arXiv · arXiv q-fin · 2026
We propose the Split-Session Cluster GARCH model for heavy-tailed multivariate dependence among asset returns decomposed into overnight and intraday components. The model uses convolution-$t$ distributions to allow tail behavior to differ across clusters defined by trading sessions and, within each session, by economic sectors. It also accommodates block-structured conditional correlation matrices, preserving parsimo…
Xinxian Chen, Peter Reinhard Hansen, Chen Tong
arXiv · arXiv q-fin · 2013
We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon sessions. After calculating the realized volatilities at various sampling frequencies we evaluate the b…
Tetsuya Takaishi, Ting Ting Chen, Zeyu Zheng
arXiv · arXiv q-fin · 2024
In this paper, we propose a complete modelling framework to value several batteries in the electricity intraday market at the trading session scale. The model consists of a stochastic model for the 24 mid-prices (one price per delivery hour) combined with a deterministic model for the liquidity costs (representing the cost of going deeper in the order book). A stochastic optimisation framework based on dynamic progra…
Enzo Cognéville, Thomas Deschatre, Xavier Warin
arXiv · arXiv q-fin · 2018
The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the trading session. These observations could be attributed, in part, to the prevalence of portfolio tra…
Seungki Min, Costis Maglaras, Ciamac C. Moallemi
arXiv · arXiv q-fin · 2026
In this work, we investigate the market-making problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market-making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the significant liquidity events available in closing auctions, we propose a Deep Q-Learning framework that explicitly incorporates…
Julius Graf, Thibaut Mastrolia
arXiv · arXiv q-fin · 2025
This paper investigates optimal execution strategies in intraday energy markets through a mutually exciting Hawkes process model. Calibrated to data from the German intraday electricity market, the model effectively captures key empirical features, including intra-session volatility, distinct intraday market activity patterns, and the Samuelson effect as gate closure approaches. By integrating a transient price impac…
Konstantinos Chatziandreou, Sven Karbach
arXiv · arXiv q-fin · 2020
Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given time horizon. Few models rigorously tackle the actual placement of these child orders. Instead, placement is mostly done with…
Jackie Jianhong Shen
arXiv · arXiv q-fin · 2026
Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the semantic complexity of rapidly shifting market regimes. This paper introduces AGENTICAITA, an agentic AI framework that replaces the traditional signal then execute paradigm with a fully autonomous deliberative loop in which multiple specialized Large Language Model agents r…
Ivan Letteri
arXiv · arXiv q-fin · 2026
This paper asks whether a small set of observable pre-market characteristics can identify trading days with systematically different intraday behavior in Micro E-Mini Nasdaq-100 (MNQ) futures. I construct a simple day-classification framework based on the overnight gap, the first 30-minute return, and first-bar trading volume relative to a rolling 20-day baseline. The framework, referred to as the Volatility-Volume-G…
Mathias Mesfin
arXiv · arXiv q-fin · 2024
Flaws of a continuous limit order book mechanism raise the question of whether a continuous trading session and a periodic auction session would bring better efficiency. This paper wants to go further in designing a periodic auction when both a continuous market and a periodic auction market are available to traders. In a periodic auction, we discover that a strategic trader could take advantage of the accumulated in…
Thibaut Mastrolia, Tianrui Xu
arXiv · arXiv q-fin · 2023
Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded p…
Simon Hirsch, Florian Ziel
arXiv · arXiv q-fin · 2018
We study the intraday behaviour of the statistical moments of the trading volume of the blue chip equities that composed the Dow Jones Industrial Average index between 2003 and 2014. By splitting that time interval into semesters, we provide a quantitative account of the non-stationary nature of the intraday statistical properties as well. Explicitly, we prove the well-known U-shape exhibited by the average trading v…
Michelle B Graczyk, Silvio M D Queirós
arXiv · arXiv q-fin · 2017
We calculate realized volatility of the Nikkei Stock Average (Nikkei225) Index on the Tokyo Stock Exchange and investigate the return dynamics. To avoid the bias on the realized volatility from the non-trading hours issue we calculate realized volatility separately in the two trading sessions, i.e. morning and afternoon, of the Tokyo Stock Exchange and find that the microstructure noise decreases the realized volatil…
Tetsuya Takaishi, Toshiaki Watanabe
arXiv · arXiv q-fin · 2013
For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on Globex. The Weibull, Kumaraswamy, Riemann and Hurwitz Zeta, parabolic, Zipf-Mandelbrot distribution…
Valerii Salov
arXiv · arXiv q-fin · 2012
Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expectations of the S&P 500 high frequency data in the morning of each trading day. Then, a more general fo…
Fulvio Baldovin, Francesco Camana, Massimiliano Caporin, Michele Caraglio, Attilio L. Stella
arXiv · arXiv q-fin · 2009
A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium price established mid-way through the trading session diverges to both much higher and much lower pric…
Randall A. LaViolette, Lory A. Ellebracht, Kevin L. Stamber, Charles J. Gieseler, Benjamin K. Cook
arXiv · arXiv q-fin · 2004
We present a model that investigates the spontaneous emergence of randomness in equity market microstructure. The phase space analysis of our model exposes an endogenous source of fluctuation in price and volume. We formulate a control problem for maximizing price regularity and stability while minimizing entanglement with the market, representing the NYSE specialists' affirmative obligation to maintain `fair and ord…
Ted Theodosopoulos, Muffasir Badshah
arXiv · arXiv q-fin · 2024
Developing effective quantitative trading strategies using reinforcement learning (RL) is challenging due to the high risks associated with online interaction with live financial markets. Consequently, offline RL, which leverages historical market data without additional exploration, becomes essential. However, existing offline RL methods often struggle to capture the complex temporal dependencies inherent in financi…
Suyeol Yun