Computational work
Python practicals
The notebooks make standard financial calculations reproducible. They are deliberately small enough to inspect line by line: the aim is to understand the valuation or empirical result, not to hide it inside a large software system.
Working with the files
Week One · Valuation and capital markets
Value irregular cash flows, compare rate conventions, calculate real returns, construct discount factors and recover forward rates from a frozen teaching curve.
Data: project cash flows, Bank of England OIS teaching curve, and UK rates/inflation extract.
Price two 2032 gilts from cash flows and the spot curve, solve for yield, measure duration and convexity, and compare exact with approximate repricing.
Data: two gilt cash-flow schedules, Bank of England nominal spot curve, and a teaching stress matrix.
Reconcile payout evidence and valuation claims, reproduce three FCFF scenarios, stress WACC and terminal growth, and compare DCF values with matched-peer multiples.
Data: Apple FY2025 payout extract, S&P 500 payout context, Aster valuation inputs, scenarios, peers and sensitivity grid.
Compare arithmetic with compound returns, calculate covariance-based portfolio risk, estimate Aster's beta, and translate estimation uncertainty into a cost-of-equity range.
Data: Kenneth French factor and industry extracts, synthetic Aster returns and cost-of-equity sensitivity.
Week Two · Risk transfer, information and digital finance
Calculate contract payoffs, compare hedged with unhedged outcomes, size a hedge and trace how daily futures settlement changes the margin account.
Data: hedge-state table, futures margin ledger and calculation metadata.
Build long and short call/put payoffs, separate payoff from profit, check parity and compare a protective put, covered call and simple combinations state by state.
Data: common terminal-price states and verified option/strategy outcomes.
Translate futures prices into rates, measure announcement-window changes, compare responses across the curve and construct an auditable hawkish/dovish text score.
Data: selected USMPD event windows, 2026 FOMC statements and dictionary-level text contributions.
Measure Bitcoin returns and drawdowns, inspect the March 2023 USDC de-peg, stress redemption resources, and compare gross with net settlement using UK payment data.
Data: Coin Metrics teaching extracts, stablecoin stress cases, UK policy status, Pay.UK system statistics and Bank of England CHAPS distribution statistics.
Data provenance
The bundles use frozen extracts so every student obtains the same result. The principal public sources are the Bank of England yield curves, the Kenneth French Data Library, the U.S. Monetary Policy Event-Study Database, Coin Metrics Community API, and Bank of England payment and settlement statistics. Synthetic company cases are labelled as such inside the files.
