Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery

We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples

Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.Comment: 32 pages,13 figures, Intech Open Scienc

Gravity-induced ischemia in the brain and prone positioning for COVID-19 patients breathing spontaneously: still far from the truth!

International audienceGlobal mindset is usually considered as a positive skill or resource that helps individuals and companies succeed internationally. We argue that it is also a collective scheme of thought that brings some actors together and sets others apart. We investigate this perspective through a qualitative study of French MNC managers, internationalisation support providers, and SME owners and managers attempting to create or grow their business in China. We reveal that global mindset is a double‐edged concept: it is not solely an instrument for integration, but also a doxa, a particular viewpoint imposed to identify and reject outsiders through symbolic struggles. This alternative conceptualisation is necessary to rethink the social forces at work in the field of international business. It is also necessary to encourage educators and practitioners to acknowledge the struggles that result from the imposition of certain views and behaviours and to adapt education, support and training programs accordingly.L’objectif de cet article est de comprendre la dynamique des compétences interculturelles individuelles et collectives des prestataires dans l’expérience de service du client. Les résultats de l’étude de cas d’une business unit française prestataire de services linguistiques qui excelle en la matière montrent qu’une articulation eff icace des deux niveaux de compétence assure la satisfaction des clients et contribue à la compétitivité de l’entreprise

Clinical characteristics and outcomes of critically ill COVID-19 patients in Sfax, Tunisia

Background Africa, like the rest of the world, has been impacted by the coronavirus disease 2019 (COVID-19) pandemic. However, only a few studies covering this subject in Africa have been published. Methods We conducted a retrospective study of critically ill adult COVID-19 patients—all of whom had a confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection—admitted to the intensive care unit (ICU) of Habib Bourguiba University Hospital (Sfax, Tunisia). Results A total of 96 patients were admitted into our ICU for respiratory distress due to COVID-19 infection. Mean age was 62.4±12.8 years and median age was 64 years. Mean arterial oxygen tension (PaO2)/fractional inspired oxygen (FiO2) ratio was 105±60 and ≤300 in all cases but one. Oxygen support was required for all patients (100%) and invasive mechanical ventilation for 38 (40%). Prone positioning was applied in 67 patients (70%). Within the study period, 47 of the 96 patients died (49%). Multivariate analysis showed that the factors associated with poor outcome were the development of acute renal failure (odds ratio [OR], 6.7; 95% confidence interval [CI], 1.75–25.9), the use of mechanical ventilation (OR, 5.8; 95% CI, 1.54–22.0), and serum cholinesterase (SChE) activity lower than 5,000 UI/L (OR, 5.0; 95% CI, 1.34–19). Conclusions In this retrospective cohort study of critically ill patients admitted to the ICU in Sfax, Tunisia, for acute respiratory failure following COVID-19 infection, the mortality rate was high. The development of acute renal failure, the use of mechanical ventilation, and SChE activity lower than 5,000 UI/L were associated with a poor outcome

Signal-Based security in wireless networks

La sécurité des systèmes de communication mobiles/sans fil est problématique, car ceux-ci sont généralement construits suivant une topologie répartie ou arborescente. Les noeuds qui composent ces réseaux sont caractérisés par des ressources limitées et connectés généralement entre eux d'une manière ad-hoc sans l'aide d'une tierce personne de confiance. Les méthodes de sécurité matures issues du monde des réseaux filaires s'appuient souvent sur des procédés nécessitant des systèmes centralisés et des ressources importantes qui sont difficiles à mettre en place dans des réseaux à fortes contraintes. Dans le cadre de cette thèse, on propose de nouvelles solutions de sécurité qui exploitent les propriétés du médium électromagnétique et de l'interface de radiocommunication dans le but d'assurer des communications sécurisées. La thèse est structurée en deux parties. La première est dédiée au problème de génération de clés de cryptage en exploitant les propriétés des systèmes de communication à bande de fréquence ultra large (ULB). Trois phases sont nécessaires pour convertir le canal radio en clés secrètes: l'estimation du canal, la quantification et l'accord mutuel entre noeuds. Des expérimentations ont été effectuées pour valider les hypothèses sur lesquelles se fondent les méthodes de génération de clés (c.-à-d. la réciprocité et la décorrélation spatiale du canal). Notre étude a montré que la robustesse de ces techniques de sécurité repose sur le choix des algorithmes de numérisation utilisés pour la conversion de la signature du canal ULB vers un format de clé. Une solution adaptative d'extraction a été proposée, évaluée et testée. La robustesse contre les attaques de prédiction du canal a été également examinée. La deuxième partie traite le problème des intrusions illégitimes aux réseaux sans fil. Dans un premier temps, nous testons expérimentalement une méthode basée sur les variations électromagnétiques afin de détecter l'attaque d'écoute passive "eavesdropping" dans les réseaux de capteurs. Par la suite, nous présentons nos travaux concernant l'attaque relais qui est une variante de l'attaque de l'homme-du-milieu et qui est considérée comme un grand défi en particulier pour les systèmes d'authentification. Une nouvelle approche basée sur la détection de la variation des caractéristiques du bruit a été proposée. Des études théoriques et expérimentales ont été conduites pour vérifier la validité de la proposition dans les systèmes de communication de type RFID.Security in mobile wireless networks is considered a major impediment since these environments are a collection of low-cost devices. They are generally collected in ad hoc manner without the help of trusted third party. Therefore, conventional security methods are always inappropriate. Recent contributions propose to explore the radio communication interface and to turn the radio propagation problems into advantages by providing new alternatives to enhance security. In this thesis, we investigate the signal-based security concept and study its effectiveness through experiments. The first part of this dissertation discusses the problem of key generation from Ultra Wide Band channel. To derive secret keys from channel measurements three stages are required: channel estimation, quantization and key agreement. A campaign of measurements has been performed to confirm the fundamental channel requirements for key generation (i.e., the reciprocity and the spatial decorrelation). Results show that the robustness of such techniques depends on the channel information used as source of randomness as well as on the underlying algorithms. Analysis on the impact of each stage (i.e. the quantization and the key agreement) on the security has been presented. An adaptive key extraction method is proposed, performances are evaluated and robustness against deterministic channel prediction attacks is presented. The second part of the dissertation considers the problem of intrusion detection. First, we test a method based on electromagnetic radiation to discover the presence of an adversary in the receiver/emitter vicinity. Then, the problem of relay attack detection is investigated in RFID systems. A relay attack is a man-in-the middle attack, where the adversary is able to successfully pass the authentication phase by relaying messages between the legitimate verifier and the prover. A new solution based on the noise channel is proposed to detect this attack. Experimental and theoretical results are provided to test the effectiveness of the new proposition

