Modeling CSI 300 Futures Price Response to Order Flow Imbalance

Summary

The study models how order flow imbalance relates to price dynamics in CSI 300 index futures. It treats imbalance as a market shock and represents its impact with a mean-reverting Ornstein–Uhlenbeck process driven by a jump-type Lévy process, rather than the commonly used Hawkes-process approach. The authors replace the drift in a geometric Brownian motion price model with this stochastic process, derive equations for log returns and their mean and variance, and examine a response ratio based on trading when imbalance reaches a trigger level.

The reported findings are that relationships between imbalance and conventional measures vary with forecast horizon, and that imbalance's memory and forecasting ability depend on market regime. The authors propose using these patterns to screen existing indicators and evaluate new ones in advance. The document gives no numerical estimates, sample details, or out-of-sample performance figures, so it supports a modeling framework and qualitative claims rather than a directly assessable trading result.

Key ideas

• The model treats order flow imbalance as a shock with mean-reverting memory and jump-driven dynamics.
• The proposed price model uses an Ornstein–Uhlenbeck process in place of the usual geometric Brownian motion drift.
• The study derives log-return, mean, variance, and response-ratio processes under stated boundary conditions.
• Relationships between imbalance and conventional measures vary with forecast horizon.
• Imbalance memory and predictive power differ across market regimes.

Tags

# Stochastic Price Dynamics in Response to Order Flow Imbalance: Evidence from CSI 300 Index Futures


# Stochastic Price Dynamics in Response to Order Flow Imbalance: Evidence from CSI 300 Index Futures









We conduct modeling of the price dynamics following order flow imbalance in market microstructure and apply the model to the analysis of Chinese CSI 300 Index Futures. There are three findings. The first is that the order flow imbalance is analogous to a shock to the market. Unlike the common practice of using Hawkes processes, we model the impact of order flow imbalance as an Ornstein-Uhlenbeck process with memory and mean-reverting characteristics driven by a jump-type Lévy process. Motivated by the empirically stable correlation between order flow imbalance and contemporaneous price changes, we propose a modified asset price model where the drift term of canonical geometric Brownian motion is replaced by an Ornstein-Uhlenbeck process. We establish stochastic differential equations and derive the logarithmic return process along with its mean and variance processes under initial boundary conditions, and evolution of cost-effectiveness ratio with order flow imbalance as the trading trigger point, termed as the quasi-Sharpe ratio or response ratio. Secondly, our results demonstrate horizon-dependent heterogeneity in how conventional metrics interact with order flow imbalance. This underscores the critical role of forecast horizon selection for strategies. Thirdly, we identify regime-dependent dynamics in the memory and forecasting power of order flow imbalance. This taxonomy provides both a screening protocol for existing indicators and an ex-ante evaluation paradigm for novel metrics.

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