High Frequency Trading (HFT) algorithms are automated feedback systems interacting with markets to maximize returns on investments. These systems have the potential to read different resolutions of market information at any given time, where Level I information is the minimal information about an equity--essentially its price--and Level II information is the full order book at that time for that equity. This paper presents a study of using Recurrent Neural Network (RNN) models to predict the spread of the DOW Industrial 30 index traded on NASDAQ, using Level I and Level II data as inputs. The results show that Level II data does not significantly improve the prediction of spread when predicting less than 100 millisecond into the future, while it is increasingly informative for spread predictions further into the future. This suggests that HFT algorithms should not attempt to make use of Level II information, and instead reallocate that computation power for improved trading performance, while slower trading algorithms may very well benefit from processing the complete order book.

We study optimal order placement in a limit order book. By modelling the limit order book dynamics as a Markov chain, we can frame the purchase of a single share as a Markov Decision Process. Within the framework of the model, we can estimate optimal decision policies numerically. The trade rate is varied using a running cost control variable. The optimal policy is found to result in a lower cost of trading as a function of the trade rate compared to a market order only strategy. / Vi studerar optimal orderläggning i en limit orderbok. Genom att modellera dynamiken av inkommande ordrar som en Markov kedja så kan vi formula optimal orderläggning som en Markov Decision Process. Inom ramverket av modellen så kan vi skatta optimala strategier numeriskt. En löpande kostnad används som en kontrollvariabel för handelstakten av den optimala strategin. Vi finner att den optimala strategin resulterar i en lägre handelskostnad som funktion av deltagande jämfört med en marknadsorder strategi.

Neste trabalho, apresentamos um conjunto de fatos empíricos e propriedades estatística de negociações em alta frequência, e discutimos algumas questões gerais comuns a dados de alta frequência tais: como discretização, espaçamento temporal irregular, durações correlacionadas, periodicidade diária, correlações temporais e as propriedades estatísticas dos fluxos de ordens. Logo apresentamos dois modelos da literatura,estilizados para a dinâmica do limit order book. No primeiro modelo os fluxo de ordens é descrito por processos de Poisson independentes, propomos para ele uma forma alternativa da prova de ergodicidade basejada em funções de Lyapunov. O segundo modelo é um modelo reduzido que toma em consideração dinâmicas tipo difusão para os tamanhos do bid e ask, e se foca só nas ordens como melhores preços, e modela explicitamente as cotações do bid e ask na presença de liquidez oculta. E por ultimo, propomos um modelo alternativo para a dinâmica do preço e do spread no limit order book, estudamos o comportamento assintótico do modelo e estabelecemos condições de ergodicidade e transitoridade. Além disso, consideramos a uma família de cadeias de Markov definidos nas sequências de caracteres (strings, ou palavras) com infinito alfabeto e para alguns exemplos inspirados nos modelos de negociações em alta frequência, obtemos condições para ergodicidade, transitoriedade e recorrência nula, para a qual usamos as técnicas de construção de funções Lyapunov. / In this work, we present a set of empirical facts and statistical properties of negotiations at high frequency and discuss some general issues common to high-frequency data such: as discretization, irregular spacing, correlated durations, daily periodicity, temporal correlations and the statistical properties of flows orders. Soon we present two models stylized in the literature for the dynamic limit order book. In the first model the order flow described by separate Poisson processes and we propose it to an alternative form of test ergodicity based on Lyapunov function. The second model is a reduced model that takes into consideration diffusion-type dynamics for the sizes of the bid and ask, and focus only on orders as best price and model explicitly quotes the bid and ask in the presence of hidden liquidity. And finally, we propose an alternative model for the price dynamics and spread in the limit order book, we study the asymptotic behavior of the model and established conditions of ergodicity. Furthermore, we consider the a family of Markov chains defined on the sequences of characters (strings, or words) with infinite alphabet. For some examples inspired by the models of high frequency trading we obtain a conditions for ergodicity, transience and null-recurrence. In order to prove this we use the construction of Lyapunov functions techniques.