Algorithmes Evolutionnaires: Prise en compte des contraintes et Application Réelle

The present work is a theoretical and experimental study in the evolutionary computation domain.The first part is an introduction to the artificial evolution with a synthesis of the principal approaches. The second part is theoretical study devoted to handling constraints in evolutionary computation. It presents an extensive review of previous constraint handling methods in the literature and their limitations. Two solutions are then proposed.The first aims to improve genetic operator exploration capacity for constrained optimization problems. It propose the logarithmic mutation operator conceived to explore both locally and globally the search space. The second solution introduce the original Adaptive Segregational ConstraintHandling Evolutionary Algorithm, which the main idea is to maintain population diversity. In order to achieve this goal, three main ingredients are used: An original adaptive penalty method that uses global information of the population to adjust the penalty coefficients; a constraint-driven recombination, where in some cases feasible individuals can only mate with infeasible individuals; a segregational selection that distinguishes between feasible and infeasible individuals to enhance the chances of survival of the feasible ones. Moreover, a niching method with an adaptive radius is added to ASCHEA in order to handle multimodal functions. Finally, to complete the ASCHEA system, a new equality constraint handling strategy is introduced, that reduce progressively the feasible domain of the explored solution in order to get is as close as possible to the reel domain (with null measure) at the end of the evolution.The third part is a case study tackling a real-word problem. The goal is to design the 2-dimensional profile of an optical lens (phase plate) in order to control focal-plane irradiance of some laser beam. The aim is to design the phase plate such that a small circular target on the focal plane is uniformally illuminated without energy loss.Cette thèse est une étude théorique et expérimentale dans le domaine du calcul évolutionnaire. La première partie propose une introduction au domaine de l'évolution artificielle avec une synthèse des principales approches.La deuxième partie est une étude théorique consacrée à la prise en compte des contraintes dans l'optimisation évolutionnaire. Son premier chapitre est une présentation détaillée des méthodes dans la littérature accompagnée d'une discussion de leurs avantages et leurs limites. Pour remédier à certaines limites, deux solutions sont proposées. La première vise l'amélioration de la capacité d'exploration des opérateurs de reproduction en présence des contraintes. Elle propose l'opérateur de mutation logarithmique conçu pour explorer à la fois localement et globalement l'espace de recherche. La deuxième est une proposition d'un nouvel algorithme pour la prise en compte des contraintes (ASCHEA), basé sur trois ingrédients: une pénalité adaptative ajustée automatiquement selon l'état courant de la population; un opérateur génétique fondé sur le concept de séduction où, dans certains cas, les individus faisables ne peuvent se reproduire qu'avec les infaisables dans le but d'explorer la frontière de la région faisable; un mécanisme de sélection ségrégationnelle en terme faisable/infaisable dont l'objectif est d'assurer un degré minimum de faisabilité de la population. Pour gérer les fonctions multimodales, une procédure de nichage a été introduite,dont le rayon est adapté automatiquement.Finalement, pour compléter le système ASCHEA, une nouvelle stratégie de prise en compte des contraintes d'égalité lui a été introduite, qui restreint progressivement le domaine de faisabilité des solutions explorées dans le but de l'approcher à la fin de l'évolution de l'espace réel de mesure nulle.La troisième partie est une étude d'une application réelle ayant pour objectif l'optimisation du profil d'une lame de phases pour le laser Méga-Joule. Le but est de minimiser la perte d'énergie sur le plan focal du système laser et la répartir uniformément sur la cible à illuminer

Recovering Volatility from Option Prices by Evolutionary Optimization

We propose a probabilistic approach for estimating parameters of an option pricing model from a set of observed option prices. Our approach is based on a stochastic optimization algorithm which generates a random sample from the set of global minima of the in-sample pricing error and allows for the existence of multiple global minima. Starting from an IID population of candidate solutions drawn from a prior distribution of the set of model parameters, the population of parameters is updated through cycles of independent random moves followed by "selection" according to pricing performance. We examine conditions under which such an evolving population converges to a sample of calibrated models. The heterogeneity..

Optimal Quantization : Evolutionary Algorithm vs Stochastic Gradient

International audienceWe propose a new method based on evolutionary optimization for obtaining an optimal L p-quantizer of a multidimen-sional random variable. First, we remind briefly the main results about quantization. Then, we present the classical gradient-based approach (this approach is well detailed in [2] and [7] for p=2) used up to now to find a "local" optimal L p-quantizer. Then, we give an algorithm that permits to deal with the problem in the evolutionary optimization framework and illustrate a numerical comparison between the proposed method and the stochastic gradient method. Finally, a numerical application to option pricing in finance is provided

Evolutionary Algorithms

Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning