Factor models and the credit risk of a loan portfolio

Factor models for portfolio credit risk assume that defaults are independent conditional on a small number of systematic factors. This paper shows that the conditional independence assumption may be violated in one-factor models with constant default thresholds, as conditional defaults become independent only including a set of observable (time-lagged) risk factors. This result is confirmed both when we consider semi-annual default rates and if we focus on small firms. Maximum likelihood estimates for the sensitivity of default rates to systematic risk factors are obtained, showing how they may substantially vary across industry sectors. Finally, individual risk contributions are derived through Monte Carlo simulation.Asset correlation, factor models, loss distribution, portfolio credit risk, risk contributions

Credit risk contributions under the Vasicek one-factor model: a fast wavelet expansion approximation

To measure the contribution of individual transactions inside the total risk of a credit portfolio is a major issue in financial institutions. VaR Contributions (VaRC) and Expected Shortfall Contributions (ESC) have become two popular ways of quantifying the risks. However, the usual Monte Carlo (MC) approach is known to be a very time consum- ing method for computing these risk contributions. In this paper we consider the Wavelet Approximation (WA) method for Value at Risk (VaR) computation presented in [Mas10] in order to calculate the Expected Shortfall (ES) and the risk contributions under the Vasicek one-factor model framework. We decompose the VaR and the ES as a sum of sensitivities representing the marginal impact on the total portfolio risk. Moreover, we present technical improvements in the Wavelet Approximation (WA) that considerably reduce the computa- tional effort in the approximation while, at the same time, the accuracy increasesPeer ReviewedPreprin

Accounting for risk of non linear portfolios: a novel Fourier approach

The presence of non linear instruments is responsible for the emergence of non Gaussian features in the price changes distribution of realistic portfolios, even for Normally distributed risk factors. This is especially true for the benchmark Delta Gamma Normal model, which in general exhibits exponentially damped power law tails. We show how the knowledge of the model characteristic function leads to Fourier representations for two standard risk measures, the Value at Risk and the Expected Shortfall, and for their sensitivities with respect to the model parameters. We detail the numerical implementation of our formulae and we emphasizes the reliability and efficiency of our results in comparison with Monte Carlo simulation.Comment: 10 pages, 12 figures. Final version accepted for publication on Eur. Phys. J.

Risk transfer with CDOs and systemic risk in banking

Large banks often sell part of their loan portfolio in the form of collateralized debt obligations (CDO) to investors. In this paper we raise the question whether credit asset securitization affects the cyclicality (or commonality) of bank equity values. The commonality of bank equity values reflects a major component of systemic risks in the banking market, caused by correlated defaults of loans in the banks' loan books. Our simulations take into account the major stylized fact of CDO transactions, the non-proportional nature of risk sharing that goes along with tranching. We provide a theoretical framework for the risk transfer through securitization that builds on a macro risk factor and an idiosyncratic risk factor, allowing an identification of the types of risk that the individual tranche holders bear. This allows conclusions about the risk positions of issuing banks after risk transfer. Building on the strict subordination of tranches, we first evaluate the correlation properties both within and across risk classes. We then determine the effect of securitization on the systematic risk of all tranches, and derive its effect on the issuing bank's equity beta. The simulation results show that under plausible assumptions concerning bank reinvestment behaviour and capital structure choice, the issuing intermediary's systematic risk tends to rise. We discuss the implications of our findings for financial stability supervision. Klassifikation: G2

THE APPLICATION OF COPULAS IN PRICING DEPENDENT CREDIT DERIVATIVES INSTRUMENTS

The aim of this paper is to use copulas functions to capture the different structures of dependency when we deal with portfolios of dependent credit risks and a basket of credit derivatives. We first present the wellknown result for the pricing of default risk, when there is only one defaultable firm. After that, we expose the structure of dependency with copulas in pricing dependent credit derivatives. Many studies suggest the inadequacy of multinormal distribution and then the failure of methods based on linear correlation for measuring the structure of dependency. Finally, we use Monte Carlo simulations for pricing Collateralized debt obligation (CDO) with Gaussian an Student copulas.default risk, credit derivatives, CDO, copulas functions, Monte Carlo simulations.

Risk Transfer with CDOs and Systemic Risk in Banking

Large banks often sell part of their loan portfolio in the form of collateralized debt obligations (CDO) to investors. In this paper we raise the question whether credit asset securitization affects the cyclicality (or commonality) of bank equity values. The commonality of bank equity values reflects a major component of systemic risks in the banking market, caused by correlated defaults of loans in the banks’ loan books. Our simulations take into account the major stylized fact of CDO transactions, the nonproportional nature of risk sharing that goes along with tranching. We provide a theoretical framework for the risk transfer through securitization that builds on a macro risk factor and an idiosyncratic risk factor, allowing an identification of the types of risk that the individual tranche holders bear. This allows conclusions about the risk positions of issuing banks after risk transfer. Building on the strict subordination of tranches, we first evaluate the correlation properties both within and across risk classes. We then determine the effect of securitization on the systematic risk of all tranches, and derive its effect on the issuing bank’s equity beta. The simulation results show that under plausible assumptions concerning bank reinvestment behaviour and capital structure choice, the issuing intermediary’s systematic risk tends to rise. We discuss the implications of our findings for financial stability supervision.

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Actuarial Applications and Estimation of Extended~CreditRisk+^+

We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death causes. Parameter families for mortality trends can be chosen freely. As model settings become high dimensional, Markov chain Monte Carlo (MCMC) is used for parameter estimation. We then link our proposed model to an extended version of the credit risk model CreditRisk$^+$. This allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm and provides numerous applications in credit, life insurance and annuity portfolios to derive P\&L distributions. Furthermore, the model allows exact (without Monte Carlo simulation error) calculation of risk measures and their sensitivities with respect to model parameters for P\&L distributions such as value-at-risk and expected shortfall. Numerous examples, including an application to partial internal models under Solvency II, using Austrian and Australian data are shown.Comment: 34 pages, 5 figure

Efficient estimation of sensitivities for counterparty credit risk with the finite difference Monte Carlo method

According to Basel III, financial institutions have to charge a credit valuation adjustment (CVA) to account for a possible counterparty default. Calculating this measure and its sensitivities is one of the biggest challenges in risk management. Here, we introduce an efficient method for the estimation of CVA and its sensitivities for a portfolio of financial derivatives. We use the finite difference Monte Carlo (FDMC) method to measure exposure profiles and consider the computationally challenging case of foreign exchange barrier options in the context of the Black–Scholes as well as the Heston stochastic volatility model, with and without stochastic domestic interest rate, for a wide range of parameters. In the case of a fixed domestic interest rate, our results show that FDMC is an accurate method compared with the semi-analytic COS method and, advantageously, can compute multiple options on one grid. In the more general case of a stochastic domestic interest rate, we show that we can accurately compute exposures of discontinuous one-touch options by using a linear interpolation technique as well as sensitivities with respect to initial interest rate and variance. This paves the way for real portfolio level risk analysis