Theodore M. Barnhill

Modeling correlated market and credit risk in fixed income portfolios

Abstract Current risk assessment methodologies separate the analysis of market and credit risk and thus misestimate security and portfolio risk levels. We propose a new approach that relates financial market volatility to firm specific credit risk and integrates interest rate, interest rate spread, and foreign exchange rate risk into one overall fixed income portfolio risk assessment. Accounting for the correlation between these significant risk factors as well as portfolio diversification results in improved risk measurement and management. The methodology is shown to produce reasonable credit transition probabilities, prices for bonds with credit risk, and portfolio value-at-risk measures.

Assessing Fiscal Sustainability Under Uncertainity

Unlike conventional fiscal sustainability assessments, the Value-at-Risk approach developed in this paper explicitly captures the contribution of key risk variables to public sector vulnerability. In an illustrative application to Ecuador, the VaR approach confirms a significant risk of government financial failure stemming from the volatility and comovements of the exchange rate, interest rates, oil prices, and output. Although dollarization has helped attenuate fiscal vulnerability, the volatility of sovereign spreads and of oil prices remain major sources of risk for Ecuadors public sector. The paper concludes with a discussion of policy implications, an evaluation of the methodology, and suggestions for future research.

Application of statistical mechanics methodology to term-structure bond-pricing models

Recent work in statistical mechanics has developed new analytical and numerical techniques to solve coupled stochastic equations. This paper applies the very fast simulated re-annealing and path-integral methodologies to the estimation of the Brennan and Schwartz two-factor term structure model. It is shown that these methodologies can be utilized to estimate more complicated n-factor nonlinear models.

Factors affecting the yields on noninvestment grade bond indices: a cointegration analysis

Abstract This study examines the long- and short-run dynamics of the yields on noninvestment grade indices. Utilizing cointegration techniques, the traditional yield spread model is found to be inadequate. A revised model finds a long-run relationship between noninvestment grade yields, Treasury securities, and default rates. Error correction models are formulated to model the short-run dynamics of different segments of the market. These models include a long-run equilibrium (between yields, default rates, and Treasuries), mutual fund flows, minor bond ratings, debt subordination measures, a stock index, and a January effect. Segmentation in the noninvestment grade market is also demonstrated.

Measuring Integrated Market and Credit Risks in Bank Portfolios : An Application to a Set of Hypothetical Banks Operation in South Africa

The banking crises of the 1990s emphasize the need to model the connections between volatility and the potential losses faced by financial institutions due to correlated market and credit risks. We present a simulation model that explicitly links changes in the financial environment and the distribution of future bank capital ratios. This forward-looking quantitative risk assessment methodology allows banks and regulators to identify risks before they materialize and make appropriate adjustments to banks` portfolios. This model was applied to the study of the risk profile of the largest South African banks in the context of the Financial System Stability Assessment (FSSA) (1999).

Modeling Correlated Systemic Liquidity and Solvency Risks in a Financial Environment with Incomplete Information

This paper proposes and demonstrates a methodology for modeling correlated systemic solvency and liquidity risks for a banking system. Using a forward looking simulation of many risk factors applied to detailed balance sheets for a 10 bank stylized United States banking system, we analyze correlated market and credit risk and estimate the probability that multiple banks will fail or experience liquidity runs simultaneously. Significant systemic risk factors are shown to include financial and economic environment regime shifts to stressful conditions, poor initial loan credit quality, loan portfolio sector and regional concentrations, bank creditors’ sensitivity to and uncertainties regarding solvency risk, and inadequate capital. Systemic banking system solvency risk is driven by the correlated defaults of many borrowers, other market risks, and inter-bank defaults. Liquidity runs are modeled as a response to elevated solvency risk and uncertainties and are shown to increase correlated bank failures. Potential bank funding outflows and contractions in lending with significant real economic impacts are estimated. Increases in equity capital levels needed to reduce bank solvency and liquidity risk levels to a target confidence level are also estimated to range from 3 percent to 20 percent of assets. For a future environment that replicates the 1987-2006 volatilities and correlations, we find only a small risk of U.S. bank failures focused on thinly capitalized and regionally concentrated smaller banks. For the 2007-2010 financial environment calibration we find substantially elevated solvency and liquidity risks for all banks and the banking system.

Assessing fiscal sustainability under uncertainty

Unlike conventional fiscal sustainability assessments, the Value-at-Risk approach developed in this paper explicitly captures the contribution of key risk variables to public sector vulnerability. In an illustrative application to Ecuador, the VaR approach confirms a significant risk of government financial failure stemming from the volatility and comovements of the exchange rate, interest rates, oil prices, and output. Although dollarization has helped attenuate fiscal vulnerability, the volatility of sovereign spreads and of oil prices remain major sources of risk for Ecuadors public sector. The paper concludes with a discussion of policy implications, an evaluation of the methodology, and suggestions for future research.

Contingent Claims Analysis Applied in Credit Risk Modeling

A fundamental in credit risk analysis is estimation of the probability that fixed-income securities will migrate to different bond rating categories. Unlike historical transition probabilities, several contingent claims credit risk methodologies simulate the value of a firms assets and/or debt ratio to create bond rating probability distributions. This study tests the validity of using empirically calculated debt ratios to assign bond ratings. The empirical tests provide evidence supporting the use of such methodologies, with some caveats. Debt ratios are industry-specific and time-dependent, so care must be taken in estimating model parameters from historical relationships.

Stochastic Volatilities and Correlations, Extreme Values and Modeling the Macroeconomic Environment, Under Which Brazilian Banks Operate

Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably well the historical evolution of volatilities by changing a decay factor appropriately. Finally, we implement a simple stochastic volatility model and simulate the credit transition matrix for two large Brazilian banks and show that this methodology has the potential to improve simulated transition probabilities as compared to the constant volatility case. In particular, it can shift CTM probabilities towards lower credit risk categories.