The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns

We address one interesting case — the predictability of excess US asset returns from macroeconomic factors within a flexible regime switching VAR framework — in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After having documented that forecast combinations provide gains in prediction accuracy and these gains are statistically significant, we show that combinations may substantially improve portfolio selection. We find that the best performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best performing combination schemes are based on the principle of relative, past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.Forecasting

Empirical risk analysis of pension insurance: the case of Germany

With this paper we seek to contribute to the literature on pension insurance systems. The financial literature tends to focus exclusively on the US pension insurance system. This is the first major empirical study to address the German occupational pension insurance (PSVaG) plan in Germany. The study is based on a Merton-type one-factor model, in which we determine the credit portfolio risk profile of the occupational pension insurance plan and compare two alternative pricing plans. We find that there is a low, yet non-negligible risk of very high losses that may threaten the existence of the occupational pension insurance plan (PSVaG). While relating risk premiums to firms' default probabilities would cause them to diverge widely, a marginal risk contribution method would produce less pronounced differences compared to the current, uniform pricing plan. --Pension insurance,Risk-adjusted premiums,Credit portfolio risk

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Employing a large number of real and financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. Importantly, the predictor set includes option-adjusted credit spread indexes based on bond portfolios sorted by maturity and credit risk as measured by the issuer’s “distance-to-default.” The portfolios are constructed directly from the secondary market prices of outstanding senior unsecured bonds issued by a large number of U.S. corporations. Our results indicate that relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the growth rates of real GDP, business fixed investment, industrial production, and employment, as well as of the changes in the unemployment rate, at horizons from the current quarter (i.e., “nowcasting”) out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe exclusively to the inclusion of our portfolio credit spreads in the set of predictors—BMA consistently assigns a high posterior weight to models that include these financial indicators.

Global Business Cycles and Credit Risk

The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconomic model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogenous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.

Global Business Cycles and Credit Risk

The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconometric model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.risk management, default dependence, economic interlinkages, portfolio choice

Are the dynamic linkages between the macroeconomy and asset prices time-varying?

We estimate a number of multivariate regime switching VAR models on a long monthly data set for eight variables that include excess stock and bond returns, the real T-bill yield, predictors used in the finance literature (default spread and the dividend yield), and three macroeconomic variables (inflation, real industrial production growth, and a measure of real money growth). Heteroskedasticity may be accounted for by making the covariance matrix a function of the regime. We find evidence of four regimes and of time-varying covariances. We provide evidence that the best in-sample fit is provided by a four state model in which the VAR(1) component fails to be regime-dependent. We interpret this as evidence that the dynamic linkages between financial markets and the macroeconomy have been stable over time. We show that the four-state model can be helpful in forecasting applications and to provide one-step ahead predicted Sharpe ratios.Macroeconomics ; Asset pricing

The Influence of Collateral on Capital Requirements in the Brazilian Financial System: an approach through historical average and logistic regression on probability of default

Using data drawn from the Brazilian Central Bank Credit Information System, this paper evaluates the impact of the use of collateral on the probability of default and, consequently, on capital requirement levels in the Brazilian financial system. Literature suggests that the existence of collateral in some credit operations increases the debtor's readiness to honor its commitment and, therefore, could result in a lower probability of default. The methodology used to calculate capital requirements is based on the Basel II IRB-Foundation Approach, although the probabilities of default have been estimated by historical averages following Basel II orientation, and corroborated by a logistic regression model. The test of hypothesis about difference between collateralized and uncollateralized probabilities of default for each risk class indicates that they are statistically different. This result was obtained both from historical average probability of default as from logistic regression model.Sob condições específicas, incluindo o requerimento de capital de 11% adotado no Brasil e a Perda dado Default (ou LGD da sigla em inglês) estabelecida em 45%, este artigo também procura identificar um fator de equivalência da razão entre os requerimentos de capital para risco de crédito na Abordagem Padronizada Simplificada e aqueles calculados pela Abordagem Básica do IRB. Para a amostra utilizada, os resultados indicam que operações de não-varejo com garantia possuem uma probabilidade média de default de 2,46% e um fator de equivalência de 60%. Em contrapartida, operações não garantidas possuem uma probabilidade média de default de 6,66% e um fator de equivalência de 93%, aproximando-se bastante do fator de ponderação de 100% da Abordagem Padronizada Simplificada.

Default Rates in the Loan Market for SMEs:Evidence from Slovakia

The current crisis raises the question whether loans to SMEs in emerging markets areinherently more risky. We use a unique unbalanced panel of nearly 700 loans made toSMEs in Slovakia between 2000 and 2005. Several probit and panel probit models showthat liquidity and profitability factors are important determinants of SME defaults.Moreover, we find that indebtedness significantly increases the probability of default.Finally, liability as proxied by the legal form of SMEs has important incentive effects.In sum, default rates and factors converged to values found in developed financialmarkets.SMEs, banking, loan default, incentives, asymmetric information, probit, financial crisis

Macroeconomic Dynamics and Credit Risk: A Global Perspective

The aim of this paper is to develop a framework for modeling conditional loss distributions through the introduction of risk factor dynamics. Asset value changes of a credit portfolio are linked to a dynamic global macroeconometric model, allowing macro effects to be isolated from idiosyncratic shocks from the perspective of default (and hence loss). Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. The model is able to control for firm-specific heterogeneity in an explicitly interdependent global context, as well as to generate multi-period forecasts of the entire loss distribution, conditional on specific macroeconomic scenarios. The approach can be used, for example, to compute the effects of a hypothetical negative equity price shock in South East Asia on the loss distribution of a credit portfolio with global exposures over one or more quarters. Our conditional modeling framework is thus a step towards joint consideration of market and credit risk. The approach has several other features of particular relevance for risk managers, such as the exploration of scale and symmetry of shocks, and the effect of non-normality on credit risk. We show that the effects of such shocks on losses are asymmetric and non-proportional, reflecting the highly non-linear nature of the credit risk model. Non-normal innovations such as Student t generate expected and unexpected losses which increase the fatter the tails of the innovations.Risk management, economic interlinkages, loss forecasting, default correlation