Market impact: a systematic study of the high frequency options market

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders—large orders that are split into smaller pieces before being sent to the market—on one of the main Asian markets. In line with our previous work on the equity market [Said, E., Bel Hadj Ayed, A., Husson, A. and Abergel, F., Market impact: A systematic study of limit orders. Mark. Microstruct. Liq., 2018, 3(3&4), 1850008.], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well-understood equity market: Square-Root Law, Fair Pricing Condition and Market Impact Dynamics.     

Reinsurance Arrangements Maximizing Insurer's Survival Probability

The article concerns the problem of purchasing a reinsurance policy that maximizes the survival probability of the insurer. Explicit forms of the contracts optimal for the insurer are derived which are stop loss or truncated stop loss depending on the initial surplus, a quota to be spend on reinsurance and pricing rules of both the insurer and the reinsurer.     

The Challenge Facing SMEs in the Eu’s New Member States

The paper considers the main threats and opportunities for SMEs in new member states, arising out of EU membership, including the implications for SMEs at the micro level. Whilst accession-related changes have important potential implications for firms of all sizes, the distinctive size-related characteristics of SMEs affect their ability to identify, cope with and respond to new sources of threat and opportunity. These potential impacts face new member countries at a relatively early stage in the development of market-based systems, which still contain many deficiencies (particularly in institutional terms) and a SME sector with many characteristics that reflect its fledgling status.     

Linking thoughts to feelings: Investigating cognitive appraisals and consumption emotions in a mixed-emotions context

While emotions have been shown to have significant influence on various consumer behaviors, the cognitive appraisals linked to consumption emotions have not been fully explored. This research investigates how individuals' cognitive interpretations of situations correspond to the emotions they experience in these contexts. Using analysis of variance and multiple discriminant analysis, our results show a systematic relationship between cognitive appraisals and 10 consumption emotions. The author's findings offer theoretical insights into these consumption emotions, the appraisal/consumption emotion correspondence, basisversus subordinate-level category differences in emotions, and mixed consumption emotions. The findings provide a practical framework for academics and practitioners interested in better understanding and managing consumption emotions. Julie A. Ruth (Ph.D., University of Michigan) is an assistant professor of marketing in the School of Business—Camden at Rutgers University. Her research interests include affect and emotions, consumer relationships, and consumer response to brand strategies such as brand alliances and sponsorships. Frédéric F. Brunel (Ph.D., University of Washington) is an assistant professor of marketing in the School of Management at Boston University. His research interests include consumer perceptions of product design and aesthetics, consumer attitude and affect, and gender and sociocultural issues in consumption. Cele C. Otnes (Ph.D., University of Tennessee) is an associate professor of business administration in the College of Commerce and Business Administration at the University of Illinois, Urbana. Her research interests include consumer rituals, affect and consumer ambivalence, and gift exchange.     

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.     

The optimal portfolio for the two-pillar mandatory pension system: the case of Poland

In the recent decade, there has been observed across the Central and Eastern European states the regulatory trend towards the increase of the non-financial (first) pension pillar size at the expense of the financial (second) pillar. It tends to question the consequences of this shift for the future retirement benefits. Applying the portfolio approach we address this issue by running a series of simulations to find out how to allocate pension contributions between both pillars in an optimal way. Our study contributes to the existing literature as follows. First, we do not perform the assessment of the predetermined regulatory solutions, but we look for an optimal one. Moreover, we allow our optimal rule to be time-varying, if necessary, which would be a true novelty in this research area. Second, we do not base our estimates on historical trends; rather, we apply the long-term economy’s projection to account for the society’s ageing impact, which is a crucially important factor for the solvency of the pension system. Adapting some of the simulation assumptions to fit the Polish case, our results confirm that current regulations underestimate the role of the capital pillar and the optimal allocation between both pillars should be time-varying.