Night cityscape with glowing stock charts and a protective shield, symbolizing tail risk management.

Is Your Portfolio Ready for Anything? Mastering Tail Risk Forecasting

Mira Elwood in Business & Economy December 2025 • 4 min read.

"Discover how incorporating overnight information can revolutionize your investment strategy and protect against extreme market events."

In today's volatile financial landscape, safeguarding your investments requires more than just traditional risk management techniques. The concept of 'tail risk' – the potential for extreme, unexpected losses – has gained prominence, compelling investors to seek more sophisticated methods for predicting and mitigating these threats. This article delves into the innovative approaches being developed to forecast tail risk, focusing on the significant role of overnight information in enhancing the accuracy of risk models.

Traditional Value-at-Risk (VaR) measurements, while useful, often fall short by only indicating the maximum potential loss at a specific confidence level, without detailing what could happen in more extreme scenarios. Expected Shortfall (ES) offers a more comprehensive view by calculating the average loss in those worst-case scenarios, making it a critical tool for truly understanding and preparing for market turbulence. Regulators increasingly favor ES, as seen in the Basel III framework, which advocates for using both VaR and ES to assess market risk comprehensively.

Recent research has spotlighted the importance of incorporating high-frequency data and overnight information into risk models. Overnight returns, reflecting events that occur outside regular trading hours, significantly contribute to overall return volatility. By integrating this data, along with realized volatility measures, semi-parametric regression models are becoming increasingly adept at forecasting tail risk, providing investors with a more robust defense against market uncertainties.

Why Overnight Information is the Missing Piece in Your Risk Forecast

Night cityscape with glowing stock charts and a protective shield, symbolizing tail risk management.

Modern financial markets operate around the clock, but major stock exchanges typically only function during standard trading hours. This creates information gaps, particularly between the closing price of one day and the opening price of the next. These gaps reflect overnight news, economic data releases, and global events that can significantly alter investor sentiment and market expectations.

Understanding and incorporating overnight returns into risk models is crucial. Overnight information captures a substantial portion of the total daily return volatility, adding a layer of unpredictability that traditional models often miss. By focusing on the difference between the opening and closing prices, investors gain a more immediate and accurate snapshot of market risk.

Here's why overnight information matters:

Global Interconnectedness: News and developments in foreign markets can impact domestic markets overnight.
Unpredictable Events: Key announcements, earnings reports, and geopolitical events often occur outside trading hours.
Investor Sentiment: Overnight news affects investor sentiment, leading to price adjustments when the market reopens.

The integration of overnight data allows for a nowcasting approach, where risk forecasts are updated immediately after the market opens, providing a timely assessment based on the most recent information. This approach is a significant leap forward from models that rely solely on historical intraday data.

Future-Proofing Your Investment Strategy

As financial markets evolve, so too must the strategies used to manage risk. Incorporating overnight information into semi-parametric regression models represents a significant advancement in tail risk forecasting. By embracing these innovative approaches, investors can better protect their portfolios from extreme market events and position themselves for long-term success. Whether you are an institutional investor or managing your personal finances, understanding and utilizing these models is essential for navigating the complexities of today's global economy.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.07134,

Title: Tail Risk Forecasting With Semi-Parametric Regression Models By Incorporating Overnight Information

Subject: q-fin.rm stat.ap

Authors: Cathy W. S. Chen, Takaaki Koike, Wei-Hsuan Shau

Published: 11-02-2024

Everything You Need To Know

What exactly is 'tail risk' and why should investors be concerned about it?

'Tail risk' refers to the potential for extreme, unexpected losses in an investment portfolio. Investors should be concerned because traditional risk management techniques may not adequately prepare them for these events. Methods for forecasting tail risk help mitigate these threats. Understanding tail risk, along with employing innovative approaches, is crucial for protecting investments against unexpected market shocks and navigating the complexities of modern financial markets.

How does Expected Shortfall (ES) improve upon traditional Value-at-Risk (VaR) measurements for assessing market risk?

Traditional Value-at-Risk (VaR) indicates the maximum potential loss at a specific confidence level but doesn't detail what could happen in more extreme scenarios. Expected Shortfall (ES) offers a more comprehensive view by calculating the average loss in those worst-case scenarios. This makes ES a more critical tool for truly understanding and preparing for market turbulence, as it provides a fuller picture of potential losses beyond the VaR threshold. Regulators, like those implementing the Basel III framework, favor ES for a more comprehensive market risk assessment.

Why is overnight information considered a 'missing piece' in traditional risk forecasting models?

Overnight information captures events and data releases that occur outside regular trading hours, which can significantly impact investor sentiment and market expectations. Traditional models often miss this layer of unpredictability because they primarily focus on intraday data. Integrating overnight returns, which reflect news, economic data, and global events, provides a more immediate and accurate snapshot of market risk and accounts for a substantial portion of the total daily return volatility.

How do semi-parametric regression models, enhanced with overnight data and realized volatility measures, improve tail risk forecasting?

Semi-parametric regression models, when enhanced with overnight data and realized volatility measures, become more adept at forecasting tail risk by incorporating a broader range of information that influences market behavior. Overnight data captures the impact of global events and news occurring outside of standard trading hours, while realized volatility measures provide insights into actual market fluctuations. By integrating these elements, these models offer a more robust defense against market uncertainties, enabling investors to better anticipate and manage extreme market events.

What is the significance of using a 'nowcasting' approach with overnight data, and how does it differ from traditional methods?

A 'nowcasting' approach, which updates risk forecasts immediately after the market opens using overnight data, provides a timely assessment based on the most recent information. This differs significantly from traditional methods that rely solely on historical intraday data, which may not reflect current market conditions or overnight events. By incorporating overnight information, nowcasting offers a more immediate and accurate snapshot of market risk, allowing investors to make more informed decisions in response to rapidly changing market dynamics. This approach represents a leap forward in providing relevant and up-to-date risk assessments.