Modeling Limit Order Book Dynamics with Compound Hawkes Process

Core Concepts

Compound Hawkes Process enhances modeling of order book dynamics by incorporating order size variability and time-of-day dependencies.

Abstract

The study introduces a novel approach using Compound Hawkes Process to model Limit Order Book dynamics, considering order size variability and time-of-day dependencies. The research focuses on maintaining positive spread, calibrating Hawkes kernels, and simulating market impact. Empirical data analysis reveals preferences for round order sizes and intraday seasonality in trading volumes. The study compares the proposed model against baselines, showcasing its ability to replicate key features of an equity stock's Limit Order Book.

Stats

"We showcase the results and quality of fits for an equity stock’s LOB in the NASDAQ exchange." "For example, we see more than 40% of the market orders and 60% of limit orders are of 100 size." "Naturally, Cancel Orders’ sizes are capped at the size of an individual order."

Quotes

"We propose a novel methodology of using Compound Hawkes Process for the LOB where each event has an order size sampled from a calibrated distribution." "In this work we show some lack of support for the hypothesis that the order arrival intensities are impacted by the past order sizes." "We conclude that the Compound Hawkes Process is a suitable candidate for the model."

Key Insights Distilled From

Limit Order Book Dynamics and Order Size Modelling Using Compound Hawkes Process

by Konark Jain,... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2312.08927.pdf

Deeper Inquiries

How does incorporating order size variability improve market impact simulations?

Incorporating order size variability in market impact simulations improves the accuracy and realism of the simulation by capturing the actual dynamics of order flow in financial markets. By considering different order sizes, the simulation can better reflect how larger orders have a more significant impact on prices compared to smaller orders. This is crucial for understanding how trading activity influences price movements and liquidity in real-world scenarios. Additionally, incorporating order size variability allows for a more nuanced analysis of market behavior, including examining the effects of different trade sizes on price changes and overall market conditions.

What potential limitations or biases could arise from assuming independent order sizes?

Assuming independent order sizes in simulations may introduce certain limitations and biases that could affect the accuracy of the results: Lack of Realism: Independent order sizes may not accurately represent actual trading behavior where traders often place orders based on various factors such as market conditions, strategies, or available liquidity. Impact on Market Dynamics: Ignoring correlations between order sizes could lead to unrealistic market impact predictions as large trades might be overestimated or underestimated without considering their relationship with other orders. Risk Management: Inaccurate representation of order sizes could result in flawed risk management strategies as they rely heavily on understanding potential impacts of different trade volumes. Algorithmic Trading Strategies: Algorithms designed based on assumptions about independent order sizes may not perform optimally when faced with real-world scenarios where correlations between trades exist.

How might these findings impact algorithmic trading strategies in real-world financial markets?

The findings from incorporating realistic features like variable-order-size modeling into simulations can have several implications for algorithmic trading strategies: Improved Strategy Performance: Algorithmic trading models that account for varying trade volumes are likely to make more informed decisions leading to improved performance outcomes. Enhanced Risk Management: By accurately assessing the potential impacts of different trade sizes, algorithms can better manage risks associated with large transactions and volatile markets. Market Impact Mitigation: Strategies that consider correlated behaviors among traders regarding their transaction volumes can help mitigate adverse effects on prices caused by large trades. Increased Adaptability: Algorithms equipped with insights from realistic simulations are more adaptable to changing market conditions and able to adjust their tactics accordingly. These findings underscore the importance of developing algorithmic trading strategies that reflect actual market dynamics through comprehensive modeling techniques like Compound Hawkes Processes with variable-order-size considerations for enhanced performance and risk management capabilities in real-world financial markets.