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We introduce a theoretical model of executives with insider information (insider-executives) granted incentivizing executive stock options (ESO). We show that while insider-executives optimize their wealth, using their insider information nullies ESO incentives, misaligning their and shareholders' interests. We oer realigning methods: granting insider-executives reload stock options (RSO) and imposing blackout trading periods (blackouts). Eective blackouts keep insider-executives incentivized without being overly restrictive, i.e., without reducing their welfare below that of outsiders. We introduce RSO pricing for insider-executives and oer policy implications: reestablishing the currently out-of-favor RSO, and allowing rms, not regulators, to set blackout periods on securities they issue.

This paper studies the multi-period mean-variance portfolio allocation problem with transaction costs. Many methods have been proposed these last years to challenge the famous uni-period Markowitz strategy.But these methods cannot integrate transaction costs or become computationally heavy and hardly applicable. In this paper, we try to tackle this allocation problem by proposing an innovative approach which relies on representing the set of admissible portfolios by a finite dimensional Wiener chaos expansion. This numerical method is able to find an optimal strategy for the allocation problem subject to transaction costs. To complete the study, the link between optimal portfolios submitted to transaction costs and the underlying risk aversion is investigated. Then a competitive and compliant benchmark based on the sequential uni-period Markowitz strategy is built to highlight the efficiency of our approach.

International audience

In the problem of online portfolio selection as formulated by Cover (1991), the trader repeatedly distributes her capital over $ d $ assets in each of $ T > 1 $ rounds, with the goal of maximizing the total return. Cover proposed an algorithm, termed Universal Portfolios, that performs nearly as well as the best (in hindsight) static assignment of a portfolio, with an $ O(d\log(T)) $ regret in terms of the logarithmic return. Without imposing any restrictions on the market this guarantee is known to be worst-case optimal, and no other algorithm attaining it has been discovered so far. Unfortunately, Cover's algorithm crucially relies on computing certain $ d $-dimensional integral which must be approximated in any implementation; this results in a prohibitive $ \tilde O(d^4(T+d)^{14}) $ per-round runtime for the fastest known implementation due to Kalai and Vempala (2002). We propose an algorithm for online portfolio selection that admits essentially the same regret guarantee as Universal Portfolios -- up to a constant factor and replacement of $ \log(T) $ with $ \log(T+d) $ -- yet has a drastically reduced runtime of $ \tilde O(d^2(T+d)) $ per round. The selected portfolio minimizes the current logarithmic loss regularized by the log-determinant of its Hessian -- equivalently, the hybrid logarithmic-volumetric barrier of the polytope specified by the asset return vectors. As such, our work reveals surprising connections of online portfolio selection with two classical topics in optimization theory: cutting-plane and interior-point algorithms.

International audience; To cope with the weakened financial system, the innovative ecosystem of cryptocurrencies and the blockchain seem to emerge as alternative solutions. While they offer a decentralized governance, cryptocurrencies also provide exposure to social dimensions. We thus pose the following research question: is there a virtuous circle between extra-financial and financial performance for cryptocurrencies? To answer this question, we analyze the social performance, an extra-financial dimension, through miners’ compensation. Using a sample of cryptocurrencies between 2015 and 2021, we follow two econometric modeling approaches, namely portfolios constituted based on miner compensation and a panel regression model. This dual approach allows an analysis of the relationship at the macro and microeconomic level as well as enhancing the robustness of our inferences. Our results show that the social and financial performance relationship is positive. Thus, enhanced value sharing in the cryptocurrency landscape appears synonymous of greater financial performance.; Pour faire face au système financier fragilisé, l’écosystème innovant des cryptomonnaies et de la blockchain semble être une alternative. En plusd’une gouvernance décentralisée, les cryptomonnaies offrent une exposition aux dimensions sociales. Nous posons ainsi la question suivante :existe-t-il un cercle vertueux entre performance extra-financière et financière pour les cryptomonnaies ? Nous analysons ainsi la performancesociale, une dimension extra-financière, à travers la rémunération des mineurs. En utilisant un échantillon de cryptomonnaies entre 2015et 2021, nous réalisons une analyse de portefeuille constitué en fonction de la rémunération des mineurs et utilisons un modèle en données de panel.Cette double approche permet d’étudier la relation à l’échelle macro et microéconomique, renforçant la robustesse des inférences. Nos résultats montrent que la relation performance sociale et boursière est positive. Ainsi, un partage de la valeur ajoutée accrue pour une cryptomonnaiesemble être synonyme de performance financière plus élevée.

International audience

International audience; This study investigates whether investor attention to the fossil fuel divestment (FFD) movement is related to the stock returns of firms involved in extracting fossil fuels. We consider three complementary indicators of investor attention to the FFD movement: (1) the US weekly Google Search Volume Index on the topic "fossil fuel divestment," (2) the US weekly media coverage of fossil fuel divestment, and (3) the number of weekly visits to the "fossil fuel divestment" page on Wikipedia. Based on a sample of weekly returns on 1,850 US firms over the period 2012-2020, our econometric estimations report a positive relationship between investor attention to FFD and excess stock returns for US fossil fuel-related firms. Therefore, contrary to what the FFD campaigners might expect, the stigmatization of the fossil fuel industry does not drive down the stock returns on fossil fuel-related firms.

