Mutual-Fund Benchmarking and Market Bubbles: A Behavioral Approach

Dynamics of equity risk premium is not directly measurable on the market. Numerous studies and empirical research analyse its volatility also considering the time span, concluding that the dynamics of equity risk premium over time is inversely proportional to the economic cycle. This study analyses the passive role that, implicitly, would place institutional investors in such a context. In reality, savings management is delegated to a small number of professional operators (institutional investors), as opposed to pure theoretical models in which every person can act directly on the market thus ensuring unlimited price elasticity. Institutional investors should be rational and completely informed so that they can assume an anticyclical position on the market. Thus, supply and demand should quickly smooth over emerging price pressures and avoid price bubbles. We analyse one possible explanation for this situation not to occur, namely, that professionals suffer from operational limits that prevent them from doing their job in the best possible way. Using empirical evidence from the Italian Stock Exchange (Comit Index), we conclude that three factors reduce the freedom of institutional investors to manage their portfolios – the market target size, the fund structure, and the benchmarking – and discuss some implications for each of them.     

Waiting time to exhaust lottery numbers

Questions related to lotteries are usually of interest to the public since people think there is a magic formula which will help them to win lottery draws. This note shows how to compute the expected waiting time to observe specific numbers in a sequence of lottery draws and show that surprising facts are expected to occur.     

Dependence Between Two Diagnostic Tests with Copula Function Approach: A Simulation Study

The study of the dependence between two medical diagnostic tests is an important issue in health research since it can modify the diagnosis and, therefore, the decision regarding a therapeutic treatment for an individual. In many practical situations, the diagnostic procedure includes the use of two tests, with outcomes on a continuous scale. For final classification, usually there is an additional “gold standard” or reference test. Considering binary test responses, we usually assume independence between tests or a joint binary structure for dependence. In this article, we introduce a simulation study assuming two dependent dichotomized tests using two copula function dependence structures in the presence or absence of verification bias. We compare the test parameter estimators obtained under copula structure dependence with those obtained assuming binary dependence or assuming independent tests.     

A class of bivariate regression models for discrete and/or continuous responses

ABSTRACT

A general class of models for discrete and/or continuous responses is proposed in which joint distributions are constructed via the conditional approach. It is assumed that the distributions of one response and of the other response given the first one belong to exponential family of distributions. Furthermore, the marginal means are related to the covariates by link functions and a dependency structure between the responses is inserted into the model. Estimation methods, diagnostic analysis and a simulation study considering a Bernoulli-exponential model, a particular case of the class, are presented. Finally, this model is used in a real data set.     

On time-irreversibility and other non-linear features in time series

Time-irreversibility, asymmetry of the distribution, and the occurrence of sudden bursts are considered, amongst others, as non-linear features in time series modeling. The implication is often made that time series showing these features must be analyzed using non-linear models. In contrast, this paper shows that time-irreversible asymmetric time series showing certain types of sudden bursts may be generated by linear models with adequate input sequences. Thus some non-linear time series features may be caused by the pattern in the input sequence rather than by non-linearity in the model. Examples are considered to illustrate the situation.     

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.     

A Bayesian analysis for the Block and Basu bivariate exponential distribution in the presence of covariates and censored data

In this paper, we introduce a Bayesian Analysis for the Block and Basu bivariate exponential distribution using Markov Chain Monte Carlo (MCMC) methods and considering lifetimes in presence of covariates and censored data. Posterior summaries of interest are obtained using the popular WinBUGS software. Numerical illustrations are introduced considering a medical data set related to the recurrence times of infection for kidney patients and a medical data set related to bone marrow transplantation for leukemia.     

The Role of Survey Data in Microsimulation Models for Social Policy Analysis

Questions about the impact of social policies cannot be answered solely on the basis of official statistics made available in tabular format. The access to microdata allows policy analysts to answer these questions with a larger set of analytical tools, in particular microsimulation models. This paper examines the interface between survey data and microsimulation models. We review different types of microsimulation models (static and dynamic) and their data requirements. We then restrict the attention to survey-based microdata, and examine issues in survey design, questionnaire content, data quality, and dissemination policy that are important from the perspective of microsimulation.     

The sovereign crisis and Italy’s potential output

We evaluate the effects of the European sovereign crisis on Italian potential output (natural output, in the absence of nominal rigidities) by simulating a New Keynesian model. Our results are as follows. First, the 2011–2013 recession subtracted 1.6 percentage points from potential output growth and widened the output gap. Second, the 2013 reforms limited the reduction in output capacity to 1.4 points and enhanced long-run growth by 3. Third, once a balanced budget is achieved in the medium term, reductions in either labor or capital income taxes would boost potential output growth by 0.2 points per year.