Christophe Perignon

HEC Paris

France

Personnal website:
Other affiliations:

Christophe Perignon created these companion sites

The Risk Map: A New Tool for Validating Risk Models
Abstract
This paper presents a new tool for validating risk models. This tool, called the Risk Map, jointly accounts for the number and the magnitude of extreme losses and graphically summarizes all information about the performance of a risk model. It relies on the concept of Value-at-Risk (VaR) super exception, which is defined as a situation in which the loss exceeds both the standard VaR and a VaR defined at an extremely low coverage probability. We then formally test whether the sequences of exceptions and super exceptions is rejected by standard model validation tests. We show that the Risk Map can be used to validate market, credit, operational, or systemic (e.g. CoVaR) risk estimates or to assess the performance of the margin system of a clearing house.
Colletaz, G., C. Hurlin, and C. Perignon, "The Risk Map: A New Tool for Validating Risk Models", SSRN.
Authors: Colletaz
Hurlin
Perignon
Coders: Colletaz
Hurlin
Perignon
Last update
Thu Jul 25 05:01:00 CEST 2013
Ranking
51
Runs
146
Visits
431
A New Approach to Comparing VaR Estimation Methods
Abstract
We develop a novel backtesting framework based on multidimensional Value-at-Risk (VaR) that focuses on the left tail of the distribution of the bank trading revenues. Our coverage test is a multivariate generalization of the unconditional test of Kupiec (Journal of Derivatives, 1995). Applying our method to actual daily bank trading revenues, we find that non-parametric VaR methods, such as GARCH-based methods or filtered Historical Simulation, work best for bank trading revenues.
Perignon, C., and D. Smith, C. Hurlin, "A New Approach to Comparing VaR Estimation Methods", Journal of Derivatives , 15, 54-66.
Authors: Perignon
Smith
Coders: Perignon
Smith
Hurlin
Last update
Mon Jul 16 10:41:00 CEST 2012
Ranking
54
Runs
11
Visits
270
Backtesting Value-at-Risk: A Duration-Based Approach
Abstract
Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision. However, existing backtesting methods have relatively low power in realistic small sample settings. Our contribution is the exploration of new tools for backtesting based on the duration of days between the violations of the Value-at-Risk. Our Monte Carlo results show that in realistic situations, the new duration-based tests have considerably better power properties than the previously suggested tests.
Hurlin, C., and C. Perignon, "Backtesting Value-at-Risk: A Duration-Based Approach", Journal of Financial Econometrics, 2, 84-108.
Authors: Pelletier
Christoffersen
Coders: Hurlin
Perignon
Last update
Mon Jul 23 05:33:00 CEST 2012
Ranking
26
Runs
17
Visits
207
Evaluating Interval Forecasts
Abstract
A complete theory for evaluating interval forecasts has not been worked out to date. Most of the literature implicitly assumes homoskedastic errors even when this is clearly violated and proceed by merely testing for correct unconditional coverage. Consequently, the author sets out to build a consistent framework for conditional interval forecast evaluation, which is crucial when higher-order moment dynamics are present. The new methodology is demonstrated in an application to the exchange rate forecasting procedures advocated in risk management.
Hurlin, C., C. Perignon, "Evaluating Interval Forecasts", International Economic Review, 39, 841-862.
Authors: Christoffersen
Coders: Hurlin
Perignon
Last update
Fri Mar 09 03:40:00 CET 2012
Ranking
32
Runs
57
Visits
167
The Best of Both Worlds: A Hybrid Approach to Calculating Value at Risk
Abstract
The hybrid approach combines the two most popular approaches to VaR estimation: RiskMetrics and Historical Simulation. It estimates the VaR of a portfolio by applying exponentially declining weights to past returns and then finding the appropriate percentile of this time-weighted empirical distribution. This new approach is very simple to implement. Empirical tests show a significant improvement in the precision of VaR forecasts using the hybrid approach relative to these popular approaches.
Hurlin, C., C. Perignon, "The Best of Both Worlds: A Hybrid Approach to Calculating Value at Risk", Risk, 1, 64-67.
Authors: Boudoukh
Richardson
Whitelaw
Coders: Hurlin
Perignon
Last update
Tue Jul 17 07:54:00 CEST 2012
Ranking
52
Runs
4
Visits
67
Diversification and Value-at-Risk
Abstract
A pervasive and puzzling feature of banks’ Value-at-Risk (VaR) is its abnormally high level, which leads to excessive regulatory capital. A possible explanation for the tendency of commercial banks to overstate their VaR is that they incompletely account for the diversification effect among broad risk categories (e.g., equity, interest rate, commodity, credit spread, and foreign exchange). By underestimating the diversification effect, bank’s proprietary VaR models produce overly prudent market risk assessments. In this paper, we examine empirically the validity of this hypothesis using actual VaR data from major US commercial banks. In contrast to the VaR diversification hypothesis, we find that US banks show no sign of systematic underestimation of the diversification effect. In particular, diversification effects used by banks is very close to (and quite often larger than) our empirical diversification estimates. A direct implication of this finding is that individual VaRs for each broad risk category, just like aggregate VaRs, are biased risk assessments.
Perignon, C., and D. Smith, "Diversification and Value-at-Risk", Journal of Banking and Finance, 34.
Authors: Perignon
Smith
Coders: Perignon
Smith
Last update
Fri Nov 23 08:29:00 CET 2012
Ranking
9999
Runs
N.A.
Visits
45
A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR
Abstract
In this paper, we propose a theoretical and empirical comparison of two popular systemic risk measures - Marginal Expected Shortfall (MES) and Delta Conditional Value at Risk (ΔCoVaR) - that can be estimated using publicly available data. First, we assume that the time-varying correlation completely captures the dependence between firm and market returns. Under this assumption, we derive three analytical results: (i) we show that the MES corresponds to the product of the conditional ES of market returns and the time-varying beta of this institution, (ii) we give an analytical expression of the ΔCoVaR and show that the CoVaR corresponds to the product of the VaR of the firm's returns and the time-varying linear projection coefficient of the market returns on the firm's returns and (iii) we derive the ratio of the MES to the ΔCoVaR. Second, we relax this assumption and propose an empirical comparison for a panel of 61 US financial institutions over the period from January 2000 to December 2010. For each measure, we propose a cross-sectional analysis, a time-series comparison and rankings analysis of these institutions based on the two measures.
Benoit, S., G. Colletaz, C. Hurlin, and C. Perignon, "A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR", SSRN.
Authors: Benoit
Colletaz
Hurlin
Perignon
Coders: Benoit
Colletaz
Hurlin
Perignon
Last update
Thu Oct 25 11:35:00 CEST 2012
Ranking
53
Runs
181
Visits
398
Value-at-Risk (Chapter 7: Portfolio Risk - Analytical Methods)
Abstract
Book description: To accommodate sweeping global economic changes, the risk management field has evolved substantially since the first edition of Value at Risk, making this revised edition a must. Updates include a new chapter on liquidity risk, information on the latest risk instruments and the expanded derivatives market, recent developments in Monte Carlo methods, and more. Value at Risk, Second Edition, will help professional risk managers understand, and operate within, today’s dynamic new risk environment.
Hurlin, C., C. Perignon, "Value-at-Risk (Chapter 7: Portfolio Risk - Analytical Methods)", McGraw-Hill, Second edition.
Authors: Jorion
Coders: Hurlin
Perignon
Last update
Fri Mar 16 04:09:00 CET 2012
Ranking
33
Runs
9
Visits
303
Techniques for Verifying the Accuracy of Risk Management Models
Abstract
Risk exposures are typically quantified in terms of a "Value at Risk" (VaR) estimate. A VaR estimate corresponds to a specific critical value of a portfolio's potential one-day profit and loss probability distribution. Given their function both as internal risk management tools and as potential regulatory measures of risk exposure, it is important to quantify the accuracy of an institution's VaR estimates. This study shows that the formal statistical procedures that would typically be used in performance-based VaR verification tests require large samples to produce a reliable assessment of a model's accuracy in predicting the size and likelihood of very low probability events. Verification test statistics based on historical trading profits and losses have very poor power in small samples, so it does not appear possible for a bank or its supervisor to verify the accuracy of a VaR estimate unless many years of performance data are available. Historical simulation-based verification test statistics also require long samples to generate accurate results: Estimates of 0.01 critical values exhibit substantial errors even in samples as large as ten years of daily data.
