Surreal illustration of financial market analysis with clocks showing trade durations.

Decoding Trade Durations: How Zero-Inflated Models are Changing Finance

Rhea Morgan in Business & Economy December 2025 • 4 min read.

"Discover how innovative statistical models are revolutionizing the analysis of high-frequency trading data, offering new insights into market dynamics and trade behaviors."

In the fast-paced world of finance, understanding the timing between transactions—known as trade durations—is crucial. Traditionally, financial analysts have used autoregressive conditional duration (ACD) models to analyze these durations, but these models often overlook a common issue: the frequent occurrence of zero values. These zero durations, representing trades that happen at the exact same time, present a significant challenge.

The standard approach has been to simply discard these zero values, an approach with obvious drawbacks. This method assumes that trades occurring at the same time are related, which isn't always the case. Merging unrelated transactions can distort the data and lead to inaccurate conclusions. The alternative of assigning a small value to zero durations is equally problematic, as it's arbitrary and can skew the distribution of durations near zero.

Enter the zero-inflated autoregressive conditional duration (ZIACD) model, a sophisticated statistical tool designed to handle excessive zero values in trade duration data. This innovative model, based on the zero-inflated negative binomial distribution, distinguishes between split transactions (large trades broken into smaller ones) and independent trades that coincidentally occur at the same time. By accurately modeling these zero values, the ZIACD model provides a more detailed and reliable analysis of market behavior.

Why Traditional Models Fall Short: The Zero-Duration Dilemma

Surreal illustration of financial market analysis with clocks showing trade durations.

Traditional ACD models typically rely on continuous distributions that cannot accommodate zero values, forcing analysts to discard or adjust these data points. This creates several problems:

Discarding zero durations: This assumes that all simultaneous trades are related, which may not be true. Unrelated trades occurring at the same time are also discarded, leading to a loss of information.

Arbitrary adjustments: Replacing zero durations with small values is subjective and distorts the distribution of durations near zero.
Loss of precision: Datasets with millisecond or even nanosecond timestamps are increasingly common. However, traditional models often fail to fully utilize this precision, as split transactions might not occur at the exact same time.

These shortcomings highlight the need for a more robust approach that can effectively handle zero values and provide a more accurate representation of trade durations. The ZIACD model addresses these issues by explicitly modeling the excess zeros, offering a more nuanced understanding of market dynamics.

The Future of Trade Duration Analysis: Embracing Complexity

The zero-inflated autoregressive conditional duration (ZIACD) model represents a significant advancement in the analysis of trade durations, providing a more accurate and detailed understanding of market dynamics. By explicitly modeling excessive zero values, this approach overcomes the limitations of traditional models and offers new insights into trade behaviors. As financial markets continue to evolve, embracing such innovative statistical tools will be essential for staying ahead of the curve and making informed decisions.

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This article is based on research published under:

DOI-LINK: 10.1515/snde-2022-0008,

Title: Zero-Inflated Autoregressive Conditional Duration Model For Discrete Trade Durations With Excessive Zeros

Subject: q-fin.st stat.me

Authors: Francisco Blasques, Vladimír Holý, Petra Tomanová

Published: 18-12-2018

Everything You Need To Know

What are trade durations and why are they important in finance?

Trade durations refer to the time intervals between transactions in financial markets. Understanding trade durations is crucial because it provides insights into market activity, liquidity, and investor behavior. Analyzing these durations helps financial analysts uncover patterns and dynamics that can inform trading strategies and risk management.

What are Autoregressive Conditional Duration (ACD) models and what limitations do they have when analyzing trade durations?

Autoregressive Conditional Duration (ACD) models are statistical tools used to analyze the time between transactions. However, traditional ACD models often struggle with the frequent occurrence of zero values in trade duration data, which represent trades that happen at the exact same time. These models typically rely on continuous distributions that cannot accommodate zero values, forcing analysts to discard or arbitrarily adjust these data points, leading to a loss of information and potential inaccuracies in the analysis.

How does the zero-inflated autoregressive conditional duration (ZIACD) model address the limitations of traditional ACD models in analyzing trade durations?

The zero-inflated autoregressive conditional duration (ZIACD) model addresses the limitations of traditional ACD models by explicitly modeling the excess of zero values in trade duration data. Unlike traditional ACD models that discard or adjust zero durations, the ZIACD model, based on the zero-inflated negative binomial distribution, distinguishes between split transactions and independent trades that coincidentally occur at the same time. This approach provides a more accurate and nuanced analysis of market behavior, as it avoids distorting the data and losing valuable information.

Why is it problematic to simply discard zero values or assign small values to zero durations when analyzing trade data?

Discarding zero durations assumes that all simultaneous trades are related, which may not be the case, leading to the loss of information from unrelated trades occurring at the same time. Assigning small values to zero durations is arbitrary and can skew the distribution of durations near zero, distorting the analysis. Both approaches fail to accurately represent the underlying market dynamics and can lead to inaccurate conclusions about trade behaviors.

In what ways does embracing the zero-inflated autoregressive conditional duration (ZIACD) model represent a shift in how trade duration analysis is approached in finance, and what are the implications for understanding market behavior?

Embracing the zero-inflated autoregressive conditional duration (ZIACD) model signifies a shift towards embracing complexity and nuance in trade duration analysis. By explicitly modeling excessive zero values, the ZIACD model provides a more detailed and reliable analysis of market behavior than traditional ACD models. This deeper understanding can lead to more informed trading strategies, improved risk management, and a more accurate assessment of market liquidity. Furthermore, as financial markets evolve with increasing data precision (e.g., millisecond or nanosecond timestamps), the ZIACD model allows analysts to fully utilize this precision, capturing the subtleties of split transactions and independent trades occurring at nearly the same time. This enhanced level of analysis is essential for staying ahead in the fast-paced world of finance and making informed decisions based on a comprehensive understanding of market dynamics.