Feeding management of dairy cattle affect grassland dynamics in an alpine pasture

The effect of different supplementary feeding rates for grazing cattle on high-altitude pastures dynamics was evaluated. A field experiment was carried out during three years in a subalpine pasture area of the Eastern Alps. The investigated pasture area was 40?ha, located between 1820 and 2230?m?a.s.l. Two paddocks were chosen in the experiment and two herds of 12 cattle each were kept in the two enclosures for 5 weeks. For the first herd (HS), the supplementary feeding rate was 4.8?kg?OM?head^?1 per day, while for the second herd (LS), the rate was 1.6?kg?OM?head^?1 per day. The amount of herbage consumed by each cattle was determined using the n-alkane technique. To monitor the pasture vegetation dynamics, eight metal exclusion cages were placed in each paddock to determine herbage growth, utilization rates, vegetation composition and animal grazing selectivity. Grazing behaviour of dairy cattle, in terms of herbage intake and species selection was affected by the different feeding rates. Cattle grazing Paddock HS consumed 1.9?kg?OM?day^?1 of herbage less than Paddock LS. In the LS paddock, cattle grazed higher phytomass rates. When the animals were fed by higher concentrate rates, a more selective grazing seemed to significantly increase the pasture necromass component. The lower grazing selectivity favoured the development of species as Nardus stricta and Deschampsia caespitosa, which are well known for their low palatability. Distinct vegetation dynamic patterns were observed, with a reduction of hair grass and an increase of legumes in the Paddock LS.     

Mechanics of good trade execution in the framework of linear temporary market impact

We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; this is paramount in volatile regimes, where price trajectories can considerably deviate from their expected value. Remarkably, however, the implementation of our strategies does not require the full specification of an SDE evolution for the traded asset price, making them robust across different models. Moreover, rather than minimising the expected trading cost, good trade execution strategies minimise trading costs in a pathwise sense, a point of view not yet considered in the literature. The mathematical apparatus for such a pathwise minimisation hinges on certain random Young differential equations that correspond to the Euler–Lagrange equations of the classical Calculus of Variations. These Young differential equations characterise our good trade execution strategies in terms of an initial value problem that allows for easy implementations.     

ARBITRAGE‐FREE BILATERAL COUNTERPARTY RISK VALUATION UNDER COLLATERALIZATION AND APPLICATION TO CREDIT DEFAULT SWAPS

We develop an arbitrage‐free valuation framework for bilateral counterparty risk, where collateral is included with possible rehypothecation. We show that the adjustment is given by the sum of two option payoff terms, where each term depends on the netted exposure, i.e., the difference between the on‐default exposure and the predefault collateral account. We then specialize our analysis to credit default swaps (CDS) as underlying portfolios, and construct a numerical scheme to evaluate the adjustment under a doubly stochastic default framework. In particular, we show that for CDS contracts a perfect collateralization cannot be achieved, even under continuous collateralization, if the reference entity’s and counterparty’s default times are dependent. The impact of rehypothecation, collateral margining frequency, and default correlation‐induced contagion is illustrated with numerical examples.     

The value relevance of IFRS in the European banking industry

The main purpose of the paper is to investigate the market valuation of accounting information in the European banking industry before and after the adoption of IFRS, the latest version of International Accounting Standards. In a value relevance framework, we apply panel methods to a multiplicative interaction model, in which the partial effects of earnings and book value on share prices are conditional on the adoption of IFRS. According to our evidence, the IFRS introduction enhanced the information content of both earnings and book value for more transparent banks. By contrast, less transparent entities did not experience significant increase in the value relevance of book value.     

The Effects of Screening and Monitoring on Credit Rationing of SMEs

In this paper, we seek to empirically assess which determinants of the capability and incentives of banks to screen and monitor firms are significant in explaining credit rationing to Italian SMEs. After testing for the presence of non‐random selection bias and the potential endogeneity of some determinants of interest, the probit model results we obtain suggest that the average banking size and the multiple banking relationship phenomenon are statistically significant factors affecting credit rationing, presumably through their impact on the aforementioned banks' capability and incentives. Other potential determinants of banks' incentives to monitor and screen, such as local banking competition and firm' capacity to collateralize, are never significant. However, when we split the sample according to the level of competition in credit markets, we find that the estimated marginal effects of all significant determinants of interest are larger in absolute value than those obtained when using the whole sample.     

Predicting Household Poverty: A Methodological Note with a Kenyan Example

Household surveys provide data that is used for identifying and measuring the poverty status of households and individuals. However, carrying out such surveys is expensive, especially in poor developing countries. Thus it is important to make maximum use of the available survey data in developing countries, especially in sub‐Saharan Africa, where such data are expensive to collect and analyse. This paper develops a simple method for using poverty indices derived from survey data for a given year, to predict poverty rates for subsequent periods without having to conduct a new household survey. We illustrate the workings of the method with data from Kenyan household surveys for 1994 and 1997.     

Price discrimination and efficient matching

This paper considers the problem of a monopoly matchmaker that uses a schedule of entrance fees to sort different types of agents on the two sides of a matching market into exclusive meeting places, where agents randomly form pairwise matches. We make the standard assumption that the match value function exhibits complementarities, so that matching types at equal percentiles maximizes total match value and is efficient. We provide necessary conditions and sufficient conditions for the revenue-maximizing sorting to be efficient. These conditions require the match value function, modified to incorporate the incentive cost of eliciting private type information, to exhibit complementarities in types.JEL Clalssification Numbers C7, D4We thank Jonathan Levin, Tracy Lewis, and the seminar audience at University of California at Los Angeles, University of British Columbia, Duke University, and 2003 Econometric Society North American Summer Meeting for comments and suggestions. We are also grateful for helpful suggestions from the referees and the Editor     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.