International audience; We implement a new framework to mitigate the errors-in-variables (EIV) problem in the estimation of asset pricing models. Considering an international data of portfolio stock returns from 1990 to 2021 widely used in empirical studies, we highlight the importance of the estimation method in time-series regressions. We compare the traditional Ordinary-Least Squares (OLS) method to an alternative estimator based on a Compact Genetic Algorithm (CGA) in the case of the CAPM, three-, and five-factor models. Based on intercepts, betas, adjusted R2 , and the Gibbons, Ross and Shanken (1989) test, we find that the CGA-based method outperforms overall the OLS for the three asset pricing models. In particular, we obtain less statistically significant intercepts, smoother R2 across different portfolios and lower GRS test statistics. Specifically, in line with Roll's critique (1977) on the unobservability of the market portfolio, we reduce the attenuation bias in market risk premium estimates. Moreover, our results are robust to alternative methods such as Instrumental Variables estimated with Generalized-Method of Moments (GMM). Our findings have several empirical and managerial implications related to the estimation of asset pricing models as well as their interpretation as a popular tool in terms of corporate financial decision-making.

We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper, we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets' returns, we describe the relationship between portfolios' return and volatility by means of a copula, without making any assumption on investors' strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data the indicator detects all past crashes in the cryptocurrency market and from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant the individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on statistical properties of portfolios, and we show how they can be computed efficiently.

The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian nature of the fBm to forecast future states of the process and make statistical arbitrages. We provide new insights into forecasting an fBm, by proposing theoretical formulas for accuracy metrics relevant to a systematic trader, from the hit ratio to the expected gain and risk of a simple strategy. In addition, we answer some key questions about optimizing trading strategies in the fBm framework: Which lagged increments of the fBm, observed in discrete time, are to be considered? If the predicted increment is close to zero, up to which threshold is it more profitable not to invest? We also propose empirical applications on high-frequency FX rates, as well as on realized volatility series, exploring the rough volatility concept in a forecasting perspective.

The treatment of uncertainties is a fundamental problem in the financial context, and more precisely in portfolio optimisation. The variables studied are often time dependent, with heavy tails. In this thesis, we are interested in tools allowing to take into account uncertainties in its main forms: statistical uncertainties, parametric uncertainties and model error, keeping in mind that we wish to apply them to the financial context.The first part is devoted to the establishment of concentration inequalities for variables with heavy tailed distributions. The objective of these inequalities is to quantify the confidence that can be given to an estimator based on observations of finite size. In this thesis, we establish new concentration inequalities which include the case of estimators with log-normal distribution.In the second part, we discuss the impact of the model error for the estimation of the covariance matrix on stock returns, under the assumption that there is an instantaneous covariance process between the returns whose present value depends on its past values. One can then explicitly construct the best estimate of the covariance matrix for a given time and investment horizon, and we show that this estimate gives the best performance with high probability in the minimum variance portfolio framework.In the third part, we propose an approach to estimate the Sharpe ratio and the portfolio allocation when they depend on parameters considered uncertain. Our approach involves the adaptation of a stochastic approximation technique for the computation of the polynomial decomposition of the quantity of interest.Finally, in the last part of this thesis, we focus on portfolio optimization with target distribution. This technique can be formalised without the need for any model assumptions on returns. We propose to find these portfolios by minimizing divergence measures based on kernels or optimal transport. Since these divergence measures can be unbounded and have not been studied much yet in the unbounded kernel case, we establish new convergence guarantees based on concentration inequalities.; Le traitement des incertitudes est un problème fondamental dans le contexte financier. Les variables étudiées sont souvent dépendantes du temps, avec des queues de distribution épaisses. Dans cette thèse, on s'intéresse à des outils permettant de prendre en compte les incertitudes sous ses formes principales: incertitudes statistiques, paramétriques et erreur de modèle, tout en gardant en tête qu’on souhaite les appliquer à ce contexte.La première partie est consacrée à l’établissement d’inégalités de concentration dans le cadre de variables à queues épaisses. L’objectif de ces inégalités est de quantifier quelle confiance on peut donner à un estimateur basé sur une taille finie d'observations. Dans cette thèse, nous établissons de nouvelles inégalités de concentration, qui couvrent notamment le cas d'estimateur à distribution log-normale.Dans la seconde partie, on traite de l'impact de l'erreur de modèle pour l'estimation de la matrice de covariance sur des rendements boursiers, sous hypothèse qu’il existe un processus de covariance instantanée entre les rendements dont la valeur présente dépend de sa valeur passée. On peut alors construire explicitement la meilleure estimée de la matrice de covariance pour un instant et un horizon d'investissement donnés, et montrer qu'elle fournie la variance réalisée la plus faible avec grande probabilité dans le cadre du portefeuille minimum variance.Dans la troisième partie, on propose une approche pour estimer le ratio de Sharpe et l'allocation de portefeuille lorsqu'ils dépendent de paramètres jugés incertains. Notre approche passe par l'adaptation d'une technique d'approximation stochastique pour le calcul de la décomposition en polynômes du chaos de la quantité d'intérêt.Enfin, dans la dernière partie de cette thèse, on s'intéresse à l'optimisation de portefeuille avec distribution cible. Cette technique peut être formalisée sans avoir recours à aucune hypothèse de modèle sur les rendements. Nous proposons de trouver ces portefeuilles en minimisant des mesures de divergence basées sur les fonctions noyau et la théorie du transport optimal.

