Lectures related to Mathematical finance

Explores forward measures, option pricing, and bond option pricing in interest rate models.

Covers Stochastic Differential Equations, Wiener increment, Ito's lemma, and white noise integration in financial modeling.

Covers Value at Risk (VaR) and Expected Shortfall (ES) in risk management, including backtesting and multivariate distributions.

Explores trading strategies in mathematical finance, backtesting scientific models, and the challenges of non-stationary markets.

Explores the application of machine learning in finance, showcasing challenges and opportunities in the field.

Covers interest rate futures, marking to market, convexity adjustment, and Vasiček model.

Explores asset pricing theorems and the Black-Scholes formula derivation in discrete time economies.

Covers the first theorem of asset pricing, self-financing portfolios, replication, Kolmogorov equations, and pricing strategies.

Explores interest rate derivatives, specifically caps and floors, cap-floor parity, pricing formulas, and implied volatilities.

Explores the use of machine learning in algorithmic trading to optimize price signals and minimize slippage in futures markets.

Covers the calibration of interest rate models using a two-factor Gaussian HJM model and the computation of Black and Bachelier cap vegas.

Explores machine learning techniques for nonlinear regression and forecasting trends in complex data sets.

Covers mean-variance efficiency, market completeness, and optimal portfolio weights in asset pricing and portfolio optimization.

Explores martingale-based linear response, complex diffusivity, and Nyquist relation in stochastic systems with time-dependent perturbation.

Provides an overview of stochastic calculus and its applications in interest rate models and financial modeling.

Covers the fundamental theorems of asset pricing, including EMM, self-financing strategies, risk-neutral pricing, and completeness of markets.

Delves into how NLP is transforming finance through market signals and expertise.

Covers the Black-Scholes-Merton model, stock dynamics, option pricing, and replicating strategies.

Covers the Black-Scholes-Merton model, dynamics, self-financing strategies, and the PDE.

Explores the Black-Scholes-Merton model, the Greeks, dynamic hedging, and engineering exposure in derivative pricing.