Neste trabalho, apresentamos um conjunto de fatos empíricos e propriedades estatística de negociações em alta frequência, e discutimos algumas questões gerais comuns a dados de alta frequência tais: como discretização, espaçamento temporal irregular, durações correlacionadas, periodicidade diária, correlações temporais e as propriedades estatísticas dos fluxos de ordens. Logo apresentamos dois modelos da literatura,estilizados para a dinâmica do limit order book. No primeiro modelo os fluxo de ordens é descrito por processos de Poisson independentes, propomos para ele uma forma alternativa da prova de ergodicidade basejada em funções de Lyapunov. O segundo modelo é um modelo reduzido que toma em consideração dinâmicas tipo difusão para os tamanhos do bid e ask, e se foca só nas ordens como melhores preços, e modela explicitamente as cotações do bid e ask na presença de liquidez oculta. E por ultimo, propomos um modelo alternativo para a dinâmica do preço e do spread no limit order book, estudamos o comportamento assintótico do modelo e estabelecemos condições de ergodicidade e transitoridade. Além disso, consideramos a uma família de cadeias de Markov definidos nas sequências de caracteres (strings, ou palavras) com infinito alfabeto e para alguns exemplos inspirados nos modelos de negociações em alta frequência, obtemos condições para ergodicidade, transitoriedade e recorrência nula, para a qual usamos as técnicas de construção de funções Lyapunov. / In this work, we present a set of empirical facts and statistical properties of negotiations at high frequency and discuss some general issues common to high-frequency data such: as discretization, irregular spacing, correlated durations, daily periodicity, temporal correlations and the statistical properties of flows orders. Soon we present two models stylized in the literature for the dynamic limit order book. In the first model the order flow described by separate Poisson processes and we propose it to an alternative form of test ergodicity based on Lyapunov function. The second model is a reduced model that takes into consideration diffusion-type dynamics for the sizes of the bid and ask, and focus only on orders as best price and model explicitly quotes the bid and ask in the presence of hidden liquidity. And finally, we propose an alternative model for the price dynamics and spread in the limit order book, we study the asymptotic behavior of the model and established conditions of ergodicity. Furthermore, we consider the a family of Markov chains defined on the sequences of characters (strings, or words) with infinite alphabet. For some examples inspired by the models of high frequency trading we obtain a conditions for ergodicity, transience and null-recurrence. In order to prove this we use the construction of Lyapunov functions techniques.

During the past 30 years, the proliferation of electronic trading has catalysed profound structural change in the global foreign exchange (FX) spot market. Today, more than 60% of the market's volume occurs via electronic trading platforms, which provide traders with round-the-clock market access from anywhere in the world. Such platforms offer several practical benefits that have encouraged market participation from a broad new class of financial institutions and have thereby spurred market growth. The most widely used electronic trading platforms in the FX spot market incorporate several features that differentiate them from those used in other financial markets. These features raise many important questions about order flow, market state, price formation, trader behaviour, and volatility. Despite the enormous trade volumes that such platforms facilitate, these questions have received almost no attention to date. In this thesis, we study a recent, high-quality data set from a large electronic trading platform in the FX spot market in order to investigate several aspects of trading via this mechanism. We calculate a wide range of statistics regarding order flow and market state, and we highlight how our findings contrast to those reported by empirical studies of electronic trading platforms in other markets. We study the autocorrelation properties of returns, absolute returns, and order flow, and we investigate the extent to which the market's organization impacts price formation. We also introduce a model designed to reproduce the most important properties of trading via such a platform. We derive several results regarding the model's temporal evolution, and we simulate the model to investigate how the interactions between individual traders influence volatility. We conclude that electronic trading platforms in the FX spot market retain many desirable features of centralized markets while providing traders with explicit control over their personal trading partnerships.

THE STIGLER-LUCKOCK MODEL FOR A LIMIT ORDER BOOK Abstract One of the types of modern-day markets are so-called order-driven markets whose core component is a database of all incoming buy and sell orders (order book). The main goal of this thesis is to extend the Stigler-Luckock model for order books to give a better insight into the price forming process and behaviour of the market participants themselves. The model introduced in this thesis focuses on a comparison of behaviour and various strategies of market makers who are sophisticated market participants profiting from extensive trading. The market is described using Markov chains, and the strategies are compared using Monte Carlo simulations and game theory. The results showed that market makers' orders should have small spread and large volumes. The final model compares two strategies in which market makers monitor their portfolio. In case of having more cash than asset (or vice versa), they shift prices of their orders to equalise the portfolio. The model recommends checking the market quite often, but acting conservatively, which means not changing prices that frequently and not jumping to conclusions just from a small imbalance in the portfolio.

