Course description
This course develops the theory and quantitative practice of financial decision-making. It covers return measurement, risk and expected utility, portfolio choice, asset pricing, fixed income, derivatives, corporate finance, market efficiency, banking, risk management, behavioral finance, and empirical financial econometrics.
Prerequisites
Calculus, linear algebra, probability, statistics, and introductory economics. Prior exposure to regression analysis is recommended.
Learning outcomes
- Calculate returns and characterize risk under alternative probability distributions.
- Derive mean-variance portfolios and asset-pricing relations.
- Apply no-arbitrage methods to bonds, forwards, and options.
- Evaluate capital budgeting, capital structure, and payout decisions.
- Analyze financial intermediation and systemic risk.
- Estimate and evaluate empirical financial models with reproducible workflows.
15-week schedule
| Week | Topic | Main output |
|---|---|---|
| 1 | Returns, risk, and asset-pricing foundations | Return calculations and SDF interpretation |
| 2 | Expected utility and risk preferences | Certainty-equivalent analysis |
| 3 | Portfolio theory | GMV and efficient-frontier construction |
| 4 | CAPM and multifactor models | Beta and factor regression |
| 5 | Arbitrage pricing and SDF | No-arbitrage pricing derivation |
| 6 | Fixed-income securities | Bond and spot-curve valuation |
| 7 | Duration and term structure | Duration-convexity risk report |
| 8 | Derivatives | Binomial option valuation and hedging |
| 9 | Capital budgeting | NPV/IRR project case |
| 10 | Capital structure and payout | Leverage valuation memo |
| 11 | Market efficiency and event studies | Event-study design |
| 12 | Banking and financial institutions | Bank balance-sheet stress exercise |
| 13 | Financial risk management | VaR/ES backtesting |
| 14 | Behavioral finance | Behavioral mechanism and testable prediction |
| 15 | Empirical finance and econometrics | Reproducible empirical project |
Teaching and learning
Each week combines conceptual instruction, mathematical derivations, numerical exercises, and empirical or computational implementation. Python or R may be used for portfolio analysis, factor regressions, volatility estimation, and risk measurement. Spreadsheet models may be used for fixed income and corporate finance.
Assessment
- Problem sets: 25%
- Portfolio and risk assignment: 20%
- Corporate finance case: 15%
- Empirical finance project: 20%
- Final examination: 20%
Assessment standards
Submissions must state return conventions, compounding, units, estimation windows, model assumptions, and data sources. Quantitative work must include diagnostics and reproducible code or spreadsheet formulas. Empirical conclusions must distinguish statistical evidence from economic interpretation.
Core references
- Bodie, Kane and Marcus, Investments.
- