DEPARTMENT OF MANAGEMENT STUDIES
INDIAN INSTITUTE OF SCIENCE
Ph.D. Thesis Colloquium of
Mr. Aditya Nittur Anantha
Thesis Supervisor: Prof. Shashi Jain
Date: 25th November 2025 [Tuesday]
Time: 10:00 AM
Venue: Annex Classroom No:2 [Management Studies]
Title: “Order-Flow Modelling for Liquidity and Quoting in Event Time”
Abstract:
This thesis investigates high-frequency order flow dynamics, liquidity measurement, and quoting policy through the lens of modern market microstructure. It develops event-driven models and frameworks that improve both the measurement of liquidity and the design of quoting policy in high-frequency markets.
In the first objective, we define an order flow imbalance (OFI) measure in event time. OFI is a well-established directional indicator for short-horizon returns [1]. While the relationship between future returns and OFI [1], as well as the effect of past return dynamics on OFI [2]is established, we find that a general method to forecast OFI under both calendar-time and event-time representations is lacking in the current literature. Prevailing forecasting approaches for OFI assume calendar-time samples, which makes their use with high-frequency event time data challenging [2, 3]. We propose a generic forecasting algorithm for OFI which can be used with both calendar-time and event-time models. By modeling the buy and sell event streams as a bivariate Hawkes process, we capture the cross excitation and self excitation effects inherent in order flow. Our method of constructing OFI aggregates order flow in event-time, allowing for high-frequency forecasts of buy-sell imbalance. In order to evaluate comparative forecasts from a family of models, we develop a forecast evaluation framework for OFI that accommodates both calendar-time models, such as Vector Auto Regression (VAR), and event-time models, such as Hawkes processes. Within this framework, we introduce a parsimonious loss function to enable
systematic forecast comparison across models. This framework allows for robust short-horizon forecasts of high-frequency OFI, which can be calibrated to market regime.
The second objective extends the methodology to the problem of multi-contract quoting. Many algorithmic trading strategies are predicated on simultaneous quoting across contracts [4–6]. While simultaneous execution is the desired outcome, market design often constrains multi-contract quoting to sequential execution. As a consequence, when quoting across multiple contracts, the order of execution matters. Empirically, it is established in the current literature that the order of execution controls cost [1]. Specifically, the difference between the desired or quoted spread and the realized spread, termed as slippage [7, 8], is associated with the order of execution. We posit that the choice of reference contract anchors the spread. This study compares relevant market information for the price stability of the reference contract under two settings – temporal evolution of order flow, and as a derived estimate from the instantaneous snapshot, or state, represented by the limit order book (LOB). Using multivariate Hawkes processes, we model the order flow to construct a stability indicator for reference contract choice that guides liquidity provision when markets are interlinked. We propose a novel Composite Liquidity Factor (CLF), which measures reference contract stability from the instantaneous order book. This framework employs the methodology developed in the first objective to forecast dependent order flow across multiple contracts to inform quoting decisions. We establish a common benchmark for evaluating quoting performance under liquidity constraints, contrasting relative order flow dynamics with indicators derived from the instantaneous LOB.