Cryptocurrency markets are highly volatile, with large price swings creating significant risks for traders and investors. This project aims to develop a high-performance, real-time risk modeling system for cryptocurrency markets by leveraging parallel computing techniques. Specifically, it focuses on modeling volatility clustering and simulating risk metrics, such as Value at Risk (VaR) and Expected Shortfall (ES), using Monte Carlo simulations. These simulations will be accelerated via GPU or CPU parallelization, enabling real-time risk assessment and management.
Cryptocurrencies exhibit extreme volatility, presenting unique challenges for risk management. The high-frequency, high-volume nature of crypto markets makes traditional computational methods insufficient for real-time modeling. By leveraging high-performance computing (HPC) and parallel processing, this project aims to provide efficient solutions for analyzing and predicting crypto asset risks. This approach aligns with ECE 759s focus on optimizing performance in complex, data-intensive systems.
This project integrates key aspects of high-performance computing:
- Parallel Algorithm Design: Monte Carlo simulations distributed across multiple GPU cores.
- Optimization Techniques: Efficient memory management and computational optimization for large-scale simulations.
- Load Balancing: Distribution of computational tasks across multiple GPU cores to maximize throughput.
- Volatility Modeling: GARCH/EGARCH models to capture volatility clustering.
- Monte Carlo Simulation: Generation of future price paths under various risk conditions.
- Risk Metrics: Calculation of Value at Risk (VaR), Expected Shortfall (ES), and stress testing.
- Extreme Value Theory (EVT): Potential exploration for modeling rare events.
- GPU Acceleration: Parallel Monte Carlo path generation to simulate thousands of price paths simultaneously.
- Batch Processing: Efficient handling of high-frequency data streams in real-time.
- Advanced Modeling: Potential implementation of regime-switching models to better capture volatility patterns.
By the project deadline, we aim to deliver:
- A fully functional prototype of the risk modeling system with GPU-accelerated Monte Carlo simulations and volatility clustering models.
- Real-time risk metrics (e.g., VaR and ES) displayed through a user-friendly dashboard.
- Performance comparison between serial and parallelized implementations, demonstrating computational speedup.
- Predictions for cryptocurrency price movements and associated risks.
We will showcase the system processing high-frequency cryptocurrency data streams and calculating risk metrics in real-time. Performance improvements achieved through parallelization (GPU) will be compared with non-parallelized implementations to highlight gains in speed and accuracy. Code will be consistently maintained and committed throughout the project.
- The prototype will focus on integrating high-performance parallel processing techniques for real-time risk modeling.
- Future extensions may explore regime-switching models to better capture volatility regime changes in cryptocurrency markets.