Module -1 (Optional): Quantum Computing & Algorithms

Exercise 1: Quantum operations

Module 0: Deep Learning

Exercise 2: Deep learning in banking

COUNTERPARTY CREDIT RISK

Module 1: Counterparty Credit Risk Requirements in Basel III

Module 2: Counterparty Credit Risk Management

DERIVATIVES IN BANKING

Module 3: Interest Rate Futures and Options

Exercise 3: IRS Valuation in Excel Exercise 4: Pricing caps and floors using Black’s model in Python Exercise 5: Swaption Pricing in Excel Exercise 6: Caplet & Swaption LMM in Python Exercise 7: Bond Options Trinomial Tree in Excel

Module 4: Other Derivatives in Banking

Exercise 8: Pricing Cross-Currency Swap Exercise 9: Equity Option Pricing in Python Exercise 10: Pricing CDS in R

CCR EXPOSURE

Module 5: Internal Model for Counterparty Risk Exposure

Exercise 11: MtM Simulation of IRS Exercise 12: Interest rate simulation with CIR/Vasicek for IRS MtM, PFE & EE Exercise 14: EE & EPE for Swaptions in Excel/VBA Exercise 15: Collateralized and Uncollateralized PE/EPE

DEEP LEARNING & QUANTUM FOR CCR

Module 6: Neural Networks for Pricing Derivatives

Exercise 16: Deep Learning — Black-Scholes model Exercise 17: Deep Learning — Bermuda Option valuation Exercise 18: Deep Learning for Expected Exposure

Module 7: Advanced ML for Volatility & Exotics

Exercise 19: Deep Learning for Volatility

Module 8: Quantum Machine Learning

Exercise 20: Classical ML vs Quantum ML for derivative pricing

Module 9: Tensor Networks in ML

Exercise 21: Derivatives Valuation — NN vs Tensor Network NN

QUANTUM COMPUTATIONAL FINANCE

Module 10: Quantum Computational Finance

Exercise 22: Monte Carlo vs Quantum Algorithms for Derivatives Exercise 23: Basket Options — Classical vs Quantum Deep Learning

CREDIT VALUE ADJUSTMENT (CVA)

Module 11: Structural Models for PD (CVA Context)

Exercise 24: CDS Spread & PD

Module 12: Reduced Form Models for PD

Exercise 25: Construction of PD & hazard rate curves

Module 14: Advanced LGD

Exercise 26: LGD — Linear Approach & Black-Scholes Exercise 27: Implicit LGD via Binomial Trees & Optimization

Module 15: CVA in Basel III

Exercise 28: BA-CVA and SA-CVA Calculation

Module 16: CVA Modelling

Exercise 29: Estimating CVA, EE, PFE Exercise 30: CVA in IRS Portfolios using Monte Carlo

XVA FRAMEWORK

Module 20: XVA Framework 소개 (CVA, DVA, FVA, CollVA, KVA)

Module 21: DVA & LVA

Exercise 35: Bilateral CVA Estimation

Module 22: FVA & Full XVA Framework + Deep Learning

Exercise 36: XVA Calculation in Python Exercise 37: CVA, DVA, FVA, CollVA, HVA, KVA, LVA, XVA in Excel Exercise 38: Deep Learning for XVA Exercise 39: Quantum Computing for XVA

MODEL VALIDATION OF CCR

Module 23: Validation of Counterparty Credit Risk

Exercise 40: Backtesting PFE (AD, KS, CV)

STRESS TESTING OF CCR

Module 24: Stress Testing of CCR

Exercise 41: CVA & EE Stress Testing Exercise 42: PD Stress Testing using VAR/MVAR

Module -1 (Optional): Deep Learning

• Exercise 1: Deep Learning feedforward perceptron neural network

Module 0 (Optional): Quantum Computing and Algorithms

• Exercise 2: Quantum computing operations

Unsupervised Learning

Module 1: Unsupervised Models

• Exercise 3: Principal components for yield curve

• Exercise 4: Segmentation of financial data with K-Means

Supervised Learning

Module 2: Support Vector Machine (SVM)

• Exercise 5: Support Vector Machine in Python – financial data

Module 3: Neural Networks (NN)

• Exercise 6: Neural Networks in Python – financial data

Module 4: Ensemble Learning

• Exercise 7: Random Forest in R and Python (data 1 and 2)

