Introduction
Predicting financial market indices like the VXX (an ETN that tracks short-term VIX futures) is a challenging task, given the complexity and interdependencies of market dynamics. In this project, we tackled this problem using a combination of ensemble models and spread-based analysis, blending sophisticated machine learning techniques with financial intuition.
This article outlines the steps we followed, the models used, and the insights derived from the analysis.
Objective
The primary goal was to predict the VXX for a specific timeframe using:
VIX predictions as a proxy for VXX (adjusted for alignment).
A spread-based model incorporating the relationship between VXX and VXZ.
This approach was designed to capture both market index movements and the spread dynamics between short-term and mid-term VIX futures.
Steps Taken
1. Data Preparation
We started by loading datasets for key indices, including:
SPX, VIX, DOW, NASDAQ, RUSSELL, and SOX (starting from 2014).
VXX and VXZ (starting from 2018).
2. Modeling Approach
Step 1: Voting Model
We employed a Voting Regressor, an ensemble model combining:
Random Forest: Captures non-linear relationships.
XGBoost: A robust boosting algorithm.
Features: SPX, VIX, DOW, NASDAQ, RUSSELL, and SOX.Target: VIX (as a proxy for VXX).The model output provided a baseline prediction for VIX, which was aligned to VXX for scaling differences.
Step 2: Quantile Model
To incorporate variability in predictions, we trained a Quantile Regression Model using the same features. This provided an alternative prediction for VIX, accounting for different quantiles.
Step 3: VXX-VXZ Spread Model
Recognizing the relationship between VXX and VXZ (short-term and mid-term VIX futures), we calculated the VXX-VXZ spread and used it as a feature to predict VXX directly. A Random Forest model was trained on this feature.
3. Combining Predictions
The final prediction for VXX was generated by:
Averaging the predictions from the Voting Model and Quantile Model (aligned to VXX).
Adding the output of the VXX-VXZ Spread Model.
This approach blended macro-market dynamics (via VIX-based models) with futures term structure (via the VXX-VXZ spread).
Evaluation
Performance Metrics
Mean Absolute Error (MAE): Quantifies average prediction error.
Mean Squared Error (MSE): Highlights larger errors.
R-squared (R²): Measures variance explained by the model.
These metrics provided quantitative validation of model accuracy.
Visualization
A plot of Actual vs Predicted VXX revealed how well the models captured market movements over time. The alignment between the actual and predicted values highlighted the model's ability to track real-world trends.
Insights and Challenges
Strengths:
The ensemble approach effectively captured market trends.
The VXX-VXZ spread added predictive power by incorporating futures term structure.
Limitations:
Prediction accuracy varied during periods of extreme market volatility (e.g., March 2020).
The models' performance could be enhanced by including additional features like macroeconomic indicators or sentiment data.
Conclusion
This project demonstrated the potential of machine learning in predicting complex financial instruments like VXX. By integrating multiple models and leveraging the relationship between VIX, VXX, and VXZ, we achieved a robust framework for prediction.
Future work could explore:
Incorporating advanced deep learning models for time series analysis.
Using external data (e.g., news sentiment, economic indicators) to improve prediction accuracy.
Disclaimer
This report is provided for informational purposes only and does not constitute financial, investment, or trading advice. Market conditions can change rapidly, and this report reflects publicly available data as of December 19, 2024. For personalized advice, consult a licensed financial advisor. Neither the authors nor distributors accept liability for any losses incurred directly or indirectly from reliance on this information.
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