As we approach 2025, financial firms must prepare for a new year of potential market volatility. That means deploying the technologies and strategies for accurate financial volatility forecasting, even as the global economy has presented some indicators of positive change.
According to a report by Forbes, the prevailing outlook for 2025 is “cautious optimism.”
“Momentum in technology innovation and lower interest rates are generally good for the stock market,” the report says. However, it also suggests that high valuations, global tensions, and uncertainty around future U.S. economic and trade policies could create risks.
In this article, we explore some of the best practices for financial firms to enhance their volatility forecasting capabilities in 2025. We also touch on the key tools and technologies firms can use to improve their forecasting capabilities.
Leveraging Advanced Volatility Forecasting Technologies
In 2025, advanced technologies will play a pivotal role in enhancing volatility forecasting capabilities for financial firms. Many financial service firms are already using the latest analytics and forecasting tools, such as artificial intelligence-powered solutions, in some capacity. However, even these technologies are likely to evolve.
Advanced Artificial Intelligence
Artificial Intelligence (AI) and Machine Learning (ML) algorithms are expected to continue to mature and deliver more accurate insights to researchers. They will likely revolutionize the way firms analyze market data and predict volatility patterns.
These technologies can process vast amounts of historical and real-time data, identifying complex relationships and patterns that human analysts might overlook.
AI-Powered Data Analytics
When AI is used to power data analytics, it can integrate diverse data from across the organization and from external sources. Advancements in this technology are also changing the very nature of applicable data for financial volatility forecasting.
For example, advancements in natural language processing (NLP) algorithms allow tools to analyze market sentiment from public sources like news articles, earnings calls, and social media posts, providing valuable insights into potential market movements. These advanced NLP tools can detect subtle nuances in language that may indicate upcoming volatility.
Quantum Computing
Quantum computing, while still in its early stages, shows the potential to transform complex financial modeling and risk assessment.
According to a report by Deloitte, mass adoption of this technology may not occur until the early 2030s. It will be used for both “offensive” capabilities—portfolio optimization, risk mitigation, and predictive modeling—and “defensive” capabilities—protecting against quantum-computer-powered cybersecurity risks.
However, quantum computers will “radically enhance computational capabilities for complex mathematical operations, such as financial simulation and modeling,” so firms should make plans now to adopt the technology.
“Firms that plan to be early movers on the offensive side will likely have to start exploring and experimenting with the potential use cases of quantum computing,” the report says. “On the defensive side, firms can start by inventorying their data and systems for the upcoming transition to postquantum cryptographic standards.”
Blockchain Technology
Finally, blockchain technology is expected to enhance transparency and reduce counterparty risk in financial transactions, potentially dampening some sources of market volatility. Smart contracts built on blockchain platforms could automate certain risk management processes, improving efficiency and reducing human error.
Financial advisors are already preparing for potential market fluctuations, with 72% planning to add more downside protection strategies to client portfolios in 2025, according to one study. The integration of advanced technologies will be crucial in developing and implementing these strategies effectively, providing more accurate volatility forecasts and enabling proactive risk management.
Enhancing Risk Management
Effective volatility forecasting depends on firms’ ability to understand the factors that will impact their investments. As financial firms prepare for potential market volatility in 2025, implementing robust risk management strategies will be crucial.
Risk Management Best Practices
Here are key approaches to risk management that financial institutions could adopt:
- Portfolio Diversification: Advisors are likely to increase portfolio diversification. This approach helps mitigate risk across various asset classes and market sectors.
- Stress Testing: Financial firms will conduct more frequent and sophisticated stress tests to assess portfolio resilience under various market scenarios, including extreme events.
- Dynamic Hedging: Implementing adaptive hedging strategies that automatically adjust based on market conditions and volatility forecasts.
- Algorithmic Trading: Employing advanced algorithms to execute trades rapidly in response to market fluctuations, helping to manage risk in real time.
- Liquidity Management: Maintaining adequate liquidity buffers to withstand potential market shocks and meet obligations during periods of heightened volatility.
- Risk Budgeting: Allocating risk across different investment strategies and asset classes to optimize portfolio performance while controlling overall risk exposure.
- Scenario Analysis: Developing and analyzing multiple potential market scenarios to prepare for various outcomes and adjust strategies accordingly.
- Tail Risk Hedging: Implementing strategies specifically designed to protect against extreme market events or “black swan” scenarios.
By implementing a combination of these approaches, financial firms can better navigate the anticipated market volatility while seeking to protect and grow client assets.
Diversifying Volatility Forecasting Methods
Relying on a single forecasting method can be risky. Financial firms should employ a diverse range of forecasting techniques, such as the following:
- GARCH models
- Implied volatility measures
- Expert judgment and qualitative analysis
- Volatility indexes
- Policy analysis
GARCH Models
GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are mathematical representations that consider the volatility clustering phenomenon observed in financial time series data. They use past market data to predict future volatility levels. However, they may struggle with sudden changes or shocks in the market, as well as capturing long-term trends.
Implied Volatility Measures
Implied volatility is derived from options prices and reflects the expectations of market participants for future volatility. It can provide insights into the sentiment and risk appetite of investors. However, it may not always accurately reflect the true underlying volatility.
Expert Judgment and Qualitative Analysis
Expert judgment and qualitative analysis involve using the knowledge and expertise of financial professionals to assess market conditions and trends. This method considers factors that may not be captured by quantitative models, such as geopolitical events and policy changes. However, it is subjective and can be influenced by biases.
Volatility Forecasting Indexes
Volatility indexes, such as the VIX (Chicago Board Options Exchange Volatility Index), track the expected volatility of S&P 500 options over the next 30 days. They provide a measure of market volatility based on investor expectations and sentiment. While they are useful for short-term forecasting, they may not be accurate in predicting longer-term trends.
Policy Analyses
Pay close attention to monetary and fiscal policy changes in major economies. Although inflation and rates of employment are stabilizing, upcoming shifts in economic policy could have a substantial impact on market volatility.
These methods should be integrated to offer a comprehensive outlook on the volatility of financial markets. Each method has its strengths and weaknesses, so combining them can provide a more accurate and robust forecast.
Navigating the Future of Financial Volatility Forecasting
As we look ahead to 2025, financial firms must adapt their volatility forecasting practices to keep pace with the evolving market landscape. By leveraging advanced technologies, enhancing risk management strategies, incorporating broader economic factors, and embracing collaboration, firms can improve their ability to navigate market volatility and make informed decisions.
Contact us at Option One Technologies today to learn more about how your organization can make more accurate predictions using technology.
