Consumer Expertise or Credit Risk? An empirical analysis of mortgage pricing

Loan mortgage interest rates are usually the result of a bank-customer negotiation process. Credit risk, consumer cross-buying potential, bundling, financial market competition and other features affecting the bargaining power of the parties could affect price. We argue that, since mortgage loan is a complex product, consumer expertise could be a relevant factor for mortgage pricing. Using data on mortgage loan prices for a sample of 1055 households for the year 2005 (Bank of Spain Survey of Household Finances, EFF-2005), and including credit risk, costs, potential capacity of the consumer to generate future business and bank competition variables, the regression results indicate that consumer expertise-related metrics are highly significant as predictors of mortgage loan prices. Other factors such as credit risk and consumer cross-buying potential do not have such a significant impact on mortgage prices. Our empirical results are affected by the credit conditions prior to the financial crisis and could shed some light on this issue.

Counter-credit-risk yield spreads: a puzzle in China's corporate bond market

In this paper, using China's risk-free and corporate zero yields together with aggregate credit risk measures and various control variables from 2006 to 2013, we document a puzzle of counter-credit-risk corporate yield spreads. We interpret this puzzle as a symptom of the immaturity of China's credit bond market, which reveals a distorted pricing mechanism latent in the fundamental of this market. We also find interesting results about relationships between corporate yield spreads and interest rates and risk premia and the stock index, and these results are somewhat attributed to this puzzle.

Wrong-way risk in stock swaps : measuring counterparty credit risk and CVA

Uma forma interessante para uma companhia que pretende assumir uma posição comprada em suas próprias ações ou lançar futuramente um programa de recompra de ações, mas sem precisar dispor de caixa ou ter que contratar um empréstimo, ou então se protegendo de uma eventual alta no preço das ações, é através da contratação de um swap de ações. Neste swap, a companhia fica ativa na variação de sua própria ação enquanto paga uma taxa de juros pré ou pós-fixada. Contudo, este tipo de swap apresenta risco wrong-way, ou seja, existe uma dependência positiva entre a ação subjacente do swap e a probabilidade de default da companhia, o que precisa ser considerado por um banco ao precificar este tipo de swap. Neste trabalho propomos um modelo para incorporar a dependência entre probabilidades de default e a exposição à contraparte no cálculo do CVA para este tipo de swap. Utilizamos um processo de Cox para modelar o instante de ocorrência de default, dado que a intensidade estocástica de default segue um modelo do tipo CIR, e assumindo que o fator aleatório presente na ação subjacente e que o fator aleatório presente na intensidade de default são dados conjuntamente por uma distribuição normal padrão bivariada. Analisamos o impacto no CVA da incorporação do riscowrong-way para este tipo de swap com diferentes contrapartes, e para diferentes prazos de vencimento e níveis de correlação.

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

The credit risk(+) model with general sector correlations

We consider an enhancement of the credit risk+ model to incorporate correlations between sectors. We model the sector default rates as linear combinations of a common set of independent variables that represent macro-economic variables or risk factors. We also derive the formula for exact VaR contributions at the obligor level.

Application of nonlinear filtering to credit risk

Merton's model views equity as a call option on the asset of the firm. Thus the asset is partially observed through the equity. Then using nonlinear filtering an explicit expression for likelihood ratio for underlying parameters in terms of the nonlinear filter is obtained. As the evolution of the filter itself depends on the parameters in question, this does not permit direct maximum likelihood estimation, but does pave the way for the `Expectation-Maximization' method for estimating parameters. (C) 2010 Elsevier B.V. All rights reserved.

Unfunded pension liabilities and sponsoring firm credit risk: an international analysis of corporate bond spreads

This paper tests empirically whether pension information derived by corporate pension accounting disclosures is priced in corporate bond spreads. The model represents a hybrid of more traditional accounting ratio-based models of credit risk and structural models of bond spreads initiated by Merton (1974). The model is fitted to 5 years of data from 2002 to 2006 featuring companies from the US and Europe. The paper finds that while unfunded pension liabilities are priced in the overall sample, they are not priced as aggressively as traditional leverage. Furthermore, an extended model shows that the pension–credit risk relation is most evident in the US and Germany, where unfunded pension liabilities are priced more aggressively than traditional forms of leverage. No pension–credit risk relation is found in the other countries sampled, notably the UK, Netherlands and France.

Confidence sets for asset correlations in portfolio credit risk

Asset correlations are of critical importance in quantifying portfolio credit risk and economic capitalin financial institutions. Estimation of asset correlation with rating transition data has focusedon the point estimation of the correlation without giving any consideration to the uncertaintyaround these point estimates. In this article we use Bayesian methods to estimate a dynamicfactor model for default risk using rating data (McNeil et al., 2005; McNeil and Wendin, 2007).Bayesian methods allow us to formally incorporate human judgement in the estimation of assetcorrelation, through the prior distribution and fully characterize a confidence set for the correlations.Results indicate: i) a two factor model rather than the one factor model, as proposed bythe Basel II framework, better represents the historical default data. ii) importance of unobservedfactors in this type of models is reinforced and point out that the levels of the implied asset correlationscritically depend on the latent state variable used to capture the dynamics of default,as well as other assumptions on the statistical model. iii) the posterior distributions of the assetcorrelations show that the Basel recommended bounds, for this parameter, undermine the levelof systemic risk.

Risk pricing practices in finance, insurance and construction

A review of current risk pricing practices in the financial, insurance and construction sectors is conducted through a comprehensive literature review. The purpose was to inform a study on risk and price in the tendering processes of contractors: specifically, how contractors take account of risk when they are calculating their bids for construction work. The reference to mainstream literature was in view of construction management research as a field of application rather than a fundamental academic discipline. Analytical models are used for risk pricing in the financial sector. Certain mathematical laws and principles of insurance are used to price risk in the insurance sector. construction contractors and practitioners are described to traditionally price allowances for project risk using mechanisms such as intuition and experience. Project risk analysis models have proliferated in recent years. However, they are rarely used because of problems practitioners face when confronted with them. A discussion of practices across the three sectors shows that the construction industry does not approach risk according to the sophisticated mechanisms of the two other sectors. This is not a poor situation in itself. However, knowledge transfer from finance and insurance can help construction practitioners. But also, formal risk models for contractors should be informed by the commercial exigencies and unique characteristics of the construction sector.

Stress testing credit risk: the Great Depression scenario

By employing Moody’s corporate default and rating transition data spanning the last 90 years we explore how much capital banks should hold against their corporate loan portfolios to withstand historical stress scenarios. Specifically, we will focus on the worst case scenario over the observation period, the Great Depression. We find that migration risk and the length of the investment horizon are critical factors when determining bank capital needs in a crisis. We show that capital may need to rise more than three times when the horizon is increased from 1 year, as required by current and future regulation, to 3 years. Increases are still important but of a lower magnitude when migration risk is introduced in the analysis. Further, we find that the new bank capital requirements under the so-called Basel 3 agreement would enable banks to absorb Great Depression-style losses. But, such losses would dent regulatory capital considerably and far beyond the capital buffers that have been proposed to ensure that banks survive crisis periods without government support.