International audience; Since cryptocurrencies were first created, the related markets have been known for their fluctuations, whether in terms of high volatility or illiquidity. Partially for those reasons, public authorities and regulators around the world have frequently attempted to regulate those markets. We aim in our study to examine whether cryptocurrency traders perceive market regulation in a beneficial way. Using an event study methodology for daily data covering the 2015-2019 period, we assess how regulatory news and events have affected returns in cryptocurrency markets. We further assess whether financial cryptocurrency characteristics and in particular their liquidity can explain cross-sectional variations in cryptocurrency return reactions. The results suggest that events that increase the probability of regulation adoption are associated with negative abnormal returns for the cryptocurrencies concerned. We also find that the magnitude of the return reactions is not the same across all the cryptocurrencies in our sample. We show that investors reacted less negatively for the most illiquid cryptocurrencies and for those that incurred more information asymmetry risk. Finally, we analyze a longer-term effect of regulatory events by studying the performance of cryptocurrencies. The risk-adjusted return in the preevent period is positive and significant, but it appears not to be significantly different from zero in the post-event period.

International audience; Gold is considered as a hedge against inflation, it offers an opportunity for portfolio diversification. This paper examines the recent evolution of seasonal anomalies in the Chinese Gold market. It studies the day of the week effect and the monthly effect through gold prices at the Shanghai Gold Exchange (SGE) over the 2002 to 2016 period. We investigate seasonal patterns in economically favorable times and unfavorable times by using a UCM model and an ARCH model. The reforms in regulations have rendered the Chinese financial market more efficient, in such cases; we expect an alteration in seasonal anomalies in the Chinese Gold market. However, it would seem that seasonality does exist in the Chinese Gold Market. The Monday returns have been positive and the Tuesday returns have been negative for the whole period. We also highlight that January and February generate the best returns. The return in the middle of the year is negative. This paper contributes to the existing finance literature by investigating the anomalies during the recent period. Although in the Chinese stock market, the seasonal anomalies persist, the index may be efficient despite the regularity in price formation, in this case, a study over a more recent period is necessary.

This paper aims to develop a feasible estimator of the Sharpe ratio that the investor would expect from estimated efficient portfolios. Based on the analytical expression of the expected Sharpe ratio, we construct an estimator that captures all the errors involved in the estimated efficient portfolios. We conduct a simulation study and find that our estimator delivers the lowest mean square error with comparison to existing estimators. Our result is robust to sample size, to number of assets and to non-normality. It works well, particularly, with short sample sizes. The superior performance of the proposed estimator is confirmed through empirical analysis. The ex-ante method developed in this work allows the investor to assess the value of efficient portfolios before investing capital.

International audience; In this paper, we propose a new algorithm to estimate all the parameters of a Self Exited Threshold AutoRegressive (SETAR) model from an observed time series. The aim of this algorithm is to relax all the hypotheses concerning the SETAR model for instance, the knowledge (or assumption) of the number of regimes, the switching variables, as well as of the switching function. For this, we reverse the usual framework of SETAR model identification of the previous papers, by first identifying the AR models using array clustering (instead of the switching variables and function) and second the switching conditions (instead of the AR models). The proposed algorithm is a pipeline of well-known algorithms in image/data processing allowing us to deal with the statistical non-stationarity of the observed time series. We pay a special attention on the results of each step over the possible discrepancies over the following step. Since we do not assume any SETAR model property, asymptotical properties of the identification results are difficult to derive. Thus, we validate our approach on several experiment sets. In order to assess the performance of our algorithm, we introduce global metrics and ancillary metrics to validate each step of the proposed algorithm.

This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a discrete-time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate, and credit intensity fluctuations, and show by numerical results that the optimal strategy improves both the solvency and asset returns of the institution compared to a standard institutional investor’s asset allocation.

In dynamic minimum variance portfolio, we study the impact of the sequence of covariance matrices taken in inputs, on the realized variance of the portfolio computed along a sample market path. The allocation of the portfolio is adjusted on a regular basis (every H days) using an updated covariance matrix estimator. In a modelling framework where the covariance matrix of the asset returns evolves as an ergodic process, we quantify the probability of observing an underperformance of the optimal dynamic covariance matrix compared to any other choice. The bounds depend on the tails of the returns, on the adjustment period H, and on the total number of rebalancing times N. These results provide asset managers with new insights into the optimality of their choice of covariance matrix estimators, depending on the depth of the backtest N H and the investment period H. Experiments based on GARCH modelling support our theoretical results.

