Earnings announcement timing,uncertainty, and volatility risk premiums

We examine the relationship between firms’ quarterly earnings report timing and uncertainty before quarterly earnings announcements. Prior research provides conflicting predictions on how investor uncertainty and report timing are related. Using implied volatilities from equity options and the realized returns to straddle positions, we find evidence that uncertainty and volatility risk premiums are higher for firms that report later in the quarter. Further tests show that the increase in option premiums is unexplained by risk factors suggesting a mispricing by investors. These results are not associated with static firm-level factors and our findings are concentrated in high growth firms.     

Agrobiodiversity,farm profits and land fragmentation: Evidence from Bulgaria

This paper presents an empirical analysis of the role of land fragmentation, crop biodiversity and their interplay with farm profitability. Original primary data are drawn from a survey conducted in the Plodiv region of Bulgaria. The econometric results stress the ambiguous role of land fragmentation on farm profitability. On one hand, land fragmentation reduces farm profitability. On the other hand, land fragmentation fosters crop diversification. We also find that crop biodiversity plays a beneficial role in farm profitability. Policies that aim to increase land consolidation and reduce fragmentation may overlook the positive link between diversity and plot heterogeneity. Policies that encourage land consolidation should, therefore, consider the crucial role that this has on other variables such as farm biodiversity.     

Comparing Approximations for Risk Measures of Sums of Nonindependent Lognormal Random Variables

Abstract

In this paper we consider different approximations for computing the distribution function or risk measures related to a discrete sum of nonindependent lognormal random variables. Comonotonic upper and lower bound approximations for such sums have been proposed in Dhaene et al. (2002a,b). We introduce the comonotonic “maximal variance” lower bound approximation. We also compare the comonotonic approximations with two well-known moment-matching approximations: the lognormal and the reciprocal Gamma approximations. We find that for a wide range of parameter values the comonotonic “maximal variance” lower bound approximation outperforms the other approximations.     

Lognormal Mixed Models for Reported Claims Reserves

Abstract

Traditional claims-reserving techniques are based on so-called run-off triangles containing aggregate claim figures. Such a triangle provides a summary of an underlying data set with individual claim figures. This contribution explores the interpretation of the available individual data in the framework of longitudinal data analysis. Making use of the theory of linear mixed models, a flexible model for loss reserving is built. Whereas traditional claims-reserving techniques don’t lead directly to predictions for individual claims, the mixed model enables such predictions on a sound statistical basis with, for example, confidence regions. Both a likelihood-based as well as a Bayesian approach are considered. In the frequentist approach, expressions for the mean squared error of prediction of an individual claim reserve, origin year reserves, and the total reserve are derived. Using MCMC techniques, the Bayesian approach allows simulation from the complete predictive distribution of the reserves and the calculation of various risk measures. The paper ends with an illustration of the suggested techniques on a data set from practice, consisting of Belgian automotive third-party liability claims. The results for the mixed-model analysis are compared with those obtained from traditional claims-reserving techniques for run-off triangles. For the data under consideration, the lognormal mixed model fits the observed individual data well. It leads to individual predictions comparable to those obtained by applying chain-ladder development factors to individual data. Concerning the predictive power on the aggregate level, the mixed model leads to reasonable predictions and performs comparable to and often better than the stochastic chain ladder for aggregate data.     

Developing Accounting Regulations that Reflect Public Viewpoints: The Australian Solution to Differential Reporting

In this paper, we analyse the factors that have shaped the approach taken by the Australian Accounting Standards Board (AASB) in addressing the issue of differential reporting in Australia. In contrast to its early adoption of International Financial Reporting Standards in 2005, the AASB has signalled an independent approach to differential reporting. Still in progress at the time of writing, we show how the AASB's approach has been shaped by feedback from key stakeholder groups, as well as by influential individuals and key events. In the face of strongly held views on both sides of the debate, the Board has moved from reliance on discursive techniques to develop and justify proposed policies to embracing to a greater extent, the use of more objective research evidence to resolve the empirical questions presented in the public debate.