Risk measurements are clearly central to risk management, in particular for banks, (re)insurance companies, and investment funds. The question of the appropriateness of risk measures for evaluating the risk of financial institutions has been heavily debated, especially after the financial crisis of 2008/2009. Another concern for financial institutions is the pro-cyclicality of risk measurements. In this paper, we extend existing work on the pro-cyclicality of the Value-at-Risk to its main competitors, Expected Shortfall, and Expectile: We compare the pro-cyclicality of historical quantile-based risk estimation, taking into account the market state. To characterise the latter, we propose various estimators of the realised volatility. Considering the family of augmented GARCH(p, q) processes (containing well-known GARCH models and iid models, as special cases), we prove that the strength of pro-cyclicality depends on the three factors: the choice of risk measure and its estimators, the realised volatility estimator and the model considered, but, no matter the choices, the pro-cyclicality is always present. We complement this theoretical analysis by performing simulation studies in the iid case and developing a case study on real data.

We use Canadian data over the period of 1991Q1 to 2019Q2 to examine the effect of higher minimum wages on consumption, measured as the real retail trade sales per adult population. Such an examination is rare in the extant literature and it is timely given the increasing debate concerning the stimulus versus inflationary effects arising from wage polices because of COVID-19 global pandemic. We apply the autoregressive distributed lag model to determine the causal relationship between these variables. We find one long-run cointegrating relationship that runs from the real minimum wage to the real retail trade sales. In addition, we find that a 1% increase in the minimum wage is associated with almost a 0.5% increase in real retail trade sales in the long run. While our findings rest on several statistical assumptions, there is strong evidence in support of the position that minimum wage strengthens aggregate consumer spending, and thereby the standard of living, economic growth and stability. This is a position that differs from the conclusions drawn from mainstream academic and policy debates on the economic usefulness and efficacy of minimum wage increases.

Most of the previous research examined the demand for alcohol consumed at the off-trade (consumed at home). However, some consumers might prefer to consume alcohol on-trade (away from home) or switch between on-trade and off-trade consumption as a reaction to price or income change. We estimate the Quadratic Almost Ideal Demand System consisting of three broad alcohol categories, consumed on-trade and off-trade, to derive own-price, cross-price, and income elasticities. Selectivity due to the high censoring is treated, and special attention is paid to quality-adjusted price. Beer consumption is the most responsive to income as well as own price changes, while spirits are the least responsive. The own-price elasticity of wine is –0.66 and –1.00 at on-trade and off-trade, respectively. Beer is more price responsive, spirits are less price responsive, and consumption reacts weaker in the off-trade market. Own-price elasticities of demand range between –1.20 and –0.41 at the off-trade and between –1.51 and –0.63 at the on-trade alcohol market. Increasing the price of wine in one market decreases wine consumption in another one. Between the two markets, wine and spirits are complementary, and wine and beer are substitutes in both markets.

Most hospital payment systems based on diagnosis-related groups (DRGs) provide payments for newly approved technologies. In Germany, they are negotiated between individual hospitals and health insurances. The aim of our study is to assess the functioning of temporary reimbursement mechanisms. We used multilevel logistic regression to examine factors at the hospital and state levels that are associated with agreeing innovation payments. Dependent variable was whether or not a hospital had successfully negotiated innovation payments in 2013 (n = 1532). Using agreement data of the yearly budget negotiations between each German hospital and representatives of the health insurances, the study comprises all German acute hospitals and innovation payments on all diagnoses. In total, 32.9% of the hospitals successfully negotiated innovation payments in 2013. We found that the chance of receiving innovation payments increased if the hospital was located in areas with a high degree of competition and if they were large, had university status and were private for-profit entities. Our study shows an implicit self-controlled selection of hospitals receiving innovation payments. While implicitly encouraging safety of patient care, policy makers should favour a more direct and transparent process of distributing innovation payments in prospective payment systems.

The increase in energy prices between 2004 and 2007 has several potential consequences for aggregate agriculture in the U.S. We estimate the derived input demand elasticities for energy as well as capital, labor, and materials using the differential supply formulation. Given that the derived input demand for energy is inelastic, it is more price-responsive than the other inputs. The results also indicate that the U.S. aggregate agricultural supply function is responsive to energy prices.

In this paper, we investigate the problem of how to combine operational losses collected from various banks of different sizes and loss reporting thresholds in order to estimate the distribution of operational loss severities for a bank of a given size. We model the severity of operational losses by using the extreme value theory to account for the reporting bias of the external data, and a regression analysis based on the GAMLSS framework to model the scaling properties of operational losses. In contrast to previous studies on the scaling problem, our analysis gives particular emphasis to the scaling properties of the tail of the loss distribution. Contrary to existing knowledge, we find that the size of a bank is an important determinant of the severity of operational losses and that the tail index of the distribution is negatively correlated with the size of the bank. The results indicate that for very large banks, distribution of the operational loss severity can be extremely heavy tailed (i.e. tail index less than 1), a finding which have significant implications for capital calculation as well as for risk management. Furthermore, we also demonstrate that the capital estimates provided by our model is consistent with the industry standards and the model can be used by individual banks to simulate data to complement their internal data.