In this thesis, we studied the problem of the first passage time in options pricing which are financial products allowing the transfer of risks related to the stochastic dynamics of financial markets. In this framework, we developed a new derivative called Geometric Istanbul Option. This option is an extension of Asian options since its price depends onthe average price of the underlying asset over a random period of time. Moreover, as in the case of barrier options, the payoff of a geometric Istanbul option depends on the first passage time of a geometric Brownian motion since we assume that the economic model chosen for the study is the Black-Scholes (1973) model. An approximation formula is given in closed-form for a geometric Istanbul option in both cases of call and put options. In our numerical approaches, we adopt the Monte-Carlo technique with reduction of the variance of the estimator obtained by the control variates method. This numerical approach has allowed us to validate the effciency of our analytical approximation formulas since the option price obtained numerically is very close to the one we propose for various input parameters such as interest rate, volatility, initial price, strike price and maturity date. Furthermore, we have shown, numerically, that our option price is relatively cheaper than that of an arithmetic Istanbul option that was rst proposed by Michel Jacques in 1979.; Dans cette thèse, nous avons étudié le problème de l’instant de premier passage dans la tarification des options qui sont des produits financiers permettant le transfert des risques liés à la dynamique stochastique des marchés financiers. Dans ce contexte, nous avons développé un nouveau produit dérivé appelé option géométrique d'Istanbul. Cette option est une extension des options asiatiques car son prix dépend du prix moyen de l'actif sous-jacent sur une période de temps aléatoire. De plus, comme dans le cas des options à barrière, le gain d'une option géométrique d'Istanbul dépend de l’instant de premier passage d'un mouvement brownien générique puisque nous supposons que le modèle économique choisi pour l'étude est celui de Black-Scholes(1973). Une formule d'approximation est donnée sous forme fermée pour une option géométrique d'Istanbul dans le cas d'une option d'achat (Call) et également pour une option de vente (Put). Dans nos approches numériques, nous adoptons la technique de Monte-Carlo avec réduction de la variance de l'estimateur obtenu par la méthode des variables de contrôle. Cette approche numérique nous a permis de valider l'efficacité de nos formules d'approximation analytiques puisque le prix de l'option obtenu numériquement est très proche de celui que nous proposons pour divers paramètres d'entrée tels que le taux d'intérêt, la volatilité, le prix initial, le prix d'exercice et la date d'échéance. Par ailleurs, nous avons montré, numériquement, que le prix de notre option est relativement moins cher que celui d'une option arithmétique d'Istanbul proposée pour la première fois par Michel Jacques en 1979.

International audience; This paper presents the findings of our study on forecasting the levelized cost of electricity of different electricity generation technologies including renewable energy (solar PV, hydropower, biomass and wind) until 2030. Usually, the cost of electrical generation from renewable and conventional sources are compared by using the LCOE method (Levelized Cost Of Energy). In general, the LCOE is calculated based on the assumptions for each technology. In this paper, we evaluate the LCOE of the various electricity generation techniques for the case of Lebanon. The study predicts future evolution of these costs in Lebanon until 2030 according to specic assumptions related to each technology and taking into account the triple aspects : technical, economic and environmental. The generation costs don't depend only on investment, maintenance and fuel costs, but also on the cost of carbon emitted by each technology per unit of the electricity produced. We focus our analysis on these assumptions and the impact of their variability on the cost by using sensitivity analysis. Our study shows that by 2030, renewable generation technologies will remain more expensive than fossil-fueled technologies. However, within certain measures taken by the State, as integration of a carbon price, renewable energy resources could emerge strongly in the electricity generation investment planning and become much competitive.; Cet article présente des prévisions du coût moyen de production de cinq technologies qui pourraient contribuer d’une manière significative à la production d'électricité au Liban : les centrales à gaz (cycle ouvert et cycle combiné), les centrales solaires (technologie photovoltaïque), les parcs éoliens, les centrqles hydroélectriques et les centrales à déchets. Habituellement, la comparaison du coût des sources renouvelables et conventionnelles se fait en utilisant la méthode du LCOE (Levelized Cost Of Energy). Dans cet article, nous estimons l'évolution future de ces coûts au Liban jusqu'en 2030 selon des hypothèses spécifiques liées à chaque technologie et en tenant compte du triple aspect: technique, économique et environnemental. Les coûts de production ne dépendent pas seulement des coûts d'investissement, d'entretien et de carburant, mais aussi du coût du carbone émis par unité d'électricité produite. Nous nous focalisons ainsi dans notre analyse sur l'impact de leur variabilité sur le coût en utilisant des analyses de sensibilité. Les principaux résultats montrent que d'ici 2030, les technologies de production renouvelable resteront plus chères que les technologies à combustibles fossiles. Cependant, dans le cadre de certaines mesures prises par l'État, les énergies renouvelables pourraient émerger fortement dans la planification des investissements et devenir très compétitives dans le contexte libanais.

International audience; We aim to advance our understanding of the adverse effects of extreme temperatures by examining the extent to which high temperatures affect stock market activity. We address this question by analyzing the trading volumes on the French stock market on days when the weather in Paris is excessively hot over the period 1995-2019. Our empirical analyses show that, on average, trading volumes fall significantly (between 4 percent and 10 percent) when maximum daily temperatures exceed 30°C (86°F). The observed negative association is remarkably robust to a battery of alternative analyses such as bin tests, event studies, and time-series regressions controlling for any seasonal effects and financial market conditions. From a theoretical perspective, this study contributes to the literature on behavioral finance by demonstrating the existence of a "hot weather" effect on financial markets. It also offers important managerial and public policy implications.

