Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Stochastic Differential Equations: Numerical Solutions for Financial Risk Modeling is a 3-day hands-on course focused on using Python to simulate and solve SDEs for real financial applications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Stochastic Differential Equations: Numerical Solutions for Financial Risk Modeling is a 3-day hands-on course focused on using Python to simulate and solve SDEs for real financial applications.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Randomness in Continuous Time
• Brownian motion, its properties and non-differentiability
• Ito integral, Ito lemma and why ordinary calculus does not apply
• Drift and diffusion terms and their financial interpretation
Standard Processes
• Geometric Brownian motion and the Black-Scholes assumptions it encodes
• Ornstein-Uhlenbeck and mean reversion for rates and spreads
• Jump diffusion and stochastic volatility such as Heston, and the fat tails they add
Numerical Schemes
• Euler-Maruyama and the Milstein correction, with their convergence orders
• Strong against weak convergence and which one your application needs
• Time step selection, stability and random number generation in Python
Estimation and Efficiency
• Path simulation for pricing and the square-root convergence rate
• Variance reduction: antithetic variates and control variates
• Confidence intervals reported alongside every simulated estimate
Applying It
• Value at Risk and expected shortfall, and the coherence argument between them
• Calibration to market data and the instability of fitted parameters
• Backtesting, model risk and the failures that models systematically miss
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
Frequently Asked Questions
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