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e.g. Bank of America VaR(99%, 1 day) on December 31, 2008 was $140 mio. Losses. Gains ... Bank of America (US) Credit Suisse First Boston (Switzerland) ... – PowerPoint PPT presentation

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Title: 1


1
The Level and Quality of Value-at-Risk Disclosure
by Commercial Banks Christophe Pérignon, HEC
Paris Daniel R. Smith, Simon Fraser
University Risk Management in Financial
Institutions 23-25 April 2009
2
  • Introduction
  • Disclosure of quantitative measures of market
    risk, such as value-at-risk is enlightening only
    when accompanied by a thorough discussion of how
    the risk measures were calculated and how they
    related to actual performance
  • Alan Greenspan

3
VaR is a quantile of the trading revenue (PL)
distribution
e.g. Bank of America VaR(99, 1 day) on December
31, 2008 was 140 mio
1-day ahead P/L distribution
Losses
Gains
VaR(99, 1 day)
4
VaR is a quantile of the trading revenue (PL)
distribution
Increase in Volatility
Losses
Gains
VaR
VaR
5
  • Our Objective
  • Joint Assessment of
  • Level Quality
  • of VaR disclosure by commercial banks
  • When quantity and quality are both at acceptable
    levels ? Reduction in information asymmetry

6
  • Our Contributions
  • Survey of actual VaR disclosures in the world
  • Formal test of whether daily VaRs are
  • Accurate Lead to the correct number of
    exceptions
  • Informative Forecast next-day volatility of
    trading revenues

7
  • Part 1 Level of VaR Disclosure
  • VaR Disclosure Index (VaRDI)
  • Sample 10 largest US commercial banks, 6 largest
    Canadian commercial banks, and top-50
    international commercial banks
  • Sample Period Entire post-1996 Market Risk
    Amendment period for US and Canadian banks, and
    year 2005 for top-50 international banks
  • Data Source Annual reports

8
  • VaR Disclosure Index (VaRDI)
  • 1. VaR Characteristics
  • a. Score of 1 if Holding Period (e.g. 1 day,
    1 month)
  • b. Score of 1 if Confidence Level (e.g. 99,
    95)
  • 2. Summary VaR Statistics
  • a. Score of 1 if High, Low, or Average VaR
  • b. Score of 1 if Year-End VaR
  • c. Score of 1 if VaR by Risk Category (e.g.
    Currency, Fixed Income, Equity)
  • d. Score of 1 if Diversification Effect is
    accounted for
  • 3. Intertemporal Comparison
  • a. Score of 1 if Summary Information about
    the Previous Year VaR
  • 4. Daily VaR Figures
  • a. Score of 1 if Histogram of Daily VaRs, or
    score of 2 if Plot of Daily VaRs
  • 5. Trading Revenues
  • a. Score of 1 if Hypothetical Revenues
  • b. Score of 1 if Revenues without Trading
    Fees
  • c. Score of 1 if Histogram of Daily
    Revenues, or score of 2 if Plot of Daily
  • Revenues

9
  • VaRDI for 10 Largest US Banks (1/2)

10
  • VaRDI for 10 Largest US Banks (2/2)

11
  • VaRDI in the U.S. and in Canada (1996-2005)

12
  • VaRDI in the World (2005)

13
  • VaRDI by Country (2005)

14
  • VaR Methods Currently Used by Banks (2005)

15
  • Historical Simulation (HS)
  • HS method based on the one-year unconditional
    distribution of the risk factors
  • HS-based VaRs are under-responsive to changes in
    conditional risk
  • Mechanical disconnection between 1-day VaR and
    actual volatility on the next day
  • True in practice?

16
  • Part 2 Quality of VaR Disclosure
  • Backtesting
  • Forecast Trading Revenue Volatility

17
  • Part 2a Backtesting

18
  • Part 2b Forecasting Trading Revenue Volatility
  • For each country included in our survey, we look
    for a bank disclosing a graph of daily VaR and
    trading revenues over a long enough sample period
  • Bank of America (US)
  • Credit Suisse First Boston (Switzerland)
  • Deutsche Bank (Germany)
  • Royal Bank of Canada (Canada)
  • Société Générale (France)
  • All banks have VaRDIs of at least 13 points
  • They all use Historical Simulation, except
    Deutsche Bank

19
  • Data Extraction
  • Actual VaRs and trading revenues are not
    available in a machine-readable format
  • We extract the data from the graphs included in
    annual reports using a Matlab-based technique
  • Convert PDF file into JPG
  • Import JPG file into Matlab
  • Add vertical lines on the image
  • Zoom in and click on each data point
  • Convert Matlab coordinates into graph coordinates
  • Many experiments and simulations are presented in
    the appendix

20
Bank of America Credit Suisse First
Boston Deutsche Bank Royal Bank of
Canada Societé Générale
The Bank of America
21
  • Measuring VaR Accuracy
  • We look for a statistical link between VaRt1t
    and the volatility of the trading revenue Rt1
  • We use a simple GARCH model as a benchmark
  • Unfair horse race risk manager knows the
    banks positions, unlike the econometrician

22
  • Methodology
  • In-Sample Augmented GARCH
  • Out-of-Sample Regression

23
(No Transcript)
24
  • Conclusion (Part I)
  • We use public information about VaR disclosed by
    banks in their annual reports
  • Large differences in the level of disclosure
    among US commercial banks
  • Upward trend in the amount of information
    disclosed to the public
  • US VaR disclosures are below average
  • Large differences in the level of disclosure
    across countries
  • Historical Simulation is the most popular VaR
    method

25
  • Conclusion (Part II)
  • Unlike the level of disclosure, the quality of
    VaR shows no sign of improvement over time
  • Disconnection between 1-day VaR and actual
    volatility on the next day
  • HS-based VaR helps little in forecasting the
    volatility of future trading revenues
  • Its incremental forecasting ability over a simple
    GARCH model is very limited
  • Only exception Deutsche Bank

26
The Level and Quality of Value-at-Risk Disclosure
by Commercial Banks Christophe Pérignon, HEC
Paris Daniel R. Smith, Simon Fraser
University Risk Management in Financial
Institutions 23-25 April 2009
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