International audience; This study examines the relationship between corporate greenwashing and product market competition (PMC). Using an unbalanced panel of 324 US firms over the 2005-2015 period, we find that the negative impact of PMC on greenwashing is conditional on the level of environmental costs. Our results suggest that PMC is an effective disciplinary mechanism for achieving economic efficiency --in the case of firms featuring a high level of environmental costs-- through an increase in the disclosure of reliable and material information.

We consider the classical problem of optimal portfolio construction with the constraint that no short position is allowed, or equivalently the valid equilibria of multispecies Lotka-Volterra equations with self-regulation in the special case where the interaction matrix is of unit rank, corresponding to species competing for a common resource. We compute the average number of solutions and show that its logarithm grows as $N^\alpha$, where $N$ is the number of assets or species and $\alpha \leq 2/3$ depends on the interaction matrix distribution. We conjecture that the most likely number of solutions is much smaller and related to the typical sparsity $m(N)$ of the solutions, which we compute explicitly. We also find that the solution landscape is similar to that of spin-glasses, i.e. very different configurations are quasi-degenerate. Correspondingly, "disorder chaos" is also present in our problem. We discuss the consequence of such a property for portfolio construction and ecologies, and question the meaning of rational decisions when there is a very large number "satisficing" solutions., Comment: 37 pages, 7 figures

There is an important literature focused on profit warnings and its impact on stock returns. We provide evidence from Moroccan stock market which aims to become an African financial hub. Despite this practical improvement, academic researches that focused on this market are scarce and our study is a first investigation in this context. Using the event study methodology and a sample of companies listed in Casablanca Stock Exchange, we examined whether the effect of qualitative warning is more negative compared to quantitative warnings in a short event window. Our empirical findings show that the average abnormal return on the date of announcement is negative and statistically significant. The magnitude of this negative abnormal return is greater for qualitative warnings than quantitative ones.

Socially Responsible Investment (SRI), known for ten years a remarkable growth, it is a multifaceted investment style that integrates the principles of sustainable development in the investment process and traditional portfolio management. SRI can take three forms among which we can mention SRI funds, this strategy is to select securities in an investment fund on the basis of environmental, social and governance criteria.Recognizing the importance of integrating extra-financial criteria in their practice, management companies undertake progressively to manage investment funds in the exclusive interest of the socially responsible investors, taking into account in their investment decisions, extra-financial constraints in addition to traditional financial constraints. This leads us to position the debate around the issue of the performance of SRI funds management companies remains essential. This research is part of a theoretical exploration which attempts to identify the determinants of the performance of SRI funds management companies and the challenges they face in their SRI fund selection process., L'Investissement Socialement Responsable (ISR), connaît depuis une dizaine d'années une croissance remarquable, il s'agit d'un style d'investissement multiforme qui consiste à intégrer les principes de développement durable dans le processus de placement et la gestion de portefeuille traditionnelle. L'ISR peut revêtir trois formes parmi lesquelles on peut citer celle des fonds ISR, cette stratégie consiste à sélectionner les titres composant un fonds d'investissement sur la base des critères environnementaux, sociaux et de gouvernance. Conscientes de l'importance d'intégrer des cirières extra-financiers dans leur pratique

In this paper, we study a portfolio optimization problem related to the asset management of life insurance companies. In a persistent low-interest-rate environment, the conditions under which the life insurance business operates are modified. To continue to offer a favorable return to the insured, life insurers should allocate more risky assets to their portfolio. But, doing so, they would be exposed to not being able to guarantee the capital. Besides, the maturity of the life insurance market creates potential conditions for massive withdrawals. We address those risk exposures by applying ruin models. We obtain formulae for the first two moments of the value of a life insurance company, depending on its activity and investment strategy. We show that the optimal asset allocation strategies can differ considerably for small changes in certain parameters of the insurer's business: the probability of insolvency, the level of guaranteed capital, and the premium rate.; Dans cet article, nous étudions un problème d'optimisation de portefeuille lié à la gestion d'actifs d'une compagnie d'assurance-vie pour son fonds euro. Dans un environnement persistant de taux bas, les conditions de fonctionnement des activités d'assurance-vie sont modifiées. Pour continuer à offrir une rémunération attractive aux assurés, les assureurs vie devraient introduire davantage d'actifs risqués dans leur portefeuille. Mais, ce faisant, ils s’exposeraient à ne pas pouvoir garantir le capital, ce qui est en contradiction avec les termes du contrat. Par ailleurs, la maturité du marché de l'assurance-vie en France crée des conditions potentielles de retraits massifs. En raison de ces multiples expositions aux risques, nous appliquons des modèles de ruine à ce problème global. Nous déterminons la formulation mathématique des deux premiers moments de la valeur d'une compagnie d'assurance-vie, en fonction de son activité et de sa stratégie d'investissement. Nous résolvons numériquement le problème d'optimisation sous contraintes. Nos résultats permettent de mieux analyser les problèmes de gestion de portefeuille des compagnies. Les stratégies d'allocation d'actifs optimales peuvent varier considérablement pour des changements minimes de certains paramètres de l'activité des assureurs : la probabilité d'insolvabilité, le niveau de capital garanti et le taux de prime.