On propose une perspective originale d'analyser les différents flux hautes fréquences d'information provenant des marchés financiers et fournit des modèles simples et intuitives qui reflètent étroitement la réalité. On observe les données empiriques et note certains faits stylisés et propose des modèles pour capturer ces faits. Dans le chapitre 1, on passe en revue les définitions et propriétés de base des marchés électroniques. En particulier, on revoit les travaux de microstructure et de modélisation du marché, et leurs relations à ce travail. On introduit la taille du "tick", qu'on utilise pour classifier les actifs et interpréter les différents résultats. Dans le chapitre 2, on montre empiriquement que l'impact d'une seule transaction dépend de la durée inter-transactions. En effet, lorsque le taux des échanges devient plus rapide, la variance des rendements des transactions augmente fortement et que ce comportement persiste à des échelles de temps plus grossières. On montre également que la valeur du spread augmente avec l'activité et on en déduit que les carnets d'ordres sont plus vide lorsque le taux des échanges est élevé. Dans le chapitre 3, on présente un modèle pour capturer le bruit de microstructure. Les prix des actifs sont représentés par la somme des rendements "tick" arrivant à des temps de Poisson aléatoires. Le modèle se compose d'une martingale diffusive contaminée par un bruit autocorrélé mais disparaissant aux échelles grossières. On est capable de capturer la signature de la variance et l'autocorrélation faible mais significative des rendements "tick". Dans le chapitre 4, on utilise les processus ponctuels de Hawkes pour modéliser l'arrivée aléatoire des transactions. On modélise la transformation échelle fine - échelle grossière des prix et en particulier l'effet sur les moments des rendements des prix. On propose une technique simple d'estimation non paramétrique de la structure de dépendance des processus de Hawkes dans le cas unidimensionnel et dans quelques cas particuliers multidimensionnels. On applique la méthode à des actifs de Future et trouve des noyaux de dépendance en loi de puissance.

Accurate forecasting of stock price movements is crucial for optimizing trade execution and mitigating risk in automated trading environments, especially when leveraging Limit Order Book (LOB) data. However, developing predictive models from LOB data presents substantial challenges due to its inherent complexities and high-frequency nature. In this thesis, the application of the General Compound Hawkes Process (GCHP) is explored to predict price volatility. Within this framework, a Hawkes process is employed to estimate the times of price changes, and a Markovian model is utilized to determine their amplitudes. The price volatility is obtained through both numerical and analytical methodologies. The performance of the GCHP is assessed on a publicly available dataset, including five distinct stocks. To enhance accuracy, the number of states in the Markov chain is gradually increased, and the advantages of incorporating a higher-order Markov chain for refined volatility estimation are demonstrated.

