australian banks have increased disclosure about their VaR recently, reaching a level post‐crisis that is similar to other regulatory jurisdictions. We find that the actual VaR estimates produced by banks are generally rejected by standard backtesting procedures. During quiet periods bank VaRs are too high, while during high volatility stress periods bank VaRs are too low. We are able to reject the null hypothesis that the daily VaRs for two banks are the 1st percentile using a quantile regression‐based test.

Suggested Citation

  • Angus Campbell & Daniel R. Smith, 2022. "An empirical investigation of the quality of value‐at‐risk disclosure in Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 469-491, March.
    • Handle: RePEc:bla:acctfi:v:62:y:2022:i:1:p:469-491
    DOI: 10.1111/acfi.12795
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    1. Beverly Hirtle, 2016. "Public disclosure and risk-adjusted performance at bank holding companies," Economic Policy Review, Federal Reserve Bank of New York, issue Aug, pages 151-173.
    2. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    3. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value‐at‐Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, June.
    4. David E Allen & Robert Powell, 2012. "The fluctuating default risk of Australian banks," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 297-325, August.
    5. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    7. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    8. David E. Allen & Robert Powell, 2009. "Transitional credit modelling and its relationship to market value at risk: an Australian sectoral perspective," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(3), pages 425-444, September.
    9. Colleen Cassidy & Marianne Gizycki, 1997. "Measuring Traded Market Risk: Value-at-risk and Backtesting Techniques," RBA Research Discussion Papers rdp9708, Reserve Bank of Australia.
    10. Pérignon, Christophe & Deng, Zi Yin & Wang, Zhi Jun, 2008. "Do banks overstate their Value-at-Risk?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 783-794, May.
    11. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Millicent Chang & Andrew B. Jackson & Marvin Wee, 2018. "A review of research on regulation changes in the Asia‐Pacific region," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(3), pages 635-667, September.
    14. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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