Meeting on Financial Risks
BBVA Auditorium, Paseo de la Castellana, 81, Madrid
of the Conference
Carrillo Menéndez, Director, RiskLab-Madrid.
Ángel Mencía, Director de Metodologías de Riesgo Corporativo, BBVA.
|| Risk Management Lessons from the
Jorion, Professor, The Paul Merage School of Business, University of
California, and Managing Director, Pacific Alternative Asset Management
Risk management, even if
flawlessly executed, does not guarantee that big losses will not occur.
Big losses can occur because of business decisions and bad luck. Even
so, the events of 2007 and 2008 have highlighted serious deficiencies
in risk models. For some firms, risk models failed because of known
unknowns. These include model risk, liquidity risk, and counterparty
risk. In 2008, risk models largely failed due to unknown unknowns,
which include regulatory and structural changes in capital markets.
Risk management systems need to be improved and place a greater
emphasis on stress tests and scenario analysis. In practice, this can
only be based on position-based risk measures that are the basis for
modern risk measurement architecture. Overall, this crisis has
reinforced the importance of risk management.
|| Practical Modelling of the Incremental Risk Charge (IRC) in the Trading Book
Dan Rosen, CEO, R2 Financial Technologies, and Visiting fellow, Fields Institute.
Revisions to the Basel II market risk framework, finalized in July 2009, require that banks
develop a methodology for calculating a new incremental risk charge (IRC). The IRC is defined
to capture credit default and migration risks that are incremental to the risks captured by the
market VaR calculation of the bank's trading book positions on unsecuritised credit products.
IRC is based on the assumption of a constant level of risk over the one-year capital horizon.
This assumption implies that exposures are rolled-over when they mature and positions whose
credit characteristics have improved or deteriorated over the liquidity horizon are replaced
with positions that have credit characteristics equivalent to those at the start of the liquidity
In this talk, we discuss the basic principles behind the IRC requirements. We present a robust
methodology to compute IRC and discuss various modelling choices, which arise in practical
implementations. The methodology combines the repeated application of single-step credit portfolio
models with advanced convolution methods to model the constant level of risk principle. In contrast
to a brute-force dynamic multi-step simulation, the methodology is transparent and does not require
heavy parameterization or operational assumptions. It further leverages existing credit portfolio tools,
already used by banks. We present a practical example and further discuss how the approach can be used
as a comprehensive stress testing tool, which allows us to understand the components of credit risk,
the major risk contributors, and the capital benefits from long-short positions and diversification.
Finally, we discuss the application to portfolios which include tranches and baskets (correlation trading).
|| Does history repeat? Credit stress scenarios
using sub-sampling in a multi-factor portfolio model
Bonti, Director, Department of Risk Management & Control,
Deutsche Pfandbriefbank AG.
Standard portfolio models for
risk capital estimation introduce randomness via systematic and
specific risk factors. A systematic risk factor influences the credit
worthiness of many borrowers. For example, future developments in an
industry branch. The models usually assign the same probability for an
improvement as for a deterioration of current conditions. In
mathematical language this translates into a symmetric probability
distribution having an expected value that implies no changes in the
What if one expects quite negative/positive developments in a certain
industry branch or country? This question is important for portfolio
management. The goal is to estimate changes in risk measures like
expected loss or risk capital as a consequence of (stress) scenarios
representing anticipated trends in systematic risk factors. In the
context of a multi-factor model, the proposed approach can also
incorporate forecasts for macro-economic variables in the scenario
design. Resulting changes in dependence measures (like e.g.
correlations) are taken into account.
|| The compensation for risk in
Ericsson, Associate Professor of Finance, Desautels Faculty of
Management, McGill University, and Research Affiliate, SIFR.
In light of the recent turmoil
in credit markets, it has been argued that investors placed exaggerated
emphasis on expected losses as measured by credit ratings. However,
selecting an investment based solely on a default probability and a
yield can be misleading. Two firms with the same default probability
can justify very different yield spreads as a result of their
systematic risk. For the same reason, two firms with the same spread
can have very different default probabilities. To properly assess an
investment in credit sensitive fixed income securities, it is necessary
to separately measure the expected loss and risk premium components of
In this talk I will address risk
premia in both corporate bond and credit derivative markets, drawing on
several recent papers. In the first part, I show that risk premia in
equity and bond markets may superficially appear difficult to
reconcile, sometimes, counter intuitively, moving in opposite
directions. I argue that in fact they behave as theory predicts when
adjusting for movements in leverage, volatility and bond contractuals.
The risk premium metric I develop helps improve the explanatory power
of credit spread regressions, in particular, usefully, for high grade
In a second step I show that
systematic risk impacts default swaps and stock options through the
same channel. Equity puts and credit default swaps are economically
similar contracts - they insure against adverse shocks to a firm's
value. I find that puts and credit default swaps for firms with greater
systematic risk are more expensive all else equal. They are both
similarly influenced by the proportion of systematic volatility for a
firm's stock. Traditional option and credit risk models predict that
the contemporaneous relationship between a derivative's price and the
underlying is independent of systematic risk. Our findings contradict
|| Lévy VaR with ALaM
le Courtois, Associate Professor of Finance and Insurance, EM Lyon
The Basle Capital Accord issued
from the Basle Committee on Banking supervision has imposed a
multiplier between 3 and 4 on the bank's internal 10-day Value-at-Risk
calculated for market risk exposure. This ad hoc factor has not been
fully explained and is poorly justified by arguing that the standard
classical models of stock price dynamics do not adequately capture
actual market risks. In this paper, we tackle this issue by adopting a
probabilistic framework based on Lévy processes with both
finite and infinite activity, but with finite variation. We accommodate
bank regulator concerns with several types of Lévy processes
which allow to exhibit a factor 3 between Brownian-VaR and
Lévy-VaR, and we show that the Lévy-VaR is more
consistent with the reality of market risk. On the technical side our
paper provides a new general Fourier formula that allows to compute VaR
quickly and efficiently as soon as the characteristic function of the
process under study is known, so for a wide class of stochastic
processes. Our study can be of interest for the computation of Solvency
Capital Requirements in the context of the preparation of Solvency 2.
|| Basis estocástico en un modelo HJM consistente con la construcción de curvas
Teresa Martínez, Quantitative Product Group, Grupo Santander.
Consideraremos un modelo HJM general para describir la evolución de las curvas de descuento y
estimación (en una y varias monedas), bajo la probabilidad de riesgo neutro asociada a la curva
de factores de descuento de la economía doméstica. Encontraremos condiciones sobre el
término de deriva de las ecuaciones que permitan al modelo ser consistente con la práctica
de mercado para construir las curvas.
|| Mesa redonda: Gestión
de riesgos en tiempos revueltos
Juan Carlos García Céspedes, Director de
Gestión Global del Riesgo, BBVA.
Didac Artés, CEO, Director General, Próxima Alfa.
Javier Martín-Artajo, Chief Investment Officer EEMEA, Chief Investment Office, JP Morgan.
Ángel Sánchez Aristi, BBVA, Managing Director, Global Markets South America & USA
Luis Seco, Professor, University of Toronto.
Santiago Carrillo Menéndez and Antonio Sánchez
Calle (RiskLab-Madrid) and
Luis Seco (RiskLab-Toronto).