How Accurate are Value-at-Risk Models at Commercial Banks

By Jeremy Berkowitz, James M. O'Brien. 2001

604 citations recorded by the source corpus.

In recent years, the trading accounts at large commercial banks have grown substantially and become progressively more diverse and complex. We provide descriptive statistics on the trading revenues from such activities and on the associated Value-at-Risk forecasts internally estimated by banks. For a sample of large bank holding companies, we evaluate the performance of banks' trading risk models by examining the statistical accuracy of the VaR forecasts. Although a substantial literature has examined the statistical and economic meaning of Value-at-Risk models, this article is the first to provide a detailed analysis of the performance of models actually in use.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/79bfdf9e13471f38935ff41a9a22bf45c85addf9

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Citations in the corpus

The Level and Quality of Value-at-Risk Disclosure by Commercial Banks (2010, neutral): “10 See Berkowitz and O’Brien (2007) and Jorion (2007) for some empirical evidence a.”
An introduction to market risk measurement (2002, background): “A recent study by Berkowitz and O’Brien (2001) examines the VaR models used by six leading US financial institutions.”
After VaR: The Theory, Estimation, and Insurance Applications of Quantile-Based Risk Measures (2006, neutral): “35 For example, evidence from a number of empirical studies suggests that the VaR models used by financial institutions can be quite inaccurate (see, e.g., Beder, 1995; Marshall and Siegel, 1997; Berkowitz and O’Brien, 2002).”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, neutral): “This complements the results of Berkowitz and O’Brien (2002), as discussed in the introduction.12”
Evaluating Value-at-Risk Models via Quantile Regression (2008, methodology): “Specically, we consider that there is a random coe¢ cient model for Rt, generated in the following way: Rt = 0(Ut) + 1(Ut)Vt (2) = xt (Ut); (3)”
Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk Approach (2014, neutral): “35See, for instance, Berkowitz and O’Brien (2002) for a comparison of GARCH-type VaR and normal VaR.”
Backtesting Parametric Value-at-Risk With Estimation Risk (2010, methodology): “This parametric approach has been the most popular in attempting to describe the dynamics of the VaR measure (cf. Berkowitz and O’Brien 2002).”
Does Systemic Risk in the Financial Sector Predict Future Economic Downturns (2012, background): “3Bernanke (1983b) identified the special role of bank lending in exacerbating the economic declines of the Great Depression, finding that the detrimental effects of bank failures went beyond the bank’s immediate stakeholders (e.”
The Hidden Dangers of Historical Simulation (2006, neutral): “(13)Despite the low power of tests based on VaR exceedances, in Berkowitz and O’Brien’s (2001) study of VaR estimates at 6 commercial banks, they found that VaR exceedances for two of the 6 banks they examined had VaR exceedances whose first order autocorrelations were statistically different from z”
Modelling daily value-at-risk using realized volatility and arch type models (2001, positive): “Lee and Saltoglu (2001), Danielsson (2002) or Berkowitz and O’Brien (2002). Because volatility is a key input to VaR models, the characterization of asset volatility (along with the density distribution of the asset returns) is of paramount importance when implementing and testing VaR models.”