On stochastic aspects of impact modeling of the innovation incentive system and business internationalization: evidence from Portuguese SMEs

Multivariate normal distribution is base for many statistical techniques, including ordinary least square inference. Here we show that in order to make research on Internationalization of Companies, more flexible approach is needed, namely partial least squares (PLS). It is a nonparametric technique, used in Structural Equation Modeling (SEM), which makes no distributional assumptions and also may be applied with small sample sizes. In this study we discuss on regularity conditions for PLS from the perspective of semi-continuous covariance which fills the gap in the current studies. The stochastic aspects, especially those related to usage of PLS-SEM, can be well integrated to the topologically grounded regression, where jumps in the covariances can occur. The purpose of the research is to analyze and understand the impact of the Incentive System (IS) for Innovation, within the scope of the National Strategic Reference Framework (QREN) 2014-2020, on the Internationalization of Portuguese Small and Medium Enterprises (SMEs). We study stochastic aspects of theoretical model which aggregates the variables Product Innovation, Marketing Innovation, Organizational Innovation and Working Conditions as determinants of Internationalization of Companies. Data were collected based on a quantitative methodology, through a self-completion questionnaire using the Likert psychometric scale, which registered 120 participants. Organizational Innovation (exogenous latent construct) and Product Innovation have shown a statistically significant indirect effect on the Internationalization of Companies (endogenous latent construct) through Marketing Innovation. The latter has a direct effect on the Internationalization (target construct). However, Working Conditions has the greatest impact on Internationalization, meaning that measures such as increasing wages, decreasing the use of temporary work and precarious work conversion into labor effective relations have a very relevant direct effect on the Internationalization of Portuguese SMEs.FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (Center for Mathematics and Applications)

Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? This paper aims to investigate whether the cryptocurrency market has recently exhibited similarly nuanced mathematical properties as the much more mature equity market. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market, and to what extent.Comment: Equal contributio

Big Data Analytics in the Agribusiness Supply Chain Management

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness

The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages

The historical and geographical spread from older to more modern languages has long been studied by examining textual changes and in terms of changes in phonetic transcriptions. However, it is more difficult to analyze language change from an acoustic point of view, although this is usually the dominant mode of transmission. We propose a novel analysis approach for acoustic phonetic data, where the aim will be to statistically model the acoustic properties of spoken words. We explore phonetic variation and change using a time-frequency representation, namely the log-spectrograms of speech recordings. We identify time and frequency covariance functions as a feature of the language; in contrast, mean spectrograms depend mostly on the particular word that has been uttered. We build models for the mean and covariances (taking into account the restrictions placed on the statistical analysis of such objects) and use these to define a phonetic transformation that models how an individual speaker would sound in a different language, allowing the exploration of phonetic differences between languages. Finally, we map back these transformations to the domain of sound recordings, allowing us to listen to the output of the statistical analysis. The proposed approach is demonstrated using recordings of the words corresponding to the numbers from ``one'' to ``ten'' as pronounced by speakers from five different Romance languages.John Coleman appreciates the support of UK Arts and Humanities Research Council grant AH/M002993/1, “Ancient Sounds: mixing acoustic phonetics, statistics and comparative philology to bring speech back from the past”. John Aston appreciates the support of UK Engineering and Physical Sciences Research Council grant EP/K021672/2, “Functional Object Data Analysis and its Applications”

The Mathematics and Statistics of Quantitative Risk Management

It was the aim of this workshop to gather a multidisclipinary and international group of scientists at the forefront of research in econometrics, financial time series analysis, extreme value theory, financial mathematics, insurance mathematics and quantitative risk management. The heterogeneous composition of this group of researchers allowed one to discuss different facets of the mathematics and statistics of quantitative risk management, to communicate the state-of-the-art in the different areas, and to point towards new directions of research

