Machine learning has many applications in finance, such as asset pricing, risk management, portfolio optimization, and fraud detection. In this post, I discuss the use of machine learning in forecasting volatility.
Using Machine Learning to Predict Market Volatility
The unpredictability of the markets is a well-known fact. Despite this, many traders and portfolio managers continue to try to predict market volatility and manage their risks accordingly. Usually, econometric models such as GARCH are used to forecast market volatility.
In recent years, machine learning has been shown to be capable of predicting market volatility with accuracy. Reference [1] explored how machine learning can be used in this context.
Findings
-Machine learning models can accurately forecast stock return volatility using a small set of key predictors: realized volatility, idiosyncratic volatility, bid-ask spread, and returns.
-These predictors align with existing empirical findings, reinforcing the traditional risk-return trade-off in finance.
-ML methods effectively capture both the magnitude and direction of predictor impacts, along with their interactions, without requiring pre-specified model assumptions.
-Large current-period volatility values strongly predict higher future volatility; small values have a muted or negative impact.
-LSTM models outperform feedforward neural networks and regression trees by leveraging temporal patterns in historical data.
-An LSTM using only volatility and return history over one year performs comparably to more complex models with additional predictors.
-LSTM models function as distribution-free alternatives to traditional econometric models like GARCH.
-Optimal lag length remains critical in LSTM performance and must be selected through model training.
-The study reports an average predicted realized volatility of 44.1%, closely matching the actual value of 43.8%.
-Out-of-sample R² values achieved are significantly higher than those typically reported in related volatility forecasting literature.
In short, the paper aimed to demonstrate the potential of machine learning for modeling market volatility. In particular, the authors have shown how the LSTM model can be used to predict market volatility and manage risks. The results suggest that this is a promising alternative approach to traditional econometric models like GARCH.
Reference
[1] Filipovic, Damir and Khalilzadeh, Amir, Machine Learning for Predicting Stock Return Volatility (2021). Swiss Finance Institute Research Paper No. 21-95
Machine Learning Models for Predicting Implied Volatility Surfaces
The Implied Volatility Surface (IVS) represents the variation of implied volatility across different strike prices and maturities for options on the same underlying asset. It provides a three-dimensional view where implied volatility is plotted against strike price (moneyness) and time to expiration, capturing market sentiment about expected future volatility.
Reference [2] examines five methods for forecasting the Implied Volatility Surface of short-dated options. These methods are applied to forecast the level, slope, and curvature of the IVS.
Findings
-The study evaluates five methods—OLS, AR(1), Elastic Net, Random Forest, and Neural Network—to forecast the implied volatility surface (IVS) of weekly S&P 500 options.
-Forecasts focus on three IVS characteristics: level, slope, and curvature.
-Random Forest consistently outperforms all other models across these three IVS dimensions.
-Non-learning-based models (OLS, AR(1)) perform comparably to some machine learning methods, highlighting their continued relevance.
-Neural Networks forecast the IVS level reasonably well but perform poorly in predicting slope and curvature.
-Elastic Net, a linear machine learning model, is consistently outperformed by the non-linear models (Random Forest and Neural Network) for the level characteristic.
-The study emphasizes the importance of model selection based on the specific IVS characteristic being forecasted.
-Performance evaluation is supported using the cumulative sum of squared error difference (CSSED) and permutation variable importance (VI) metrics.
-The research highlights the utility of Random Forest in capturing complex, non-linear patterns in IVS dynamics.
-Accurate IVS forecasting is valuable for derivative pricing, hedging, and risk management strategies.
This research highlights the potential of machine learning in forecasting the implied volatility surface, a key element in options pricing and risk management. Among the five methods studied, Random Forest stands out as the most consistent and accurate across multiple IVS features.
Reference
[2] Tim van de Noort, Forecasting the Characteristics of the Implied Volatility Surface for Weekly Options: How do Machine Learning Methods Perform? Erasmus University, 2024
Closing Thoughts
These studies highlight the growing effectiveness of machine learning in financial forecasting, particularly for market volatility and implied volatility surfaces. Models like LSTM and Random Forest demonstrate clear advantages over traditional methods by capturing complex patterns and dependencies. As financial markets evolve, leveraging such tools offers a promising path for enhancing predictive accuracy and risk management.