Le développement des marchés électroniques organisés induit une pression constante sur la recherche académique en finance. L'impact sur le prix d'une transaction boursière portant sur une grande quantité d'actions sur une période courte est un sujet central. Contrôler et surveiller l'impact sur le prix est d'un grand intérêt pour les praticiens, sa modélisation est ainsi devenue un point central de la recherche quantitative de la finance. Historiquement, le calcul stochastique s'est progressivement imposé en finance, sous l'hypothèse implicite que les prix des actifs satisfont à des dynamiques diffusives. Mais ces hypothèses ne tiennent pas au niveau de la ``formation des prix'', c'est-à-dire lorsque l'on se place dans les échelles fines des participants de marché. Des nouvelles techniques mathématiques issues de la statistique des processus ponctuels s'imposent donc progressivement. Les observables (prix traité, prix milieu) apparaissent comme des événements se réalisant sur un réseau discret, le carnet d'ordre, et ceci à des échelles de temps très courtes (quelques dizaines de millisecondes). L'approche des prix vus comme des diffusions browniennes satisfaisant à des conditions d'équilibre devient plutôt une description macroscopique de phénomènes complexes issus de la formation des prix. Dans un premier chapitre, nous passons en revue les propriétés des marchés électroniques. Nous rappelons la limite des modèles diffusifs et introduisons les processus de Hawkes. En particulier, nous faisons un compte rendu de la recherche concernant le maket impact et nous présentons les avancées de cette thèse. Dans une seconde partie, nous introduisons un nouveau modèle d'impact à temps continu et espace discret en utilisant les processus de Hawkes. Nous montrons que ce modèle tient compte de la microstructure des marchés et est capable de reproduire des résultats empiriques récents comme la concavité de l'impact temporaire. Dans le troisième chapitre, nous étudions l'impact d'un grand volume d'action sur le processus de formation des prix à l'échelle journalière et à une plus grande échelle (plusieurs jours après l'exécution). Par ailleurs, nous utilisons notre modèle pour mettre en avant des nouveaux faits stylisés découverts dans notre base de données. Dans une quatrième partie, nous nous intéressons à une méthode non-paramétrique d'estimation pour un processus de Hawkes unidimensionnel. Cette méthode repose sur le lien entre la fonction d'auto-covariance et le noyau du processus de Hawkes. En particulier, nous étudions les performances de cet estimateur dans le sens de l'erreur quadratique sur les espaces de Sobolev et sur une certaine classe contenant des fonctions « très » lisses / The development of organized electronic markets induces a constant pressure on academic research in finance. A central issue is the market impact, i.e. the impact on the price of a transaction involving a large amount of shares over a short period of time. Monitoring and controlling the market impact is of great interest for practitioners; its modeling and has thus become a central point of quantitative finance research. Historically, stochastic calculus gradually imposed in finance, under the assumption that the price satisfies a diffusive dynamic. But this assumption is not appropriate at the level of ”price formation”, i.e. when looking at the fine scales of market participants, and new mathematical techniques are needed as the point processes. The price (last trade, mid-price) appears as events on a discrete network, the order book, at very short time scales (milliseconds). The Brownien motion becomes rather a macroscopic description of the complex price formation process. In the first chapter, we review the properties of electronic markets. We recall the limit of diffusive models and introduce the Hawkes processes. In particular, we make a review of the market impact research and present this thesis advanced. In the second part, we introduce a new model for market impact model at continuous time and living on a discrete space using process Hawkes. We show that this model that takes into account the market microstructure and it is able to reproduce recent empirical results as the concavity of the temporary impact. In the third chapter, we investigate the impact of large orders on the price formation process at intraday scale and at a larger scale (several days after the meta-order execution). Besides, we use our model to discuss stylized facts discovered in the database. In the fourth part, we focus on the non-parametric estimation for univariate Hawkes processes. Our method relies on the link between the auto-covariance function and the kernel process. In particular, we study the performance of the estimator in squared error loss over Sobolev spaces and over a certain class containing "very'' smooth functions