In this paper, we define stochastic volatility operators in Hilbert space which are analogs to the widely-used SABR model [14] in finite dimensional case. We show the existence of the mild solution and some related regularity properties. Our proof is based on Leray-Schauder fixed point theorem and some priori inequalities on the stochastic operator processes we construct.

In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration., Kum-Hwan Roh gratefully acknowledges the support of Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government [Grant No. NRF-2017R1D1A3B03036548]

Financial crises are an important research topic because of their impact on the economy, businesses, and populations. However, prior research tends to generate reactive systemic risk measures, in the sense that the measure surges after the crisis starts. Few of them succeed in warning of financial crises in advance. In this paper, we first sketch a toy model that produces normal mixture distributions based on a dynamic regime switching model. We derive that the relative concavity among various indices tends to increase before a crisis. Using Polymodel theory, we introduce a measure of concavity as a crisis risk indicator, and test it against known crises observed in the past. We validate this indicator by a trading strategy holding long or short positions on the S & P 500 Index, depending on the indicator value.

The object of research is the publishing process for the magazine’s release. One of the most problematic areas is the difficulty of competition between print publishers and media that provide information services on the web. Therefore, for publishers, it is especially important to ensure consistency of work within the entire publishing process based on its rational organization. In particular, the development of structural diagrams and relationships at different levels of preparation and implementation of publishing projects is relevant. Network planning methods are used in research. The obtained results suggest that the proposed structural scheme for managing the publishing process is successful. Due to the presence of the proposed links between the participants in the publishing process, the scheme provides the ability to plan each current issue of the magazine with a guarantee of the announced terms of the magazine's release. The influence of two scenarios – pessimistic and optimistic – on the deadline is analyzed. It has been established that the pessimistic forecast does not allow for the specified target date if it is 1 time per quarter. The optimistic forecast provides the possibility of a guaranteed quarterly publication of the magazine – the target date for all publishing processes before the start of the finished circulation is 89–92 days. In the case of an optimistic forecast, the network schedule for publishing the magazine showed that all works within the publishing process, before the distribution of the current issue's circulation, have a reserve of time. While in the implementation of the pessimistic scenario, only two works have a reserve of time – the design and approval of the design of the advertisement with the customer. The peculiarities of the proposed solutions are that the pessimism and optimism of the scenarios are determined in relation to those works for which everything depends only on the performer. In turn, these are exactly the works that do not require external approvals.

This paper addresses the problem of utility maximization under uncertain parameters. In contrast with the classical approach, where the parameters of the model evolve freely within a given range, we constrain them via a penalty function. We show that this robust optimization process can be interpreted as a two-player zero-sum stochastic differential game. We prove that the value function satisfies the Dynamic Programming Principle and that it is the unique viscosity solution of an associated Hamilton-Jacobi-Bellman-Isaacs equation. We test this robust algorithm on real market data. The results show that robust portfolios generally have higher expected utilities and are more stable under strong market downturns. To solve for the value function, we derive an analytical solution in the logarithmic utility case and obtain accurate numerical approximations in the general case by three methods: finite difference method, Monte Carlo simulation, and Generative Adversarial Networks., Comment: 25 figures

I study the allocation problem of investors who hold their portfolio until a target wealth is attained. The strategy suppresses final wealth uncertainty but creates an investment time horizon risk. I begin with a simple mean variance model transposed in the duration domain, then study a dynamic portfolio choice problem with Generalized Expected Discounted Utility preferences. Using long-term US return data, I show in the mean variance model that a large amount of time horizon risk can be diversified away by investing a significant share of equities. In the dynamic model, more impatient investors are also more averse to timing risk and invest less in equities. The equity share is downward trending with accumulated wealth relative to its target. J.E.L. codes: D8, E21

The commodity pricing literature advocates the design of long-short portfolios based on equal weights. Relaxing the assumption of naive diversification, this article studies the benefits of applying sophisticated weighting schemes to the construction of long-short momentum and term structure portfolios. Weighting schemes based on risk minimization and risk timing are found to dominate the naive allocation and the weighting schemes based on utility maximization. This conclusion is not challenged by concerns pertaining to transaction costs, illiquidity, data mining, sub-periods, and model parameters and robustly persists when we consider as sorting signals hedging pressure, speculative pressure and, to a lower extent, basis-momentum.

International audience; This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. Our study is based on the case of the Maximum Variety Portfolio and may be obviously extended to other classical frameworks with analogous results. We focus on the fact that the assets should preferably be classified in homogeneous groups before applying the proposed methodology which is to whiten the data before estimating the covariance matrix using the robust Tyler M-estimator and the Random Matrix Theory (RMT). The proposed procedure is applied and compared to standard techniques on real market data showing promising improvements.

