Peter N. Posch

The bonus pool, mark to market and free cash flow: producer surplus and its vesting in the financial markets

Regulatory proposals that seek to limit or govern finance industry bonuses in the interests of systemic stability need to be grounded in the financial economics of producer surplus and its distribution. In this respect, existing treatments of economic agency in justifying large bonus awards are content to accept accounting Profit and Loss (P&L) numbers as a basis for the managerial bonus pool. We argue that managerial bonuses and shareholder dividends should be treated more symmetrically, and constrained by free cash flow criteria that capture producer surplus created by genuine managerial ability. Priority rules should apply, such that fair market value is a compensation for shareholder risk bearing and not a source of managerial surplus. The use of free cash flow conversion ratios neutralises the free option problem that has become a social irritant in public bailouts.

Wall Street’s Bailout Bet: Market Reactions to House Price Releases in the Presence of Bailout Expectations

The rise and subsequent collapse of US house prices was one of the factors underlying the recent financial crisis. One could expect that the crisis brought increased attention to the housing market and thus led to stronger market reactions to house price news. We find that reactions indeed change, but with a peculiar twist: from September 2008 on, good news from the housing market are associated with falling US stock prices, and vice versa. The likely explanation, for which we provide cross-sectional evidence, is that falling house prices increased the market’s trust in a government bail-out, thereby increasing market valuations of firms that were expected to benefit from government rescue measures.

Managing and trading sovereign risk using credit derivatives and government markets

Purpose - – The sovereign debt crisis in Europe increased the demand for asset manager worldwide to monitor and manage their sovereign risk. While using information from the credit derivatives and bond markets has been used widely in the corporate sector its usage for sovereign risk is novel. The paper aims to discuss these issues. Design/methodology/approach - – The basis between a sovereign credit default swap (CDS) and the government bond contains valuable information for assets managers and traders alike. The paper demonstrates the use of the basis between the announcement date and the issue date of a new government bond to decide whether an investment in this bond is profitable. Findings - – With this strategy, the authors are able to generate both over all excess returns with a European sovereign portfolio since 2008 as well as a constant outperformance of simple average euro government bond portfolios. The paper furthermore tests the economic rationale behind this trading strategy and confirms prior findings from the corporate market. CDS market liquidity is among the main driver and it follows that the CDS market is faster in anticipating risks than the bond market not only for corporate but also for sovereign entities. Originality/value - – The authors are the first to study the sovereign basis in a sound trading and asset management environment. The paper provides economic explanations and checks for the robustness of the results before the primary issuance of a new government bond.

In Contango Versus Backwardation, the Truth May Not be in Convenience: Disequilibrium States and the Spot-Forward Balance in Commodity Markets.

Abstract The notion of a stochastic ‘convenience yield’ to explain variations and reversals in the spot -forward premium is a common rationalisation in commodity market research. However, such variations may arise from causes more intrinsically related to the structure and cash flows of the extended commodity markets. An instance is where the market can be subject to disequilibrium phases, characterised by rationing or clearing impediments that interfere with arbitrage. Th ese are likely to arise when market inventory is in short supply, so that disequilibrium switches can be based on the inventory/sales ratio.

How do Rating Agencies Score in Predicting Firm Performance

We use dynamic panel analysis to examine whether credit rating agencies achieve what they claim to achieve, namely, look into the future when assigning their ratings. We find that Moodys ratings help predict individual financial ratios over a horizon of up to five years. Ratings also predict a multivariate credit score, again over five years. The contribution of ratings appears to be economically significant and robust for different specifications.

Value-based assessment of sovereign risk

Purpose - – The purpose of this paper is to introduce a methodology to evaluate sovereign risk. Hereby, a value-based approach using different market measures is introduced. Design/methodology/approach - – This study’s approach aims to provide a value-based assessment of sovereign risk, combining market measures from government bond, credit derivatives and other markets as well as economic indicators. Findings - – The study finds that the assessment of sovereign risk is only possible when using information from different markets and adjusting according to the information included in these measures. Combining both market-based and economic information leads to a value-based evaluation of sovereign risk. Practical implications - – The practical implications are given for any institution with sovereign risk on their asset side. In fact, part of this research was done for the German Actuarial Foundation which uses the recommendations of this paper for the insurance industry. Originality/value - – The study’s approach is novel because it is the first to include several market-based and economic measures of a sovereign and combines it into a value-based assessment.