Optimal uncertainty quantification of a risk measurement from a computer code

La quantification des incertitudes lors d'une étude de sûreté peut être réalisée en modélisant les paramètres d'entrée du système physique par des variables aléatoires. Afin de propager les incertitudes affectant les entrées, un modèle de simulation numérique reproduisant la physique du système est exécuté avec différentes combinaisons des paramètres d'entrée, générées suivant leur loi de probabilité jointe. Il est alors possible d'étudier la variabilité de la sortie du code, ou d'estimer certaines quantités d'intérêt spécifiques. Le code étant considéré comme une boîte noire déterministe, la quantité d'intérêt dépend uniquement du choix de la loi de probabilité des entrées. Toutefois, cette distribution de probabilité est elle-même incertaine. En général, elle est choisie grâce aux avis d'experts, qui sont subjectifs et parfois contradictoires, mais aussi grâce à des données expérimentales souvent en nombre insuffisant et entachées d'erreurs. Cette variabilité dans le choix de la distribution se propage jusqu'à la quantité d'intérêt. Cette thèse traite de la prise en compte de cette incertitude dite de deuxième niveau. L'approche proposée, connue sous le nom d'Optimal Uncertainty Quantification (OUQ) consiste à évaluer des bornes sur la quantité d'intérêt. De ce fait on ne considère plus une distribution fixée, mais un ensemble de mesures de probabilité sous contraintes de moments sur lequel la quantité d'intérêt est optimisée. Après avoir exposé des résultats théoriques visant à réduire l'optimisation de la quantité d'intérêt aux point extrémaux de l'espace de mesures de probabilité, nous présentons différentes quantités d'intérêt vérifiant les hypothèses du problème. Cette thèse illustre l'ensemble de la méthodologie sur plusieurs cas d'applications, l'un d'eux étant un cas réel étudiant l'évolution de la température de gaine du combustible nucléaire en cas de perte du réfrigérant.Uncertainty quantification in a safety analysis study can be conducted by considering the uncertain inputs of a physical system as a vector of random variables. The most widespread approach consists in running a computer model reproducing the physical phenomenon with different combinations of inputs in accordance with their probability distribution. Then, one can study the related uncertainty on the output or estimate a specific quantity of interest (QoI). Because the computer model is assumed to be a deterministic black-box function, the QoI only depends on the choice of the input probability measure. It is formally represented as a scalar function defined on a measure space. We propose to gain robustness on the quantification of this QoI. Indeed, the probability distributions characterizing the uncertain input may themselves be uncertain. For instance, contradictory expert opinion may make it difficult to select a single probability distribution, and the lack of information in the input variables affects inevitably the choice of the distribution. As the uncertainty on the input distributions propagates to the QoI, an important consequence is that different choices of input distributions will lead to different values of the QoI. The purpose of this thesis is to account for this second level uncertainty. We propose to evaluate the maximum of the QoI over a space of probability measures, in an approach known as optimal uncertainty quantification (OUQ). Therefore, we do not specify a single precise input distribution, but rather a set of admissible probability measures defined through moment constraints. The QoI is then optimized over this measure space. After exposing theoretical results showing that the optimization domain of the QoI can be reduced to the extreme points of the measure space, we present several interesting quantities of interest satisfying the assumption of the problem. This thesis illustrates the methodology in several application cases, one of them being a real nuclear engineering case that study the evolution of the peak cladding temperature of fuel rods in case of an intermediate break loss of coolant accident

Pricing and hedging in incomplete financial markets

In the practical part, Chapter 4 considers numerical methods for indifference pricing in a stochastic volatility model. In Chapter 5, a feasible procedure is developed for calculating the CVaR price in unit-linked insurance products under an additional assumption. This assumption is relaxed in Chapter 6.

D-optimal designs for complex Ornstein–Uhlenbeck processes

Complex Ornstein–Uhlenbeck (OU) processes have various applications in statistical modelling. They play role e.g. in the description of the motion of a charged test particle in a constant magnetic field or in the study of rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov used such a process to model the so-called Chandler wobble, small deviation in the Earth’s axis of rotation. In these applications parameter estimation and model fitting is based on discrete observations of the underlying stochastic process, however, the accuracy of the estimation strongly depend on the observation points. This paper studies the properties of D-optimal designs for estimating the parameters of a complex OU process with a trend. In special situations we show that in contrast with the case of the classical real OU process, a D-optimal design exists not only for the trend parameter, but also for joint estimation of the covariance parameters, moreover, these optimal designs are equidistant