International audience

Given the importance of the residential property sector, we propose to study direct housing as an asset class within the optimal multi-asset portfolio. In this study, we consider the performances of direct housing within the metropolis Grand Paris between 1996 and 2017. We test the inflation hedging property of direct housing as well as its diversification benefits. We also define the optimal weight of this particular asset class when held together with stocks and bonds. Our finds bring several contributions to the French residential market within a portfolio management context. First, directly-held housing investment should bring diversification benefits to the mixed-asset portfolio. Second, residential asset is confirmed to be a hedge against inflation and particularly against its unexpected component. Third, using hierarchical clustering technique, we divided the "Grand Paris" into five homogenous groups of communes and give the optimal weight of each of the 150 communes and each group of communes into the tangency portfolio. Weights stability through time is checked through moving windows. We also compare our finding when considering listed real estate with direct housings.

This paper studies the extreme dependencies among energy, agriculture and metal commodities markets, with an emphasis on local co-movements. By applying a novel, copula-based, local Kendall’s tau approach to measure nonlinear local dependence in regions, we identified asymmetric co-movements in and between bull and bear markets, as well as the changing trend in the degree of co-movements. Starting from a non-parametric mixture copula, we found that commodities markets’ co-movements increase in extreme situations. In addition, we found a stronger dependence between energy and other commodities markets at lower tails. Therefore, we showed that the energy market can offer diversification solutions for risk management in the case of extreme bull market events.

We consider a variant of the basic problem of the calculus of variations, where the Lagrangian is convex and subject to randomness adapted to a Brownian filtration. We solve the problem by reducing it, via a limiting argument, to an unconstrained control problem that consists in finding an absolutely continuous process minimizing the expected sum of the Lagrangian and the deviation of the terminal state from a given target position. Using the Pontryagin maximum principle, we characterize a solution of the unconstrained control problem in terms of a fully coupled forward–backward stochastic differential equation (FBSDE). We use the method of decoupling fields for proving that the FBSDE has a unique solution. We exploit a monotonicity property of the decoupling field for solving the original constrained problem and characterize its solution in terms of an FBSDE with a free backward part.

Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.

This paper presents how the use of a cleaned and robust covariance matrix estimate can improve significantly the overall performance of maximum variety and minimum variance portfolios. We assume that the asset returns are modelled through a multi-factor model where the error term is a multivariate and correlated elliptical symmetric noise extending the classical Gaussian assumptions. The factors are supposed to be unobservable and we focus on a recent method of model order selection, based on the random matrix theory to identify the most informative subspace and then to obtain a cleaned (or de-noised) covariance matrix estimate to be used in the maximum variety and minimum variance portfolio allocation processes. We apply our methodology on real market data and show the improvements it brings if compared with other techniques especially for non-homogeneous asset returns.

International audience; This paper assesses the impact of low-carbon policy on stock returns by means of an environmental extension of Fama and French's (2015) five factor model. This paper makes four major contributions. Firstly, for the first time a factor, GMC (green minus carbon), meant to provide the premium which results from not paying a carbon price is constructed. The GMC factor is obtained by means of a sample of 182 firms from 19 European countries operating in 35 sectors: from January 2008 to December 2018 the value-weight returns of 91 firms regulated by the 2003/87/CE directive are subtracted from the value-weight returns of 91 firms exempted by the 2003/87/CE directive upon which the EU-ETS is based. Secondly, we provide evidence that the addition of the GMC factor improves the performance of the 5 factor model in Europe in the 2008-2018 time span. Thirdly, results show that there is a high green premium rather than a carbon premium as it was asserted by parts of the literature, and that this green premium is highly statistically significant. Fourthly, after performing a carbon stress test, we show the effects of EU-ETS average price shocks on both carbon and green firms for each market cap tranche.

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impacts. To do so, we improve the accuracy of the control randomization approach in the case of discrete controls, and propose a global iteration procedure to further improve the allocation estimates. We validate our numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model. We quantify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic allocation protects the investor's capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk-aversion level and investment horizon., Comment: 25 pages, 4 figures

The present study introduce the human capital component to the Fama and French five-factor model proposing an equilibrium six-factor asset pricing model. The study employs an aggregate of four sets of portfolios mimicking size and industry with varying dimensions. The first set consists of three set of six portfolios each sorted on size to B/M, size to investment, and size to momentum. The second set comprises of five index portfolios, third, a four-set of twenty-five portfolios each sorted on size to B/M, size to investment, size to profitability, and size to momentum, and the final set constitute thirty industry portfolios. To estimate the parameters of six-factor asset pricing model for the four sets of variant portfolios, we use OLS and Generalized method of moments based robust instrumental variables technique (IVGMM). The results obtained from the relevance, endogeneity, overidentifying restrictions, and the Hausman's specification, tests indicate that the parameter estimates of the six-factor model using IVGMM are robust and performs better than the OLS approach. The human capital component shares equally the predictive power alongside the factors in the framework in explaining the variations in return on portfolios. Furthermore, we assess the t-ratio of the human capital component of each IVGMM estimates of the six-factor asset pricing model for the four sets of variant portfolios. The t-ratio of the human capital of the eighty-three IVGMM estimates are more than 3.00 with reference to the standard proposed by Harvey et al. (2016). This indicates the empirical success of the six-factor asset-pricing model in explaining the variation in asset returns. Keywords: FF portfolio, Human capital, IVGMM approach, Return predictability, Six-factor asset pricing model, JEL classification: G12, C36, J24

