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.

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

This dissertation consists of five separate and self-contained essays. They have been written as distinct papers. Although there is a fair amount of overlap and cross-reference in analysis and discussion, the intention is that potential readers should be able to read them separately. Essay 1: An Empirical Analysis of the Trading Structure at the Stockholm Stock Exchange.This essay describes and analyzes the trading structure at the Stockholm Stock Exchange. In the empirical part, we report stylized facts based on intraday transaction and order book data, focusing on the intraday behavior of returns, trading activity, order placement and bid/ask spread, on the importance of the tick size and finally on some characteristics of the limit order book. Our main empirical conclusions are that a) the intraday U-shape in trading activity found in earlier U.S. studies on the whole also pertains to the Stockholm Stock Exchange, b) the limit order placement also follows an intraday U-shape, c) there is no distinct intraday pattern in returns, d) the volatility and bid/ask spread seems to be higher at the beginning of the trading day, e) the tick size is economically important, and f) the price impact of an order is a nonlinear function of its quantity, implying price inelastic demand and supply. Essay 2: An Empirical Analysis of the Trading Structure at the Stockholm Options and Forwards Exchange, OM.We first describe and analyze the trading structure at the Stockholm Options and Forward Exchange, OM Stockholm. It is characterized by some interesting market microstructure features, such as a high degree of transparency in a fully computerized trading system and a possibility to submit combination orders. We also present empirically results from tests on the intra- and interday trading volume of the OMX index derivatives, both in terms of number of contracts traded and in terms of number of transactions. There is evidence of a high degree of intraday variation in trading volume and some interday variation. The extension of trading hours of the underlying stocks, during the studied period should, according to modern trade concentration models, affect the distribution of trading across the day. Although no formal test of the models is possible with this data set, we are able to shed some supportive additional light on all of these models. Essay 3: Tick Size, Market Liquidity and Trading Volume: Evidence from the Stockholm Stock Exchange. (This essay was co-authored with Patrik Sandås.)The regulated tick size at a securities exchange puts a lower bound on the bid/ask spread. We use cross-sectional and cross-daily data from the Stockholm Stock Exchange to assess if this lower bound is economically important and if it has any direct effect on market depth and traded volume. We find a) strong support that the tick size is positively correlated to market depth and c) some support that it is negatively related to traded volume. We identify different groups of agents to whom a lower tick size would be beneficial and to whom it would be detrimental. Essay 4: An Analysis of the Lead-Lag Relationship between the OMX Index Forwards and the OMX Cash Index.This essay investigates the intraday lead-lag structure in returns between on the one hand the OMX cash index and on the other hand the OMX index forwards and the OMX synthetic index forwards in Sweden. The data set includes 22 months of data, from December 1991, to September 1993. It is divided into three sub-periods. The main conclusion is that there is a high degree of bidirectional interdependence, with both series Granger causing each other. Using a Sims-test, we find that the forwards as well as synthetic forwards lead the cash index with between fifteen and thirty minutes, while the cash index leads the forwards with about ten to fifteen minutes.. This implies a longer lead from the cash index to the forwards than in previous studies. The large interdependence could possibly be due to higher transaction costs, lower liquidity in the forward market and the specific trading environments used for Swedish securities. Essay 5: Order Flow Dynamics: Evidence from the Helsinki Stock Exchange. (This essay was co-authored with Kaj Hedvall.)This essay investigates the dynamics of the order flow in a limit order book. In contrast to previous studies, our data set from the Helsinki Stock Exchange encompasses the entire order book structure, including the dealer identities. This enables us to focus on the order behavior of individual dealers. We classify the events in the order book and study the structure of subsequent events using contingency tables. In specific, the structure of subsequent events initiated by the same dealer is compared to the overall event structure. We find that order splitting is more frequent than order imitation. Furthermore, if the spread increases as a result of a trade, other dealers quickly restore the spread, by submitting new limit orders. One conclusion is therefore that there exists a body of potential limit orders outside the formal limit order book and that there is a high degree of resiliency in our limit order book market. As a logical consequence, a large dealer strategically splits his order, in order for the market to supply additional liquidity. One interpretation of our results is that a limit order book market can accommodate larger orders than is first apparent by the outstanding limit orders. Another interpretation is that a limit order book structure gives room for informed traders to successively trade on their information. A third interpretation is that prices only slowly incorporate new information. / Diss. Stockholm : Handelshögskolan, 1994

During the last few years, market micro-structure research has been active in analysing the dependence of market efficiency on different market character­ istics. Make-take fees are one of those topics as they might modify the incen­ tives for participating agents, e.g. broker-dealers or market-makers. In this thesis, we propose a Hawkes process-based model that captures statistical differences arising from different fee regimes and we estimate the differences on limit order book data. We then use these estimates in an attempt to measure the execution quality from the perspective of a market-maker. We appropriate existing theoretical market frameworks, however, for the pur­ pose of hireling optimal market-making policies we apply a novel method of deep reinforcement learning. Our results suggest, firstly, that maker-taker exchanges provide better liquidity to the markets, and secondly, that deep reinforcement learning methods may be successfully applied to the domain of optimal market-making. JEL Classification Keywords Author's e-mail Supervisor's e-mail C32, C45, C61, C63 make-take fees, Hawkes process, limit order book, market-making, deep reinforcement learn­ ing kiselrastislavSgmail.com barunik@f sv.cuni.cz

Submitted by Guilherme Hideo Assaoka Hossaka (ghossaka@gmail.com) on 2018-09-07T17:43:58Z No. of bitstreams: 1 Stochastic_Supply_Curves_and_Liquidity_Costs.pdf: 6114705 bytes, checksum: 383773b2814d582892c750a566229869 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2018-09-18T13:52:02Z (GMT) No. of bitstreams: 1 Stochastic_Supply_Curves_and_Liquidity_Costs.pdf: 6114705 bytes, checksum: 383773b2814d582892c750a566229869 (MD5) / Made available in DSpace on 2018-09-26T20:00:18Z (GMT). No. of bitstreams: 1 Stochastic_Supply_Curves_and_Liquidity_Costs.pdf: 6114705 bytes, en checksum: 383773b2814d582892c750a566229869 (MD5) Previous issue date: 2018-06-26 / Market Liquidity is characterized by the easiness and freedom to trade assets at desired volumes and for prices perceived as representative of their values. When there is a scarcity of bid and ask offers at those terms, traders face the so called Market Liquidity Risk and they must offer concessions on their original offers, leading to additional costs. Approaches to model this phenomena exist in broad variety but a common component of most Market Liquidity models is an instantaneous cost component, also known as transaction/execution costs or realized/instantaneous impact. This element, here the Liquidity Cost, gives the actual trading prices faced by a trader, frequently a deviation from the unobservable “true price”, normally represented as a GBM with the mid-price as a proxy for modeling purposes. Although it is clear that Liquidity Costs are a relevant aspect of Market Liquidity Risk and it is present in many models, it is relegated to a more simplistic treatment, being though as well-behaved, deterministic, smooth and static. The main point of this work is to follow a different approach by evaluating Liquidity Costs at a microstructural level by estimating the Stochastic Supply Curve from C¸ etin-Jarrow-Protter Model for Brazilian equities. To do so, high-frequency-data from B3’s ftp is used and to build Limit Order Books for several stocks at intraday periods. The empirical findings support the existence of non-trivial Stochastic Supply Curves as a representation for Liquidity Costs in several equities on Brazilian Markets. Additionally, there is evidence that Liquidity Costs may behave in contrast with some of the literature, being stochastic with time-varying functional representations on the LOB and with liquidity parameters that could be represented as mean-reverting stochastic process.