• Exercise 8: Gradient Boosting Trees

Futures and Options

Module 5: Futures and Options

• Exercise 9: Heston Model

• Exercise 11: Call Option Valuation in Python

• Exercise 12: American Option Binomial Valuation in Python

• Exercise 14: Option valuation by Monte Carlo Simulation in R and Python

• Exercise 15: Black–Scholes valuation in R

Module 6: Strategies with Options

(No exercises listed)

Module 7: Treatment of Volatility

• Exercise 16: Local and Implied Volatility in R, volatility surface plots in R

• Exercise 17: GARCH volatility modeling in Python

• Exercise 18: Jump diffusion model simulation in R

• Exercise 19: Neural Networks for implied volatility

Module 8: Option Portfolio Management

• Exercise 20: Estimation of Delta, Gamma, Theta and Vega in Julia and Python

• Exercise 21: Estimation of Greeks using R

Module 9: Derivatives Used in Banking

• Exercise 22: Pricing Cross Currency Swap

• Exercise 23: Equity Option Pricing in Python

Interest Rate Options & Yield Curves

Module 10: Term Structure of Interest Rates (Yield Curve)

• Exercise 24: Construction of Euribor and Eonia term structure curve in Python

• Exercise 25: Construction of the interest rate term structure curve (deposits, FRAs, IRS + PCA)

• Exercise 26: Bank of Spain Nelson–Siegel exercise in R and Excel

• Exercise 27: Term structure using cubic splines and basis splines in Excel

Module 11: Term Interest Rate Structure for SOFR (Yield Curve)

• Exercise 28: Multicurve estimation and optimization with Jacobian matrices in Python

• Exercise 29: Multicurve estimation with Jacobian matrices

• Exercise 30: SOFR curve estimation

Module 12: Calibration of Stochastic Interest Rate Models

• Exercise 31: Calibration of the Hull–White, CIR and Vasicek models

Module 14: Futures, Swaps and Interest Rate Options

• Exercise 32: Pricing of caps and floors with Black’s model in Excel

• Exercise 33: Swaption pricing in Python

• Exercise 34: Caplet and Swaption Libor Market Model in Excel and VBA

Exotic Options

Module 15: Exotic Options – 1st and 2nd Generation

• Exercise 35: Lookback option pricing in Excel with VB

• Exercise 36: Asian Option Pricing in Python

• Exercise 37: Barrier option pricing in Python

Module 16: Option Speed-Up Pricing Techniques

• Exercise 38: European and American option pricing using Monte Carlo simulation in Python

• Exercise 39: Call option valuation with FFT (Carr–Madan, Lewis, Convolution) in Python

• Exercise 40: Option valuation using trinomial tree

• Exercise 41: Adjoint Automatic Differentiation to estimate Greeks in Python

AI for Derivatives Pricing

Module 17: Deep Learning for Derivatives Pricing

• Exercise 42: Deep Learning to assess the Black–Scholes model

• Exercise 43: Deep Learning for Bermuda Swaption pricing

Module 18: Machine Learning for Implied Volatility and Greeks

• Exercise 44: Machine Learning for Implied Volatility

• Exercise 45: Greeks using Machine Learning models

Module 19: Advanced ML for Volatility and Exotic Options

• Exercise 46: Deep Learning Volatility

Quantum AI for Derivatives Pricing

Module 20: Quantum Machine Learning

• Exercise 47: Classical ML vs Quantum ML GANs to value a derivative

Module 21: Tensor Networks for Machine Learning

• Exercise 48: Derivatives pricing model using Neural Networks vs tensorized Neural Networks

Quantum Pricing

Module 22: Accelerated Quantum Monte Carlo Simulation

• Exercise 49: Quantum Monte Carlo Simulation vs Classical Monte Carlo Simulation

Module 23: Pricing of Derivatives using Quantum Algorithms

• Exercise 50: European put and call option pricing using quantum algorithm

• Exercise 51: American option pricing using a quantum algorithm

• Exercise 52: Pricing of derivatives using Monte Carlo vs quantum algorithms

• Exercise 53: Basket Options Pricing using quantum algorithm

• Exercise 54: Bull Spread Pricing using quantum algorithm

• Exercise 55: Asian Barrier Spread pricing using quantum algorithm