International audience; We argue that the risk of an asset is measured by the covariance of an asset's return with the return on the aggregate market and human capital. The intertemporal and consumption-based CAPM, along with an extended version of CAPM framework examines the excess return on Fama and French portfolios sorted on size-BE/ME and momentum across the economies. The frequently used priced factors in anomaly literature include, Fama and French factors, momentum, dividend yield, bond market factors, saving, along with aggregate market and human capital component. Using unique panel data sets of emerging and developed economies, the panel regression, IV-GMM with random effects and PCA, finds the aggregate market and human capital are the strongest predictors of asset returns across the economies. Furthermore, the aggregate market and saving are strong predictors of asset return in emerging economies, whereas aggregate market and human capital emerge the best predictors of asset return in developed economies. Interestingly, human capital subsumes the predictive ability of Fama and French factors and becomes redundant along with momentum, dividend yield, and bond market factors.

International audience; This paper presents a performance evaluation of stock picking by merging several technical indicators. Several fusion operators have been proposed either in the probabilistic or in the possibilistic framework. The latter fuzzy framework has been introduced to manage the uncertain information embedded in financial time series due to human biases as studied by behavioral finance. Performances of portfolio resulting from the proposed systems, are evaluated according to cumulative returns but also through a risk analysis point of view (Sharpe ratio). Two fusion mechanisms (one probabilistic, one possibilistic) aiming at discriminating common information from merged technical indicators, produce the higher portfolio performances. It also appears that selecting specific technical indicators affects the overall performances of the proposed stock picking systems. Indeed, studying the technical indicators selection through a shared/non shared information point of view, reveals possibilistic framework is more robust to redundant sources than probabilistic framework. Effects of some parameters used in the fusion algorithms (amount of assets, window length analysis, . . .) are also investigated. Results from all these tests clearly show the high potentiality of technical indicators fusion to improve portfolio performances. However, these first promising results have to be further inspected within wider contexts, as discussed at the end of the paper.

Decision making under uncertainty is often viewed as an optimization problem under choice criterium, but its calibration raises the "inverse" problem to recover the criterium from the data. An example is the theory of "revealed preference" by Samuelson in the 40s, \cite{samuelson1938}.\\ The observable is a so-called increasing characteristic process $\mbX=(\scX_t(x))$ and the objective is to recover a dynamic stochastic utility $\bfU$ "revealed" in the sense where "$U(t,\scX_t(x))$ is a martingale". A linearized version is provided by the first order conditions $U_x(t,\scX_t(x))=Y_t(u_z(x)$, and the additional martingale conditions of the processes $\scX_x(t,x)Y_t(u_z(x))$ and $\scX_t(x)Y_t(u_z(x))$. When $\mbX$ and $\bfY$ are regular solutions of two SDEs with random coefficients, under strongly martingale condition, any revealed utility is solution of a non linear SPDE, and is the stochastic value function of some optimization problem. More interesting is the dynamic equilibrium problem as in He and Leland \cite{HL}, where $Y$ is coupled with $\scX$ so that the monotonicity of $Y_t(z,u_z(z)) $ is lost. Nevertheless, we solve the He \& Leland problem (in random environment), by characterizing all the equilibria: the adjoint process still linear in $y$ (GBM in Markovian case) and the conjugate utilities are a deterministic mixture of stochastic dual power utilities. Besides, the primal utility is the value function of an optimal wealth allocation in the Pareto problem.

International audience; Considered to be a booming investment style, Socially Responsible Investment (SRI) integrates sustainable development principles into financial investment and investment fund management. Indeed, according to previous research, SRI calls into question the current financial system disconnected from the real economy by developing new investment strategies in response to sustainable development challenges. These are revolutionizing pre-established financial management methods in the capital markets, which would allow SRI to reconcile finance with the real economy and create a new, more sustainable economic system. This article is a theoretical exploration of the key strategies of SRI, highlighting the role that SRI could play in the growth of the sustainable economy.

The purpose of this paper is to develop an explicit construction of consistent utilities, using the stochastic flows approach developed in [KM13] and [KM16]. Starting from a family of utility functions indexed by some parameter α (for example the risk aversion of different agents), the idea is to randomize α and construct a non standard stochastic utilities processes. Two approach are developed, the first one consists to built directly from the class {U α , α ∈ R} a global one U as a sup-convolution. The second approach which is very different, consists to define from a class (X α , Y α) α∈R of monotonic processes a global pair (X * , Y *) as a mixture. The non standard stochastic utility is then obtained by composing stochastic flows and interpreted as the aggregate utility of all considered agents .

International audience; Banks play a predominant role in the economy and are subject to growing expectations from stakeholders. It is therefore important to understand the financial impact of CSR on banks' activities. This article examines the impact of CSR on bank efficiency by using a DEA Dynamic Network Model. Based on an international sample of 184 banks in 41 countries over the 2009-2015 period, our empirical investigation reveals a positive impact of CSR on bank efficiency. We further show that this relationship is contingent upon the institutional context. Specifically, we find that CSR has a positive impact on bank efficiency only in developed countries, in countries where investor protection is high and in countries featuring a high degree of stakeholder orientation. We thus assert that some institutional characteristics must be present for the positive impact of CSR on bank efficiency to materialize.

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted., [ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted. [ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.