DEPARTMENT OF MANAGEMENT STUDIES
INDIAN INSTITUTE OF SCIENCE
Ph.D. Thesis Colloquium of
Mr. Aditya Nittur Anantha
(Research Supervisors: Dr. Shashi Jain & Dr. Santosh Chavan)
Date: 25th November 2025 [Tuesday]
Time: 10:00 AM
Venue: Annex Classroom No:2 [Management Studies]
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489 498 481 574 85
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Title: “Order-Flow Modelling for Liquidity and Quoting in Event Time”
Abstract:
This thesis investigates topics in high-frequency trading. Models and methods are presented for order flow dynamics, liquidity measurement, and quoting policy through the lens of modern market microstructure. The thesis develops event-driven models and frameworks that improve both the measurement of liquidity and the design of quoting policy suited to high-frequency markets.
The first objective defines an order flow imbalance (OFI) measure in event time. OFI is a well-established directional indicator for short-horizon returns (see Cont, Kukanov and Stoikov, 2014). While the relationship between future returns and OFI, as well as the effect of past return dynamics on OFI (Chordia and Subrahmanyam, 2002), is well documented, a general method to forecast OFI under both calendar-time and event-time representations is lacking. Existing OFI forecasting approaches typically assume calendar-time sampling, which makes their use with high-frequency event-time data challenging (Chordia and Subrahmanyam, 2002; Easley, López de Prado and O’Hara, 2012).
To address this gap, the thesis proposes a generic forecasting algorithm for OFI that can be used with both calendar-time and event-time models. By modelling the buy and sell event streams as a bivariate Hawkes process, the framework captures the cross-excitation and self-excitation effects inherent in order flow. OFI is constructed by aggregating order flow in event time, enabling high-frequency forecasts of buy–sell imbalance. To evaluate competing forecasts from a family of models, the thesis develops a forecast evaluation framework for OFI that accommodates calendar-time models (such as Vector Auto Regression, VAR) and event-time models (such as Hawkes processes). Within this framework, a parsimonious loss function is introduced to enable systematic forecast comparison across models, allowing robust short-horizon OFI forecasts that can be calibrated to market regime.
The second objective extends the methodology to the problem of multi-contract quoting. Many algorithmic trading strategies rely on simultaneous quoting across contracts (Cartea, Jaimungal and Penalva, 2015; Guéant, 2013; Bergault, Guéant and Stoikov, 2021). While simultaneous execution is often the desired outcome, market design typically constrains multi-contract quoting to sequential execution, so the order in which contracts are executed matters. Empirical evidence shows that this execution order affects transaction cost (Cont, Kukanov and Stoikov, 2014). The difference between the desired or quoted spread and the realized spread—slippage (Hasbrouck, 2007; O’Hara, 1995)—is closely linked to execution order.
The thesis posits that the choice of a reference contract anchors the spread. It then compares relevant market information for the price stability of this reference contract under two settings: (i) temporal evolution of order flow, and (ii) a derived estimate based on the instantaneous limit order book (LOB) state. Using multivariate Hawkes processes, it models order flow to construct a stability indicator for reference contract choice that guides liquidity provision when markets are interlinked. A novel Composite Liquidity Factor (CLF) is proposed to measure reference-contract stability from the instantaneous order book. This framework employs the OFI methodology developed in the first objective to forecast dependent order flow across multiple contracts, thereby informing quoting decisions. A common benchmark is established for evaluating quoting performance under liquidity constraints, contrasting relative order flow dynamics with indicators derived from the instantaneous LOB.
The third objective deepens this analysis by examining the stability of the reference price in multi-contract high-frequency quoting. It studies how transient order events and noisy updates weaken reference price reliability and proposes filtration methods to extract more robust signals. The central trade-off is explicit: overly aggressive filtering can delay or attenuate reference updates, increasing slippage, whereas insufficient filtering passes transient disturbances directly into quotes.
To navigate this trade-off, the thesis develops a diagnostic ladder that evaluates candidate filters on transparent criteria: stability, computational efficiency, alignment with event-time order-flow pressure, and execution impact. It then identifies conditions under which filtration improves reference stability and enhances the effectiveness of quoting strategies. The diagnostic ladder is used to contrast the efficacy of OFI via two formulations. The first uses all order flow events under filtration to construct an Order Book Imbalance (OBI) measure. The second uses only trade events, as in the first objective, to construct OFI, which can be viewed as OBI filtered for trade events. The diagnostic ladder then measures how these indicators associate with returns under multiple conditions. Empirically, filtration is shown to improve the association between returns and OFI, whereas, under filtration, the association between OBI and returns shows no significant improvement.
Taken together, these contributions propose a methodology for modelling and evaluating OFI forecasts, analysing multi-contract quoting policy under liquidity constraints, and assessing the effect of order-flow filtration on OFI stability. Parsimonious frameworks are developed as methodological contributions in each objective, allowing integration into practical trading and liquidity management applications. Empirical analyses with high-frequency data sets illustrate the application of the proposed models and methodology.
Keywords: Order Flow Imbalance (OFI), Order Book Imbalance (OBI), High-Frequency Trading, Liquidity, Multi-Contract Quoting, Hawkes Processes, Limit Order Book, Filtration
References
[1] Rama Cont, Alexei Kukanov, and Sasha Stoikov. Price dynamics in a markovian limit order market. Quantitative Finance, 14(1):61–71, 2014.
[2] Tarun Chordia and Avanidhar Subrahmanyam. Order imbalance and individual stock returns. Journal of Financial Economics, 65(1):41–68, 2002.
[3] David Easley, Marcos M López de Prado, and Maureen O’Hara. The microstructure of the “flash crash”: Flow toxicity, liquidity crashes, and the probability of informed trading. Journal of Portfolio Management, 37(2):118–128, 2012.
[4] Álvaro Cartea, Sebastian Jaimungal, and José Penalva. Algorithmic and High-Frequency Trading. Cambridge University Press, 2015.
[5] Olivier Guéant. The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making. CRC Press, 2013.
[6] Pierre Bergault, Olivier Gueant, and Sasha Stoikov. Market making with high-frequency signals. Quantitative Finance, 21(2):199–215, 2021.
[7] Joel Hasbrouck. Empirical Market Microstructure: The Institutions, Economics, and Econo-metrics of Securities Trading. Oxford University Press, 2007.
[8] Maureen O’Hara. Market Microstructure Theory. Blackwell, 1995.
ALL ARE WELCOME