Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data

ArXiv ID: 2306.12446 “View on arXiv”

Authors: Unknown

Abstract

This study aims to compare multiple deep learning-based forecasters for the task of predicting volatility using multivariate data. The paper evaluates a range of models, starting from simpler and shallower ones and progressing to deeper and more complex architectures. Additionally, the performance of these models is compared against naive predictions and variations of classical GARCH models. The prediction of volatility for five assets, namely S&P500, NASDAQ100, gold, silver, and oil, is specifically addressed using GARCH models, Multi-Layer Perceptrons, Recurrent Neural Networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In the majority of cases, the Temporal Fusion Transformer, followed by variants of the Temporal Convolutional Network, outperformed classical approaches and shallow networks. These experiments were repeated, and the differences observed between the competing models were found to be statistically significant, thus providing strong encouragement for their practical application.

Keywords: volatility forecasting, Temporal Fusion Transformer, GARCH models, recurrent neural networks (RNN), temporal convolutional networks (TCN)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper applies standard deep learning architectures (RNNs, CNNs, Transformers) and classical GARCH models with relatively low mathematical derivation, focusing on implementation and benchmarking. It demonstrates strong empirical rigor through backtesting on multiple assets, statistical significance testing, and comparison against baselines.

  flowchart TD
    A["Research Goal<br>Compare DL Models for<br>Multivariate Volatility Prediction"] --> B["Data Preparation<br>5 Assets: S&P500, NASDAQ100, Gold, Silver, Oil"]
    
    B --> C["Model Implementation"]
    
    subgraph C ["Computational Processes"]
        C1["GARCH Models<br>Classical Baseline"]
        C2["MLP<br>Shallow Network"]
        C3["RNN<br>Recurrent Neural Net"]
        C4["TCN<br>Temporal Convolutional Net"]
        C5["TFT<br>Temporal Fusion Transformer"]
    end
    
    C --> D["Statistical Evaluation<br>Repeated Experiments & Significance Testing"]
    
    D --> E{"Key Findings/Outcomes"}
    
    E --> F["TFT & TCN Variants<br>Outperform All Others"]
    E --> G["Significant Improvement<br>Over GARCH & Shallow Networks"]
    E --> H["Strong Case for<br>Practical Application"]