Dynamics of Trust in Networks and Systemic Risk
Joao da Gama Batista (Ecole Centrale Paris, France)
Joint work with Jean-Philippe Bouchaud and Damien Challet

Wednesday June 4, 16:30-17:00 | session 6.6 | Risk Management | room L

The feeling of confidence or trust is widely considered a crucial ”macro” variable to describe the dynamics of the financial markets. It is driven both by objective information (e.g.: the state of the economy) and interaction between the agents. Moreover, it is highly sensitive to expectations about what other people expect, which is one of the fundamental feedback loops for self-fulfilling prophecies. Sudden evaporation of trust tends to reinforce financial crises, for instance by causing liquidity breakdowns. In contrast to more static approaches, we model both the creation and destruction of the network over time, with a mutual feedback between one’s links to one’s trustworthiness. The dynamics of this model are such that losing trust is a faster phenomena than gaining trust, triggering panic avalanches which cause the sudden disintegration of the network for specific parameter values.

Economic Capital Modeling - closed form approximation for real-time applications
Thomas Ribarits (European Investment Bank, Luxembourg)
Joint work with Heikki Seppälä, Jenny Bai Hua, Ser-Huang Poon and Axel Clement

Wednesday June 4, 17:00-17:30 | session 6.6 | Risk Management | room L

Economic capital (ECap) modeling is a fundamental part of Pillar II of the Basel regulatory framework. Indeed, “sophisticated” financial institutions need to have in place internal models for the assessment of the level of the overall capital buffer which is deemed sufficient to cover the risk of their business activities. On top, ECap models are also frequently used for pricing purposes on an ex-ante basis: financial institutions need to know the incremental economic capital (IECap), i.e. the size by which the overall capital buffer needs to be increased after addition of e.g. a single new loan to the existing portfolio. This is important in order to be able to price such additional loan accordingly. Finally, ECap contributions (ECapC) are also required ex-post in order to break down the overall capital buffer to the individual obligors, products etc. within the portfolio. Simulation of IECap and ECapC can be computationally expensive and unstable, but it appears that closed form approximations provide accurate, consistent and quick solutions in many cases. The formula introduced in this paper is based on the multi-factor approximation from [Pykhtin,2004] applicable to a default-mode Merton type model. As such, correlations between obligors (stemming from a multi-factor-model) are taken into account, but the formula also captures amortization schedules for each individual position - a feature which is of practical relevance, but often neglected in standard default-mode models. For the time being, credit-risk mitigants such as the existence of guarantors on individual loans, are not captured by the formula, neither are credit migrations or correlations between default probabilities and loss given defaults. Our formula allows for approximation of all ECap contributions without extra computational cost. After calculation of the ECap of the original portfolio, IECap can be computed within few seconds and more accurately than in standard linear approximations based on ECap contributions. Disclaimer: The views and opinions expressed in this paper are those of the authors and do not necessarily reflect those of the European Investment Bank or the European Investment Bank Institute. All figures shown are based on purely hypothetical test portfolios.

Assessing Financial Model Risk
Giacomo Scandolo (University of Verona, Italy)
Joint work with Pauline Barrieu

Wednesday June 4, 17:30-18:00 | session 6.6 | Risk Management | room L

Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.