Meeting on Financial Risks
Thursday, May 29th, 2014
BBVA Auditorium, Paseo de la Castellana, 81, Madrid
of the Conference
Carrillo Menéndez, Director, RiskLab-Madrid.
Lorenzo de Cristóbal, Head of the Global Markets Risk Unit, BBVA.
|| Modeling and Monitoring Risk Acceptability in Markets: The Case of the Credit Default Swap Market
Dilip Madan, Professor of Finance at the Robert H. Smith School of Business, University of Maryland.
Regulators Minimal discounted distorted expectations across a range of stress levels are employed to model risk acceptability in markets. Interactions between discounting and stress levels used in measure changes are accommodated by lowering discount rates for the higher stress levels. Acceptability parameters represent a maximal and minimal discount rate, a maximal stress level and the speed of rate reduction in response to stress. An explicit model relating credit default swap (CDS) prices to default probabilities is formulated with a view to making the default risk market acceptable. Data on CDS prices and default probabilities for the six major US banks obtained from the Risk Management Institute of the National University of Singapore is employed to estimate parameters defining acceptability and the movements in market implied recovery rates. We observe that the financial crisis saw an increase in the maximal discount rate and its spread over the minimal rate along with an increase in the maximal stress level being demanded for acceptability and a stable pattern for the speed of rate adjustment through the period. The maximal rate, rate spread and stress levels have come down but with periods in the interim where they have peaked as they did in the crisis. Recovery rates have oscillated and they did fall substantially but have recovered towards 40 percent near the end of the period.
|| Central Bank Collateral, Asset Fire Sales, Regulation and Liquidity
Ulrich Bindseil, Director General Market Operations at the ECB.
We analyze the potential roles of bank asset fire sales and recourse to central bank credit to ensure banks' funding liquidity and solvency. Both asset liquidity and central bank haircuts are modeled as power functions within the unit interval. Funding stability is captured as strategic bank run game in pure strategies between depositors.
Asset liquidity, the central bank collateral framework and regulation determine jointly the ability of the banking system to deliver maturity transformation and financial stability. The model also explains why banks tend to use the least liquid eligible assets as central bank collateral and why a sudden non-anticipated reduction of asset liquidity, or a tightening of the collateral framework, can destabilize short term liabilities of banks. Finally, the model allows discussing how the collateral framework can be understood, beyond its essential aim to protect the central bank, as financial stability and non-conventional monetary policy instrument.
|| From Net Asset Values to Solvency Capital Requirements: A Least-Squares Monte-Carlo Approach
Olivier le Courtois, Professor of Finance and Insurance, Head of the Center for Financial Risks Analysis, EM Lyon Business School.
We show how net asset values (NAVs) and solvency capital requirements (SCRs) can be computed at the level of an insurance company issuing participating contracts subject to surrender and mortality risks and investing its assets in bonds and stocks. In order to reach this goal, we use three different methods. First, we use the least-squares Monte Carlo method. Then, we introduce a method that rests on the order statistics of net asset values. Finally, we introduce an enhanced method for ranking net asset values. This enhanced method can be used for validating the results obtained with the least-squares Monte-Carlo approach.
|| The Use of Models in Bank Stress Tests
Fernando de la Mora, Managing Director, Alvarez & Marsal.
Bank stress tests are becoming a critical tool for regulatory oversight, internal capital management, and investors confidence. Stress test requirements are an important part of the new regulatory framework that introduce multiple financial constraints to banks (capital, liquidity and leverage) which will continue to drive banks' restructuring and balance sheet optimization strategies. In this context, we would like to discuss:
Different stress testing methods and approaches being used in the US, UK and Europe
Comparing stress test results
Calculation rules and underlying methodologies for financial and loss forecasting
Stress testing techniques used for pre-provision revenue, credit, market, securitization, sovereign and funding risks
Implications for bank modeling and model risk management practices
|| Conscious Asset allocation methodologies: the good, the bad, and the ugly from the alternative investment's standpoint
Gabriele Susinno, Executive Director, Head of Quantitative Research, Hedge Funds, Unigestion SA.
While the identification of top managers who can deliver attractive returns over time is a key component of the allocation of assets in a hedge fund portfolio, similarly important is how to allocate among those very same managers. A process that seeks to addresses both superior manager selection and optimal asset allocation is key to long term performance and risk management.
There are many methodologies that investors can employ to in order to construct portfolios. A quick scan of the investment manager universe will result in managers who swear by a quantitative-heavy approach, while others rely on gut feeling and experience to allocate capital. The majority of managers will of course rely on a combination of these two approaches - but what combination? The assumptions used can result in very different portfolios.
Typical optimization methodologies involve constructing portfolios based on a combination of expected returns, expected risks and expected correlations between portfolio components. However, as investors will have recently experienced, future returns are hardly predictable (especially over the time-frame of a typical HF investor). We would argue that expectations about risk have shown to be more stable and can be relied upon when building portfolios. That being said, the next question is what to do with this stable predictor of hedge fund risk?
We believe that by establishing a set of simple rules characterizing portfolios based on Expected Return Independent Allocations investors can not only simplify their own lives, but build portfolios that can minimize downside risks - which is principally the main risk that most hedge fund investors are looking to control. Quantitative tools, which ever an investor chooses to use, should help investors make sense of often complex data and ask the right questions that will maximize value added from a qualitative perspective.
|| Liquidity Risk Management
Thomas Schmale, Solution Management Analytical Banking, SAP..
Calculation and ad hoc analysis of liquidity risk profiles and their backtesting simulation and stresstesting with interactive impact analysis. Regulatory compliance combined with simulation of key figures.
|| Mesa redonda: La tasa sobre las transacciones financieras: ¿sí o no?
Manuel Andrade, Director Comercial, BME Clearing.
Ángel Sánchez Aristi, Responsable de Client Coverage, BBVA.
Luis Seco, CEO Sigma II y RiskLab Toronto.
Santiago Carrillo Menéndez and Antonio Sánchez
Calle (RiskLab-Madrid) and
Luis Seco (RiskLab-Toronto).