Credit Risk Modeling Using Excel and VBA with DVD: Löffler/Credit

Preface to the 2nd edition. Preface to the 1st edition. Some Hints for Troubleshooting. 1 Estimating Credit Scores with Logit. Linking scores, default probabilities and observed default behavior. Estimating logit coefficients in Excel. Computing statistics after model estimation. Interpreting regression statistics. Prediction and scenario analysis. Treating outliers in input variables. Choosing the functional relationship between the score and explanatory variables. Concluding remarks. Appendix. Logit and probit. Marginal effects. Notes and literature. 2 The Structural Approach to Default Prediction and Valuation. Default and valuation in a structural model. Implementing the Merton model with a one-year horizon. The iterative approach. A solution using equity values and equity volatilities. Implementing the Merton model with a T -year horizon. Credit spreads. CreditGrades. Appendix. Notes and literature. Assumptions. Literature. 3 Transition Matrices. Cohort approach. Multi-period transitions. Hazard rate approach. Obtaining a generator matrix from a given transition matrix. Confidence intervals with the binomial distribution. Bootstrapped confidence intervals for the hazard approach. Notes and literature. Appendix. Matrix functions. 4 Prediction of Default and Transition Rates. Candidate variables for prediction. Predicting investment-grade default rates with linear regression. Predicting investment-grade default rates with Poisson regression. Backtesting the prediction models. Predicting transition matrices. Adjusting transition matrices. Representing transition matrices with a single parameter. Shifting the transition matrix. Backtesting the transition forecasts. Scope of application. Notes and literature. Appendix. 5 Prediction of Loss Given Default. Candidate variables for prediction. Instrument-related variables. Firm-specific variables. Macroeconomic variables. Industry variables. Creating a data set. Regression analysis of LGD. Backtesting predictions. Notes and literature. Appendix. 6 Modeling and Estimating Default Correlations with the Asset Value Approach. Default correlation, joint default probabilities and the asset value approach. Calibrating the asset value approach to default experience: the method of moments. Estimating asset correlation with maximum likelihood. Exploring the reliability of estimators with a Monte Carlo study. Concluding remarks. Notes and literature. 7 Measuring Credit Portfolio Risk with the Asset Value Approach. A default-mode model implemented in the spreadsheet. VBA implementation of a default-mode model. Importance sampling. Quasi Monte Carlo. Assessing Simulation Error. Exploiting portfolio structure in the VBA program. Dealing with parameter uncertainty. Extensions. First extension: Multi-factor model. Second extension: t-distributed asset values. Third extension: Random LGDs. Fourth extension: Other risk measures. Fifth extension: Multi-state modeling. Notes and literature. 8 Validation of Rating Systems. Cumulative accuracy profile and accuracy ratios. Receiver operating characteristic (ROC). Bootstrapping confidence intervals for the accuracy ratio. Interpreting caps and ROCs. Brier score. Testing the calibration of rating-specific default probabilities. Validation strategies. Testing for missing information. Notes and literature. 9 Validation of Credit Portfolio Models. Testing distributions with the Berkowitz test. Example implementation of the Berkowitz test Representing the loss distribution. Simulating the critical chi-square value. Testing modeling details: Berkowitz on subportfolios. Assessing power. Scope and limits of the test. Notes and literature. 10 Credit Default Swaps and Risk-Neutral Default Probabilities. Describing the term structure of default: PDs cumulative, marginal and seen from today. From bond prices to risk-neutral default probabilities. Concepts and formulae. Implementation. Pricing a CDS. Refining the PD estimation. Market values for a CDS. Example. Estimating upfront CDS and the Big Bang protocol. Pricing of a pro-rata basket. Forward CDS spreads. Example. Pricing of swaptions. Notes and literature. Appendix. Deriving the hazard rate for a CDS. 11 Risk Analysis and Pricing of Structured Credit: CDOs and First-to-Default Swaps. Estimating CDO risk with Monte Carlo simulation. The large homogeneous portfolio (LHP) approximation. Systemic risk of CDO tranches. Default times for first-to-default swaps. CDO pricing in the LHP framework. Simulation-based CDO pricing. Notes and literature. Appendix. Closed-form solution for the LHP model. Cholesky decomposition. Estimating PD structure from a CDS. 12 Basel II and Internal Ratings. Calculating capital requirements in the Internal Ratings-Based (IRB) approach. Assessing a given grading structure. Towards an optimal grading structure. Notes and literature. Appendix A1 Visual Basics for Applications (VBA). Appendix A2 Solver. Appendix A3 Maximum Likelihood Estimation and Newtons Method. Appendix A4 Testing and Goodness of Fit. Appendix A5 User-defined Functions. Index.