Workflow:Avhz RustQuant Stochastic Process Simulation
| Knowledge Sources | |
|---|---|
| Domains | Quantitative_Finance, Stochastic_Processes, Simulation |
| Last Updated | 2026-02-07 19:00 GMT |
Overview
End-to-end process for instantiating, simulating, and visualizing stochastic processes used in quantitative finance modeling.
Description
This workflow covers the generation of sample paths from a wide range of stochastic processes commonly used in financial modeling. RustQuant implements 18+ stochastic processes through a unified StochasticProcess trait, supporting Euler-Maruyama, Milstein, and Strang Splitting discretization schemes. Processes include standard models (Brownian Motion, Geometric Brownian Motion, Ornstein-Uhlenbeck), short-rate models (CIR, Hull-White, Ho-Lee, Vasicek, BDT), jump-diffusion models (Merton), fractional processes (fBM, fCIR, fOU), and multi-factor models (Heston, SABR, CEV).
Usage
Execute this workflow when you need to generate simulated price paths, interest rate trajectories, or volatility paths for use in pricing, risk management, scenario analysis, or model validation. This is the foundational building block for any Monte Carlo-based analysis.
Execution Steps
Step 1: Select a Stochastic Process
Choose the appropriate stochastic process based on the asset class and modeling requirements. Each process is instantiated with its specific parameters (drift, volatility, mean reversion speed, long-run mean, etc.).
Available process families:
- Brownian motion family - BM, ABM, GBM, Geometric Brownian Bridge
- Mean-reverting processes - Ornstein-Uhlenbeck, CIR, Extended Vasicek
- Short-rate models - Hull-White, Ho-Lee, Black-Derman-Toy
- Jump-diffusion - Merton Jump Diffusion
- Stochastic volatility - Heston, SABR, CEV
- Fractional processes - fBM, fCIR, fOU (long-memory dynamics)
Step 2: Configure Simulation Parameters
Set up the simulation configuration: initial value, start and end times, number of discrete time steps, discretization scheme, number of independent simulation paths, and whether to use parallel execution.
Configuration parameters:
- Initial value - Starting point of the process (e.g., spot price, initial rate)
- Time range - Start time (typically 0) and end time (e.g., 1.0 for one year)
- Number of steps - Discretization granularity (e.g., 252 for daily trading days)
- Scheme - Euler-Maruyama (first-order), Milstein (higher-order for processes with state-dependent diffusion)
- Number of simulations - How many independent paths to generate
- Parallel flag - Enable multi-threaded path generation
Step 3: Generate Sample Paths
Execute the path generation by calling the generate method on the process with the configured parameters. The simulation engine produces a Trajectories object containing the time grid and all generated paths.
What happens:
- Random increments are drawn from the appropriate distribution
- The discretization scheme advances the process step by step
- Each path is an independent realization of the process
- The output contains both the time vector and the matrix of path values
Step 4: Visualize or Consume Paths
Use the generated trajectories for downstream analysis. Paths can be plotted for visual inspection, fed into Monte Carlo pricing engines, used for Value-at-Risk estimation, or analyzed statistically to verify process properties.
Common uses:
- Plot paths using the Plotly integration for interactive visualization
- Feed paths into option pricing routines for Monte Carlo valuation
- Compute path statistics (mean, variance, autocorrelation) to validate the process
- Use paths for scenario analysis and stress testing