Bertrand K. Hassani

Multivariate VaRs for operational risk capital computation: a vine structure approach

The Basel Advanced Measurement Approach requires financial institutions to compute capital requirements on internal data sets. In this paper we introduce a new methodology permitting capital requirements to be linked with operational risks. The data are arranged in a matrix of 56 cells. Constructing a vine architecture, which is a bivariate decomposition of a n-dimensional structure (n > 2), we present a novel approach to compute multivariate operational risk VaRs. We discuss multivariate results regarding the impact of the dependence structure on the one hand, and of LDF modeling on the other. Our method is simple to carry out, easy to interpret and complies with the new Basel Committee requirements.

A modified Panjer algorithm for operational risk capital calculations

Operational risk management inside banks and insurance companies is an important task. The computation of a risk measure associated to these kinds of risks lies in the knowledge of the so-called loss distribution function (LDF). Traditionally, this LDF is computed via Monte Carlo simulations or using the Panjer recursion, which is an iterative algorithm. In this paper, we propose an adaptation of this last algorithm in order to improve the computation of convolutions between Panjer class distributions and continuous distributions, by mixing the Monte Carlo method, a progressive kernel lattice and the Panjer recursion. This new hybrid algorithm does not face the traditional drawbacks. This simple approach enables us to drastically reduce the variance of the estimated value-at-risk associated with the operational risks and to lower the aliasing error we would have using Panjer recursion itself. Furthermore, this method is much less timeconsuming than a Monte Carlo simulation. We compare our new method with more sophisticated approaches already developed in operational risk literature.

Distortion Risk Measures or the Transformation of Unimodal Distributions into Multimodal Functions

The particular subject of this paper, is to construct a general framework that can consider and analyse in the same time upside and downside risks. This paper offers a comparative analysis of concept risk measures, we focus on quantile based risk measure (ES and VaR), spectral risk measure and distortion risk measure. After introducing each measure, we investigate their interest and limit. Knowing that quantile based risk measure cannot capture correctly the risk aversion of risk manager and spectral risk measure can be inconsistent to risk aversion, we propose and develop a new distortion risk measure extending the work of Wang (2000) [38] and Sereda et al (2010) [34]. Finally, we provide a comprehensive analysis of the feasibility of this approach using the S&P500 data set from o1/01/1999 to 31/12/2011.

Stress Testing Engineering: The Real Risk Measurement?

Stress testing is used to determine the stability or the resilience of a given financial institution by deliberately submitting. In this paper, we focus on what may lead a bank to fail and how its resilience can be measured. Two families of triggers are analysed: the first stands in the stands in the impact of external (and / or extreme) events, the second one stands on the impacts of the choice of inadequate models for predictions or risks measurement; more precisely on models becoming inadequate with time because of not being sufficiently flexible to adapt themselves to dynamical changes.

Should the Advanced Measurement Approach Be Replaced with the Standardized Measurement Approach for Operational Risk

Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London.

Artificial Neural Network to Serve Scenario Analysis Purposes

In this chapter we introduce how artificial neural networks could be used for scenario analysis purposes. Here we present the main theoretical features, illustrate a simple deep learning strategy applied to cyber security and present a application to assess the risk of bankruptcy of a target entity. Pros and cons of the use of artificial neural networks are discussed in details.

Dependencies and Relationships Between Variables

In this chapter we address the topic of the capture of dependencies, as these are intrinsically connected to scenario analysis. We present different ways of capturing them, either using various correlation coefficients, implementing a regression or relying on copulas. A last section provides some elements of discussion.

The Consensus Approach

In this chapter we will present the so-called consensus approach in which the scenarios are analysed in a workshop and a decision is made if a consensus is reached. We discuss the whys and wherefores of the strategy presenting the main advantages and limitations of the approach. We also provide solutions to overcome the main issues.

Forward-Looking Underlying Information: Working with Time Series

In this chapter we rely on time series processes to perform scenario analysis. We present the theoretical foundations, the main properties of models which could be implemented and the methodologies to adjust them. For illustration purposes, we apply these methodologies to real data.

Leveraging Extreme Value Theory

In this chapter we propose to work within the extreme value theory framework. We suggest implementing a “block maxima” strategy to fit a generalised extreme value distribution on periodic maxima provided by risk managers. Here, we rely on risk managers expertise to assess the risks, and we transform the heterogeneous information provided into a homogenous risk evaluation.