Ce travail de thèse aborde deux sujets : (i) L'utilisation d'une nouvelle méthode numérique pour l'évaluation des options sur un panier d'actifs, (ii) Le risque de liquidité, la modélisation du carnet d'ordres et la microstructure de marché. Premier thème : Un algorithme glouton et ses applications pour résoudre des équations aux dérivées partielles. L'exemple typique en finance est l'évaluation d'une option sur un panier d'actifs, laquelle peut être obtenue en résolvant l'EDP de Black-Scholes ayant comme dimension le nombre d'actifs considérés. Nous proposons d'étudier un algorithme qui a été proposé et étudié récemment dans [ACKM06, BLM09] pour résoudre des problèmes en grande dimension et essayer de contourner la malédiction de la dimension. L'idée est de représenter la solution comme une somme de produits tensoriels et de calculer itérativement les termes de cette somme en utilisant un algorithme glouton. La résolution des EDP en grande dimension est fortement liée à la représentation des fonctions en grande dimension. Dans le Chapitre 1, nous décrivons différentes approches pour représenter des fonctions en grande dimension et nous introduisons les problèmes en grande dimension en finance qui sont traités dans ce travail de thèse. La méthode sélectionnée dans ce manuscrit est une méthode d'approximation non-linéaire appelée Proper Generalized Decomposition (PGD). Le Chapitre 2 montre l'application de cette méthode pour l'approximation de la solution d'une EDP linéaire (le problème de Poisson) et pour l'approximation d'une fonction de carré intégrable par une somme des produits tensoriels. Un étude numérique de ce dernier problème est présenté dans le Chapitre 3. Le problème de Poisson et celui de l'approximation d'une fonction de carré intégrable serviront de base dans le Chapitre 4 pour résoudre l'équation de Black-Scholes en utilisant l'approche PGD. Dans des exemples numériques, nous avons obtenu des résultats jusqu'en dimension 10. Outre l'approximation de la solution de l'équation de Black-Scholes, nous proposons une méthode de réduction de variance des méthodes Monte Carlo classiques pour évaluer des options financières. Second thème : Risque de liquidité, modélisation du carnet d'ordres, microstructure de marché. Le risque de liquidité et la microstructure de marché sont devenus des sujets très importants dans les mathématiques financières. La dérégulation des marchés financiers et la compétition entre eux pour attirer plus d'investisseurs constituent une des raisons possibles. Dans ce travail, nous étudions comment utiliser cette information pour exécuter de façon optimale la vente ou l'achat des ordres. Les ordres peuvent seulement être placés dans une grille des prix. A chaque instant, le nombre d'ordres en attente d'achat (ou vente) pour chaque prix est enregistré. Dans [AFS10], Alfonsi, Fruth et Schied ont proposé un modèle simple du carnet d'ordres. Dans ce modèle, il est possible de trouver explicitement la stratégie optimale pour acheter (ou vendre) une quantité donnée d'actions avant une maturité. L'idée est de diviser l'ordre d'achat (ou de vente) dans d'autres ordres plus petits afin de trouver l'équilibre entre l'acquisition des nouveaux ordres et leur prix. Ce travail de thèse se concentre sur une extension du modèle du carnet d'ordres introduit par Alfonsi, Fruth et Schied. Ici, l'originalité est de permettre à la profondeur du carnet d'ordres de dépendre du temps, ce qui représente une nouvelle caractéristique du carnet d'ordres qui a été illustré par [JJ88, GM92, HH95, KW96]. Dans ce cadre, nous résolvons le problème de l'exécution optimale pour des stratégies discrètes et continues. Ceci nous donne, en particulier, des conditions suffisantes pour exclure les manipulations des prix au sens de Huberman et Stanzl [HS04] ou de Transaction-Triggered Price Manipulation (voir Alfonsi, Schied et Slynko) / This work is organized in two themes : (i) A novel numerical method to price options on manyassets, (ii) The liquidity risk, the limit order book modeling and the market microstructure.First theme : Greedy algorithms and applications for solving partial differential equations in high dimension Many problems of interest for various applications (material sciences, finance, etc) involve high-dimensional partial differential equations (PDEs). The typical example in finance is the pricing of a basket option, which can be obtained by solving the Black-Scholes PDE with dimension the number of underlying assets. We propose to investigate an algorithm which has been recently proposed and analyzed in [ACKM06, BLM09] to solve such problems and try to circumvent the curse of dimensionality. The idea is to represent the solution as a sum of tensor products and to compute iteratively the terms of this sum using a greedy algorithm. The resolution of high dimensional partial differential equations is highly related to the representation of high dimensional functions. In Chapter 1, we describe various linear approaches existing in literature to represent high dimensional functions and we introduce the high dimensional problems in finance that we will address in this work. The method studied in this manuscript is a non-linear approximation method called the Proper Generalized Decomposition. Chapter 2 shows the application of this method to approximate the so-lution of a linear PDE (the Poisson problem) and also to approximate a square integrable function by a sum of tensor products. A numerical study of this last problem is presented in Chapter 3. The Poisson problem and the approximation of a square integrable function will serve as basis in Chapter 4for solving the Black-Scholes equation using the PGD approach. In numerical experiments, we obtain results for up to 10 underlyings. Second theme : Liquidity risk, limit order book modeling and market microstructure. Liquidity risk and market microstructure have become in the past years an important topic in mathematical finance. One possible reason is the deregulation of markets and the competition between them to try to attract as many investors as possible. Thus, quotation rules are changing and, in general, more information is available. In particular, it is possible to know at each time the awaiting orders on some stocks and to have a record of all the past transactions. In this work we study how to use this information to optimally execute buy or sell orders, which is linked to the traders' behaviour that want to minimize their trading cost. In [AFS10], Alfonsi, Fruth and Schied have proposed a simple LOB model. In this model, it is possible to explicitly derive the optimal strategy for buying (or selling) a given amount of shares before a given deadline. Basically, one has to split the large buy (or sell) order into smaller ones in order to find the best trade-off between attracting new orders and the price of the orders. Here, we focus on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. The additional feature is a time-varying LOB depth that represents a new feature of the LOB highlighted in [JJ88, GM92, HH95, KW96]. We solve the optimal execution problem in this framework for both discrete and continuous time strategies. This gives in particular sufficient conditions to exclude Price Manipulations in the sense of Huberman and Stanzl [HS04] or Transaction-Triggered Price Manipulations (see Alfonsi, Schied and Slynko). The seconditions give interesting qualitative insights on how market makers may create price manipulations