We provide a rationale for imposing counter-cyclical capital ratios on banks. In our simple model, bankers cannot pledge the entire future revenues to investors, which limits borrowing in good and bad times. Complete markets do not sufficiently stabilize credit fluctuations, as banks allocate too much borrowing capacity to good states and too little to bad states. As a consequence, bank credit, output, capital prices or wages are excessively volatile. Imposing a (stricter) capital ratio in good states corrects the misallocation of the borrowing capacity, increases expected output and can be beneficial to all agents in the economy. Although in our economy, all agents are risk-neutral, counter-cyclical capital ratios are an effective stabilization tool. To ensure this effectiveness, capital ratios have to be based on ex ante equity capital, as classical capital ratios can be bypassed.
We identify the impact of short-term interest rates on credit risk-taking in the short and long run by analyzing a comprehensive credit register from Spain, a country where for the last twenty years monetary policy was mostly decided abroad. Duration analyses show that lower overnight rates prior to loan origination lead banks to lend more to borrowers with a worse credit history and to grant more loans with a higher per-period probability of default. Lower overnight rates during the life of the loan reduce this probability. Bank, borrower and market characteristics determine the impact of overnight rates on credit risk-taking.
Are Islamic banks inherently more stable than conventional banks? We address this question by applying a survival analysis based on the Cox proportional hazard model to a comprehensive sample of 421 banks in 20 Middle and Far Eastern countries from 1995 to 2010. By comparing the failure risk for both bank types, we find that Islamic banks have a significantly lower risk of failure than that of their conventional peers. This lower risk is based both unconditionally and conditionally on bank-specific (microeconomic) variables as well as macroeconomic and market structure variables. Our findings indicate that the design and implementation of early warning systems for bank failure should recognize the distinct risk profiles of the two bank types.
A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.
Background: Value sensitivity – the ability to recognize value-related issues when they arise in practice – is an indispensable competence for medical practitioners to enter decision-making processes related to ethical questions. However, the psychological competence of value sensitivity is seldom an explicit subject in the training of medical professionals. In this contribution, we outline the traditional concept of moral sensitivity in medicine and its revised form conceptualized as value sensitivity and we propose an instrument that measures value sensitivity.
Methods: We developed an instrument for assessing the sensitivity for three value groups (moral-related values, values related to the principles of biomedical ethics, strategy-related values) in a four step procedure: 1) value identification (n = 317); 2) value representation (n = 317); 3) vignette construction and quality evaluation (n = 37); and 4) instrument validation by comparing nursing professionals with hospital managers (n = 48).
Results: We find that nursing professionals recognize and ascribe importance to principle-related issues more than professionals from hospital management. The latter are more likely to recognize and ascribe importance to strategy-related issues.
Conclusions: These hypothesis-driven results demonstrate the discriminatory power of our newly developed instrument, which makes it useful not only for health care professionals in practice but for students and people working in the clinical context as well.
A new approach for multivariate modelling and prediction of asset returns is proposed. It is based on a two-component normal mixture, estimated using a fast new variation of the minimum covariance determinant (MCD) method made suitable for time series. It outperforms the (shrinkage-augmented) MLE in terms of out-of-sample density forecasts and portfolio performance. In addition to the usual stylized facts of skewness and leptokurtosis, the model also accommodates leverage and contagion effects, but is i.i.d., and thus does not embody, for example, a GARCH-type structure. Owing to analytic tractability of the moments and the expected shortfall, portfolio optimization is straightforward, and, for daily equity returns data, is shown to substantially outperform the equally weighted and classical long-only Markowitz framework, as well as DCC-GARCH (despite not using any kind of GARCH-type filter).
Two new tests for the symmetric stable Paretian distribution with tail index 1 < α < 2 are proposed. The test statistics and their associated approximate p-values are instantly computed and do not require use of the stable density or distribution or maximum likelihood estimation. They exhibit high power against a variety of alternatives, and much higher power than the existing test based on the empirical characteristic function. The two tests are combined to yield a test that has substantially higher power. A fourth test based on likelihood ratio is also studied. Extensions are proposed to address the asymmetric case and are shown to have reasonable actual size properties and high power against several viable alternatives.
This paper develops a novel and highly efficient numerical algorithm for the gap risk-adjusted valuation of leveraged certificates. The existing literature relies on Monte Carlo simulations, which are not fast enough to be used in a market making environment. This is because issuers need to compute thousands of price updates per second. By valuing leveraged certificates as multi-window barrier options, we explicitly model random jumps that occur at known times, such as between the exchange closing and re-opening. Our algorithm combines the one-day transition probability with Simpson's numerical integration rule. This yields a backward induction scheme which requires a significantly coarser spacial and time grid than finite difference methods. We confirm its robustness and accuracy through Monte Carlo simulations.
We develop a simple macroeconomic model with extreme financial frictions (no credit markets) and show that poverty traps can emerge even in the absence of leverage. In our model, farmers produce fruit by renting land from landlords. Crops are exposed to aggregate shocks (weather risk). To guarantee themselves a positive consumption level even after a bad crop, farmers store fruit as precautionary savings and adjust their scale of activity to the level of these savings. The land that is not rented to farmers is cultivated by landlords, who are less productive. We show that there is a unique Markov competitive equilibrium, in which the rental price of land increases with the level of farmers’ savings. A decline in savings, caused by a bad crop, may bring the economy into a ”poverty trap”, even in the absence of any leverage. Fluctuations of output are caused by productivity shocks and amplified by fluctuations in the level of activity of farmers. The simplicity of our model allows us to study analytically why the long run behavior of the economy may differ markedly from the one predicted by the steady state paradigm. Specifically, we show that when the risk-adjusted productivity of farmers is high and the elasticity of the land supply is low, using the steady state paradigm leads to serious mis-estimations of the long run average state of the economy.
