Efficient Computation of Exposure Pro files for Counterparty Credit Risk
Qian Feng (Centrum Wiskunde & Informatica (CWI), The Netherlands)
Joint work with Cornelis W. Oosterlee, Cornelis S.L. de Graaf and Drona Kandhai

Tuesday June 3, 11:00-11:30 | session 1.6 | Risk Management | room L

Credit Valuation Adjustment (CVA) and Potential Future Exposure (PFE) have arisen special interest in the risk management of the financial industry since the credit crisis in year 2008. Both quantities depend on the exposure distributions along the time horizon. We are interested in the exposure values of Bermudan options. Two efficient methods are present for the computation of the exposure profiles: one is a combination of the COS method and Monte Carlo simulation, while the other is an extension of the stochastic-grid-bundling-method (SGBM). Both methods are based on a simulated stochastic grid. The results are studied when the underlying asset follows the Black Scholes and the Heston' model, and we compare the difference under these two models to see the impact of stochastic volatility.

How to Manage Model Risk and Model Ambiguity in a Large Dimension asset allocation problem?
Sandrine Foldvari (LSE, UK)
Joint work with Pauline Barrieu

Tuesday June 3, 11:30-12:00 | session 1.6 | Risk Management | room L

In this paper, we study the question of model risk control and model blending when performing asset allocation, in order to provide a clear and traceable way to account for model ambiguity, blend the different models considered and compute a cash reserve. Indeed, investors often have to decide how to allocate their wealth among various assets, by mixing different (and potentially conflicting) models for the assets returns. At a time when regulators impose more constraints on banks and financial institutions in terms of liquidity ratios and capital requirements, as specified in Basel III, taking into account model risk has become increasingly crucial in the asset allocation problem. To deal with these issues, we introduce a two-step robust ambiguity adjustment offering the advantages of being tractable and easy to implement even in large dimension. This approach decomposes the ambiguity aversion into two components: a model specific absolute ambiguity aversion and a relative ambiguity aversion across the set of different prior models considered for the asset returns. The decision process first involves the transformation of the optimal allocations under each prior through a generic absolute ambiguity function. Then the adjusted allocations are mixed through an adjustment function that reflects the relative ambiguity aversion of the investor towards the different models within the set of priors considered, as well as their relative contribution to the overall performance and risk of the portfolio. We illustrate the methodology through the study of a theoretical example and perform some empirical tests on European stock data.

Systemic Risk and Centralized Clearing of OTC derivatives: A Network Approach
Svetlana Borovkova (Vrije Universiteit Amsterdam, The Netherlands)
Joint work with Hicham Lalaoui El Mouttalibi

Tuesday June 3, 12:00-12:30 | session 1.6 | Risk Management | room L

In September 2009, G20 paved the way for the mandatory central clearing of over-the-counter (OTC) derivatives, which came into effect in December 2012. This new regulation involves a central clearing counterparty (CCP): a financial institution acting as an intermediary between buyers and sellers of OTC derivatives. The rationale behind this regulation is that, by removing bilateral agreements, CCPs will absorb the risks facing individual firms and act as a cushion in the event of market stress. However, this increases the systemic importance of CCPs within the financial system.
In this paper, we analyze the effect of central clearing of OTC derivatives on the financial system stability by means of network simulation approach. We build simple but realistic networks of financial rms, connected by bilateral links and via a single CCP. We simulate balance sheets of firms and introduce shocks to the system to simulate defaults. The default mechanism and shock absorption in presence of the CCP is modeled in the way that maximally reflects the reality. We run Monte Carlo simulations of the networks' evolution and obtain their default and contagion characteristics. We analyze the likelihood of the CCP's default and compare the stability of the financial network with and without the CCP for various network configurations and market scenarios.
We find that, for a homogeneous financial system, the presence of the CCP increases the network's stability and the probability of the CCP's failure is virtually zero. However, for non-homogeneous financial networks, we find the opposite e ects: the presence of the CCP leads in this case to a disproportionately large probability of contagion defaults, especially for smaller financial firms. Furthermore, we find that the probability of the CCP failure is substantial in this case, regardless of the capitalization requirements. In all, we find that non-homogeneous networks exhibit greater instability and contagion in the presence of the CCP: a worrying fact, given that any real financial system is highly inhomogeneous in terms of size and concentration.