Financial Modeling - Derivatives Concepts and Applications

Explore the core ideas of financial derivatives, including forwards, futures, options, and swaps, and learn how these instruments enable risk management, hedging, and synthetic payoff structures.

Explore derivatives concepts and market mechanics, including forward loans, zero-coupon bonds, equity and currency swaps, and the liquidity roles of market makers, brokers, and risk management in best execution.

Explore convergence trades that bet on narrowing rate differences between instruments, and learn how yield curves, discount conventions, forward loans, and forward rates reveal arbitrage opportunities.

Illustrate a synthetic frb built from a forward loan and its paid-in-arrears cash flows. Explain the two interest rate payments interpretation and the fair market value at time zero.

Explore euro dollar futures, their cash settlement, and how forward rates determine the discount rate, with notes on TED spreads and bid-ask adjustments.

Learn how to engineer interest rate swaps, price them with forward rates and swap spreads, and decompose swaps into horizontal and vertical structures to build synthetic bonds.

Price plain vanilla interest rate swaps using bond prices, notional, and semiannual payments. Show how pay fixed and receive floating alter duration and how swaps modify portfolio duration.

Explore how repos and reverse repos enable collateralized financing of bonds, including special and cheapest to deliver bonds, and how forward purchases and butterflies shape yield strategies.

Explore currency forwards, forex markets, and currency swaps. Learn how forward and spot rates create synthetic bonds and enable cross-currency financing.

Analyze derivatives concepts and alternative investments, including oil futures, hedge funds, and illiquid assets, and apply cost of carry, convenience yield, backwardation, and contango to pricing.

Explore how options act as volatility instruments via delta hedging and gamma scalping. Understand how Greeks—delta, gamma, theta, vega—drive time value, hedging costs, and market maker outcomes.

Explore Monte Carlo option pricing by simulating stock paths with volatility and interest, computing call and put values and their present value, including VBA and data table techniques.

Explore interactive bond pricing across bond, money market, and commercial paper markets using data tables, dropdowns, and what-if analysis; validate inputs, apply market-specific discount formulas, and visualize results with charts.

This lecture demonstrates pricing bonds, FRA, and swaps using VBA in Excel, including creating modules, subs and functions, using input boxes, ranges, and buttons to automate FRA and swap calculations.

Learn practical methods to price bonds and FRAs using swap formulas, zero-bond pricing with delta, and VBA-driven implementations in Excel.

Master random number generation in Excel and VBA, including rand and randomise. Apply normal and box-muller methods, correlation, and variance-covariance concepts to simulate stock returns and Monte Carlo scenarios.

Explore Monte Carlo basics with circle area estimation using random points, and apply it to retirement planning by simulating wealth, drawdowns, and risk with data tables and stochastic paths.

Use Monte Carlo simulations to price options by modeling asset dynamics with a stochastic process, building a replicating portfolio, and linking to Black-Scholes and the binomial model.

Explore Monte Carlo simulations to generate stock price paths and price options using a stochastic differential equation, with VBA automation in Excel.

Create strategy-specific profit charts in Excel using risk reversal, short straddle, and butterfly setups; compute profits with Black-Scholes, data tables, and dropdown displays.

Explore Monte Carlo in depth to price options under risk-neutral models, using random numbers, Brownian motion and Black-Scholes concepts, with variance reduction and convergence insights.

Explore delta hedging and stop-loss strategies for pricing options, compare Euler and Milstein methods for simulating geometric Brownian motion, and assess risk-neutral replication via Monte Carlo.