Hurlin, C., C. Perignon, "Techniques for Verifying the Accuracy of Risk Management Models", Journal of Derivatives, 3, 73-84.
Authors: Kupiec
Coders: Hurlin
Perignon
Last update
Tue Apr 17 02:14:00 CEST 2012
Ranking
57
Runs
26
Visits
339
The pernicious effects of contaminated data in risk management
Abstract
Banks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks’ own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profit-and-loss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (market-risk induced) regulatory capital.
Fresard, L., C. Perignon, and A. Wilhelmsson, "The pernicious effects of contaminated data in risk management", Journal of Banking and Finance, 35.
Authors: Fresard
Perignon
Wilhelmsson
Coders: Fresard
Perignon
Wilhelmsson
Last update
Fri Nov 23 08:31:00 CET 2012
Ranking
9999
Runs
N.A.
Visits
42
The level and quality of Value-at-Risk disclosure by commercial banks
Abstract
In this paper we study both the level of Value-at-Risk (VaR) disclosure and the accuracy of the disclosed VaR figures for a sample of US and international commercial banks. To measure the level of VaR disclosures, we develop a VaR Disclosure Index that captures many different facets of market risk disclosure. Using panel data over the period 1996–2005, we find an overall upward trend in the quantity of information released to the public. We also find that Historical Simulation is by far the most popular VaR method. We assess the accuracy of VaR figures by studying the number of VaR exceedances and whether actual daily VaRs contain information about the volatility of subsequent trading revenues. Unlike the level of VaR disclosure, the quality of VaR disclosure shows no sign of improvement over time. We find that VaR computed using Historical Simulation contains very little information about future volatility.
Perignon, C., and D. Smith, "The level and quality of Value-at-Risk disclosure by commercial banks", Journal of Banking and Finance, 34.
Authors: Perignon
Smith
Coders: Perignon
Smith
Last update
Fri Nov 23 08:30:00 CET 2012
Ranking
9999
Runs
N.A.
Visits
25
A New Approach to Comparing VaR Estimation Methods
Abstract
We develop a novel backtesting framework based on multidimensional Value-at-Risk (VaR) that focuses on the left tail of the distribution of the bank trading revenues. Our coverage test is a multivariate generalization of the unconditional test of Kupiec (Journal of Derivatives, 1995). Applying our method to actual daily bank trading revenues, we find that non-parametric VaR methods, such as GARCH-based methods or filtered Historical Simulation, work best for bank trading revenues.
Perignon, C., and D. Smith, "A New Approach to Comparing VaR Estimation Methods", Journal of Derivatives, Winter.
Authors: Smith
Perignon
Coders: Perignon
Smith
Last update
Fri Nov 23 08:30:00 CET 2012
Ranking
9999
Runs
N.A.
Visits
43
Margin Backtesting
Abstract
This paper presents a validation framework for collateral requirements or margins on a derivatives exchange. It can be used by investors, risk managers, and regulators to check the accuracy of a margining system. The statistical tests presented in this study are based either on the number, frequency, magnitude, or timing of margin exceedances, which are de…ned as situations in which the trading loss of a market participant exceeds his or her margin. We also propose an original way to validate globally the margining system by aggregating individual backtesting statistics ob- tained for each market participant.
Hurlin, C., and C. Perignon, "Margin Backtesting", University of Orleans, HEC Paris.
Authors: Hurlin
Perignon
Coders: Hurlin
Perignon
Last update
Wed Jul 23 01:26:00 CEST 2014
Ranking
36
Runs
377
Visits
433
Value-at-Risk (Chapter 5: Computing VaR)
Abstract
Book description: To accommodate sweeping global economic changes, the risk management field has evolved substantially since the first edition of Value at Risk, making this revised edition a must. Updates include a new chapter on liquidity risk, information on the latest risk instruments and the expanded derivatives market, recent developments in Monte Carlo methods, and more. Value at Risk will help professional risk managers understand, and operate within, today’s dynamic new risk environment.
Hurlin, C., C. Perignon, "Value-at-Risk (Chapter 5: Computing VaR)", MacGraw-Hill, Third Edition.
Authors: Jorion
Coders: Hurlin
Perignon
Last update
Mon Mar 19 09:33:00 CET 2012
Ranking
44
Runs
63
Visits
328