Bridging proofs and policy, this BS plunges students into Dynamic Stochastic General Equilibrium (DSGE) modeling with Dynare, Ito calculus for option pricing, and spectral decomposition of time-series via FFT. Python notebooks harness Prophet for demand forecasts, while a High-Performance Computing (HPC) bootcamp optimizes Monte-Carlo VaR on GPU arrays. Graduates wield martingale theory and machine learning interchangeably.
Heston model calibration using Levenberg–Marquardt algorithm
Spectral density study of crypto returns vs. equities
Parallelized Monte-Carlo risk for climate catastrophe bonds
DSGE model integrating green tech TFP shocks
Sparse VAR forecasting of macro indicators post-pandemic
Optimal stopping problem for ride-hail surge acceptance
Reinforcement-learning trading bot benchmarked against Q-Learner
Copula-based stress test of pension-fund portfolios
Policy brief on Solvency II adjustments for climate risk
Game-theoretic model of vaccine patent-waiver negotiations
Automatic differentiation (AD) for DSGE sensitivity analysis
Causal inference using synthetic controls in energy markets
Quantum Monte-Carlo prototype on qiskit finance
Equilibrium search algorithm for E-commerce price wars
DeepAR demand forecast for grocery perishables
Prove, compute & predict the forces driving global markets.
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