Kay Giesecke

Affine Point Processes and Portfolio Credit Risk

This paper analyzes a family of multivariate point process models of correlated event timing whose arrival intensity is driven by an affine jump diffusion. The components of an affine point process are self- and cross-exciting and facilitate the description of complex event dependence structures. ODEs characterize the transform of an affine point process and the probability distribution of an integer-valued affine point process. The moments of an affine point process take a closed form. This guarantees a high degree of computational tractability in applications. We illustrate this in the context of portfolio credit risk, where the correlation of corporate defaults is the main issue. We consider the valuation of securities exposed to correlated default risk and demonstrate the significance of our results through market calibration experiments. We show that a simple model variant can capture the default clustering implied by index and tranche market prices during September 2008, a month that witnessed significant volatility.

Systemic Risk: What Defaults Are Telling Us

This paper develops dynamic measures of the systemic risk of the financial sector as a whole. It defines systemic risk as the conditional probability of failure of a sufficiently large fraction of the total population of financial institutions. This definition recognizes that the cause of systemic distress is the correlated failure of institutions to meet obligations to creditors, customers, and trading partners. The likelihood estimators of the failure probability are based on a dynamic hazard model of correlated failure timing that captures the influence on failure timing of time-varying macroeconomic and sector-specific risk factors, and of spillover effects. Tests indicate that our measures provide accurate out-of-sample forecasts of the term structure of systemic risk in the United States for the period from 1998 to 2009. This paper was accepted by Wei Xiong, finance.

Exploring the Sources of Default Clustering

We study the sources of corporate default clustering in the United States. We reject the hypothesis that firms’ default times are correlated only because their conditional default rates depend on observable and latent systematic factors. By contrast, we find strong evidence that contagion, through which the default by one firm has a direct impact on the health of other firms, is a significant clustering source. The amount of clustering that cannot be explained by contagion and firms’ exposure to observable and latent systematic factors is insignificant. Our results have important implications for the pricing and management of correlated default risk.

Time-Changed Birth Processes and Multiname Credit Derivatives

A credit investor such as a bank granting loans to firms or an asset manager buying corporate bonds is exposed to correlated corporate default risk. A multiname credit derivative is a financial security that allows the investor to transfer this risk to the credit market. In this paper, we study the valuation and risk analysis of multiname derivatives. To capture the complex economic phenomena that drive the pricing of these securities, we introduce a time-changed birth process as a probabilistic model of correlated event timing. The self-exciting property of a time-changed birth process captures the feedback from events that is often observed in credit markets. The stochastic variation of arrival rates between events captures the exposure of firms to common economic risk factors. We derive a closed-form expression for the distribution of a time-changed birth process, and develop analytically tractable pricing relations for a range of multiname derivatives valuation problems. We illustrate our results by calibrating a tranche forward and option pricer to market rates of index and tranche swaps.

Premia for Correlated Default Risk

Using data on corporate default experience in the U.S. and market rates of CDX index and tranche swaps of various maturities, we estimate reduced-form models of correlated default timing in the CDX High Yield and Investment Grade portfolios under actual and risk-neutral probabilities. The striking contrast between the estimated processes followed by the actual and risk-neutral arrival intensities of defaults, and between the parameters governing the actual and risk-neutral dynamics of the risk-neutral intensities, indicates the presence of substantial default risk premia in CDX swap market rates. The effects of risk premia on swap rates covary strongly across maturities, and depend on general stock market volatility and several measures of credit spreads. Large moves in the effects of these premia on swap rates have natural interpretations in terms of economic and financial market developments during the sample period, April 2004 to October 2007. Our results suggest that a large portion of the movements in CDX swap market rates observed during the sample period may be caused by changing attitudes toward correlated default risk rather than changes in the economic factors affecting the actual risk of clustered defaults, which ultimately governs swap payoffs.

A Top-Down Approach to Multiname Credit

A multiname credit derivative is a security that is tied to an underlying portfolio of corporate bonds and has payoffs that depend on the loss due to default in the portfolio. The value of a multiname derivative depends on the distribution of portfolio loss at multiple horizons. Intensity-based models of the loss point process that are specified without reference to the portfolio constituents determine this distribution in terms of few economically meaningful parameters and lead to computationally tractable derivatives valuation problems. However, these models are silent about the portfolio constituent risks. They cannot be used to address applications that are based on the relationship between portfolio and component risks, for example, constituent risk hedging. This paper develops a method that extends these models to the constituents. We use random thinning to decompose the portfolio intensity into a sum of constituent intensities. We show that a thinning process, which allocates the portfolio intensity to constituents, uniquely exists, and is a probabilistic model for the next-to-default. We derive a formula for the constituent default probability in terms of the thinning process and the portfolio intensity, and develop a semi-analytical transform approach to evaluate it. The formula leads to a calibration scheme for the thinning processes and an estimation scheme for constituent hedge sensitivities. An empirical analysis for September 2008 shows that the constituent hedges generated by our method outperform the hedges prescribed by the Gaussian copula model, which is widely used in practice.

Large Portfolio Asymptotics for Loss From Default

We prove a law of large numbers for the loss from default and use it for approximating the distribution of the loss from default in large, potentially heterogeneous portfolios. The density of the limiting measure is shown to solve a nonlinear stochastic partial differential equation, and certain moments of the limiting measure are shown to satisfy an infinite system of stochastic differential equations. The solution to this system leads to the distribution of the limiting portfolio loss, which we propose as an approximation to the loss distribution for a large portfolio. Numerical tests illustrate the accuracy of the approximation, and highlight its computational advantages over a direct Monte Carlo simulation of the original stochastic system.

Risk Analysis of Collateralized Debt Obligations

Collateralized debt obligations, which are securities with payoffs that are tied to the cash flows in a portfolio of defaultable assets such as corporate bonds, play a significant role in the financial crisis that has spread throughout the world. Insufficient capital provisioning due to flawed and overly optimistic risk assessments is at the center of the problem. This paper develops stochastic methods to measure the risk of positions in collateralized debt obligations and related instruments tied to an underlying portfolio of defaultable assets. It proposes an adaptive point process model of portfolio default timing, a maximum likelihood method for estimating point process models that is based on an acceptance/rejection resampling scheme, and statistical tests for model validation. To illustrate these tools, they are used to estimate the distribution of the profit or loss generated by positions in multiple tranches of a collateralized debt obligation that references the CDX High Yield portfolio and the risk capital required to support these positions.

Estimating tranche spreads by loss process simulation

A credit derivative is a path dependent contingent claim on the aggregate loss in a portfolio of credit sensitive securities. We estimate the value of a credit derivative by Monte Carlo simulation of the affine point process that models the loss. We consider two algorithms that exploit the direct specification of the loss process in terms of an intensity. One algorithm is based on the simulation of intensity paths. Here discretization introduces bias into the results. The other algorithm facilitates exact simulation of default times and generates an unbiased estimator of the derivative price. We implement the algorithms to value index and tranche swaps, and we calibrate the loss process to quotes on the CDX North America High Yield index.

Exact Sampling of Jump Diffusions

This paper develops a method for the exact simulation of a skeleton, a hitting time, and other functionals of a one-dimensional jump diffusion with state-dependent drift, volatility, jump intensity, and jump size. The method requires the drift function to be C 1 , the volatility function to be C 2 , and the jump intensity function to be locally bounded. No further structure is imposed on these functions. The method leads to unbiased simulation estimators of security prices, transition densities, hitting probabilities, and other quantities. Numerical results illustrate its features.