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DerivativesPortfolio & RiskFixed IncomeCorporate FinanceStochastic ModelsGuide
Guide
Derivatives pricing
01Forwards, calls and puts02Combining positions03Arbitrage and the forward price04Put–call parity05The one-step binomial model06Risk-neutral probabilities07Multi-step trees and backward induction08American options and early exercise09Delta and hedging10Geometric Brownian motion11The Black–Scholes formula12The Greeks: gamma, vega, theta and rho13Implied volatility and the volatility smile14Stochastic volatility and the Heston model15Static replication: Breeden–Litzenberger and Carr–Madan16Exotic options: barriers, Asians and digitals
Portfolio & risk
01Returns and volatility02Diversification and correlation03The efficient frontier and the capital market line04Risk aversion and the optimal complete portfolio05CAPM, beta and the security market line06Performance measures: Sharpe, Treynor, Jensen and information ratio07Factor models and systematic risk08Value at Risk and expected shortfall
Fixed income
01Bond prices and yields02Duration and convexity03Immunization and duration matching04Spot rates, forward rates and the yield curve05Bond futures and the cheapest-to-deliver06Interest rate swaps07Callable bonds and negative convexity08Credit risk: default probabilities and credit spreads
Corporate finance
01Interest, compounding and discounting02Annuities, perpetuities and loan amortization03Net present value and internal rate of return04The weighted average cost of capital05Capital structure and the Modigliani–Miller theorems06Discounted cash flow valuation07Relative valuation with multiples08Real options: the value of flexibility
Stochastic models
01Random walks and Brownian motion02Probability spaces, σ-algebras and random variables03Filtrations, adapted processes and stopping times04Conditional expectation and martingales05Properties of Brownian motion06The reflection principle and first passage times07The Brownian bridge08The Itô integral09Itô's lemma10Kolmogorov equations and the Feynman–Kac formula11Change of measure and Girsanov's theorem12Monte Carlo option pricing13Simulating SDEs: the Euler–Maruyama scheme14Mean reversion: Ornstein–Uhlenbeck and Vasicek15Jumps and the Merton jump-diffusion model
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Guide

Lessons meant to be read in order. Each one only uses what came before it.

Portfolio & risk

Risk and return, diversification, the efficient frontier and CAPM.

Start
0 of 8 read
  1. 01Returns and volatilitySimple and log returns, standard deviation, annualising and the square-root-of-time rule.
  2. 02Diversification and correlationTwo-asset portfolios, the role of covariance, and why correlation drives risk down.
  3. 03The efficient frontier and the capital market lineMean–variance optimisation, the minimum-variance portfolio, a risk-free asset and the tangency portfolio.
  4. 04Risk aversion and the optimal complete portfolioMean–variance utility, indifference curves, the risk-aversion coefficient and the best point on the capital allocation line.
  5. 05CAPM, beta and the security market lineSystematic versus idiosyncratic risk, beta as market sensitivity, expected return and alpha.
  6. 06Performance measures: Sharpe, Treynor, Jensen and information ratioRisk-adjusted performance, which risk each measure charges for, and why the rankings can disagree.
  7. 07Factor models and systematic riskThe single-index model, splitting systematic from specific variance, and multi-factor models.
  8. 08Value at Risk and expected shortfallLoss quantiles, the tail beyond them, normal versus fat-tailed returns, and the square-root-of-time rule.

Check yourself

Questions from the lessons of this series. Open a lesson's group whenever you want to test yourself.

Risk aversion and the optimal complete portfolio2 questionsRe-read the lesson →
Check yourself

A portfolio has μ=9%\mu = 9\%μ=9% and σ=15%\sigma = 15\%σ=15%. What is its certainty equivalent for an investor with A=4A = 4A=4? Answer in percent.

Check yourself

An investor's optimal weight in the risky portfolio is y∗=0.6y^{*} = 0.6y∗=0.6. Their risk aversion doubles while the market stays the same. What is the new optimal weight?

Performance measures: Sharpe, Treynor, Jensen and information ratio2 questionsRe-read the lesson →
Check yourself

With rf=3%r_f = 3\%rf​=3% and μM=10%\mu_M = 10\%μM​=10%, a fund with β=1.4\beta = 1.4β=1.4 earned μ=13%\mu = 13\%μ=13%. What is its Jensen's alpha? Answer in percent.

Check yourself

A fund will be a small slice of a large, well-diversified pension portfolio. Which measure should decide whether to add it?

Factor models and systematic risk2 questionsRe-read the lesson →
Check yourself

In the single-index model stock A has βA=1.2\beta_A = 1.2βA​=1.2, stock B has βB=0.8\beta_B = 0.8βB​=0.8, and the index volatility is σM=20%\sigma_M = 20\%σM​=20%. What is Cov⁡(RA,RB)\operatorname{Cov}(R_A, R_B)Cov(RA​,RB​), as a decimal?

Check yourself

An equally weighted portfolio holds more and more stocks, all with beta β\betaβ and specific volatility σε\sigma_\varepsilonσε​. What does its volatility tend to?

Value at Risk and expected shortfall2 questionsRe-read the lesson →
Check yourself

A position of 500,000 has daily volatility σ=2%\sigma = 2\%σ=2% and μ=0\mu = 0μ=0. What is its one-day 95% VaR in the normal model? Use z0.95≈1.645z_{0.95} \approx 1.645z0.95​≈1.645.

Check yourself

Which risk measure can make a diversified portfolio look riskier than the sum of its parts?

About this tool

Narrative guides that connect the interactive tools into a story: financial instruments and their payoffs, the no-arbitrage principle, risk-neutral pricing, delta hedging and the road to the Black–Scholes formula. Each guide links to the labs where you can try the ideas yourself.

Frequently asked questions

In what order should I read the guides?

Start with instruments and no-arbitrage, then risk-neutral pricing and delta hedging; together they lead to the Black–Scholes formula.

Who are the guides for?

Students of financial and insurance mathematics and anyone who wants intuition behind derivatives pricing, not just the formulas.