Why Distinguish Between Statistics and Mathematical Statistics–The Case of Swedish Academia

A separation between the academic subjects statistics and mathematical statistics has existed in Sweden almost as long as there have been statistics professors. The same distinction has not been maintained in other countries. Why has it been kept for so long in Sweden, and what consequences may it have had? In May 2015, it was 100 years since Mathematical Statistics was formally established as an academic discipline at a Swedish university where Statistics had existed since the turn of the century. We give an account of the debate in Lund and elsewhere about this division during the first decades after 1900 and present two of its leading personalities. The Lund University astronomer (and mathematical statistician) C. V. L. Charlier was a leading proponent for a position in mathematical statistics at the university. Charlier's adversary in the debate was Pontus Fahlbeck, professor in political science and statistics, who reserved the word statistics for ‘statistics as a social science’. Charlier not only secured the first academic position in Sweden in mathematical statistics for his former PhD student Sven Wicksell but also demonstrated that a mathematical statistician can be influential in matters of state, finance as well as in different natural sciences. Fahlbeck saw mathematical statistics as a set of tools that sometimes could be useful in his brand of statistics. After a summary of the organisational, educational and scientific growth of the statistical sciences in Sweden that has taken place during the last 50 years, we discuss what effects the Charlier–Fahlbeck divergence might have had on this development.     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

A New Regulatory Paradigm for the Digital Sector in Europe

The new regulatory framework in the digital sector in Europe is likely to fall short of expectations. A fragmented digital market with insuffi cient incentives for investment is the probable outcome. European regulators should abandon their approach in favour of a new and more flexible set of ideas discussed in this paper.     

Leader Emergence in Nascent Venture Teams: The Critical Roles of Individual Emotion Regulation and Team Emotions

This study advances a theory of how different aspects of emotion regulation influence individual leader emergence in the intensely emotional context of nascent venture teams. Despite the growing amount of research on the role of leadership in the entrepreneurial process, the emergence of leaders in nascent venture teams has rarely been explored. Drawing on theories and research on leadership emergence and emotion regulation, we argue that the two aspects of emotion regulation (i.e., reappraisal and suppression) exert opposite effects on the degree to which nascent venture team members come to perceive an individual as a leader. We also theorize that team emotions arising from affective events moderate the relationship between reappraisal and leader emergence in such teams. Data from 103 nascent venture teams without prior leaders show a negative relationship between individuals’ trait disposition to suppress emotions and their emergence as leaders, and a positive relationship between their trait disposition to reappraise emotions and their emergence as leaders. Moreover, we find that negative team emotions magnify the positive association between reappraisal and leader emergence, while positive team emotions mitigate it. We discuss the implications of our findings for the literature on entrepreneurial leadership, entrepreneurial emotions, and leadership in general.     

NO‐ARBITRAGE PRICING UNDER SYSTEMIC RISK: ACCOUNTING FOR CROSS‐OWNERSHIP

We generalize Merton’s asset valuation approach to systems of multiple financial firms where cross‐ownership of equities and liabilities is present. The liabilities, which may include debts and derivatives, can be of differing seniority. We derive equations for the prices of equities and recovery claims under no‐arbitrage. An existence result and a uniqueness result are proven. Examples and an algorithm for the simultaneous calculation of all no‐arbitrage prices are provided. A result on capital structure irrelevance for groups of firms regarding externally held claims is discussed, as well as financial leverage and systemic risk caused by cross‐ownership.     

Hedging costs and joint determinants of premiums and spreads in structured financial products

Evaluating more than 317,000 discount certificates in the German secondary market, we find that premiums and spreads are endogenous and negatively related but depend on different key determinants. The fundamental determinants of the premiums are mainly profit-related, that is, dividends of the underlying, issuers’ credit risk, lifecycle effect, and competition, whereas hedging costs are less important. However, initial hedging costs (IHC) are priced into the premium in the case of large inventory changes. The spread is mostly determined by hedging costs and risk components, such as IHCs, rebalancing costs, volatility, scalper risk, and overnight gap risk—but also by dividends.     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.