Stochastic Differential Equations: Numerical Solutions for Financial Risk Modeling
Mastering Financial Risk: Hands-on Stochastic Differential Equations for Real-World Applications
About This Course
Stochastic Differential Equations: Numerical Solutions for Financial Risk Modeling is a 3-day hands-on workshop focused on using Python to simulate and solve SDEs for real financial applications. Participants will model asset price dynamics (GBM), implement numerical solvers (Euler–Maruyama, Milstein), and build Monte Carlo workflows to estimate key risk metrics like VaR and CVaR. The workshop also introduces advanced models like stochastic volatility (Heston) and modern directions in quantitative risk modeling.
Aim
To provide participants with a strong conceptual and computational understanding of Stochastic Differential Equations (SDEs) and their numerical implementation for financial risk modeling, enabling them to simulate asset dynamics, apply Monte Carlo techniques, and evaluate portfolio risk using practical Python-based approaches.
Workshop Objectives
- To introduce the fundamentals of stochastic processes and Stochastic Differential Equations (SDEs) used in financial modeling.
- To implement numerical methods such as Euler–Maruyama and Milstein for solving SDEs.
- To apply Monte Carlo simulation techniques for estimating financial risk measures like VaR and CVaR.
- To model and compare asset dynamics under GBM and stochastic volatility frameworks.
- To expose participants to modern trends in quantitative risk modeling, including advanced stochastic approaches.
Workshop Structure
Day 1 — SDE Basics for Finance + Simulation
- Brownian motion, Wiener process, filtration
- Ito integral + Ito’s lemma (finance intuition)
- GBM model, link to Black–Scholes, assumptions vs reality
Hands-on :
- Simulate Brownian paths (multiple trajectories, dt sensitivity)
- Simulate GBM price paths + distribution checks (log-returns, volatility impact)
Day 2 — Numerical Solvers + Risk Metrics
- Discretization, strong vs weak convergence (practical view)
- Euler–Maruyama, Milstein, stability & timestep tradeoffs
Hands-on :
- Implement Euler–Maruyama for GBM; compare with closed-form
- Milstein vs Euler error/variance comparison + runtime
Day 3 — Advanced Models + “Risk Engine” Build
- Stochastic volatility (Heston) + correlated Brownian motions
- CIR rates (optional) + Jump diffusion (overview)
- Model risk: calibration intuition & scenario relevance
Hands-on :
- Simulate Heston (variance paths + price paths)
- Build a mini Monte Carlo risk engine
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
03/05/2026
IST 4 : 30 PM
Workshop Dates
03/05/2026 – 03/07/2026
IST 5 :30 PM
Workshop Outcomes
- Understand and interpret stochastic processes and SDEs in financial contexts.
- Implement numerical schemes (Euler–Maruyama, Milstein) using Python.
- Simulate asset price paths and analyze volatility behavior.
- Build Monte Carlo frameworks to compute VaR and Expected Shortfall (CVaR).
- Compare classical and stochastic volatility models for portfolio risk assessment.
Fee Structure
Student
₹2999 | $70
Ph.D. Scholar / Researcher
₹3999 | $80
Academician / Faculty
₹4999 | $90
Industry Professional
₹6999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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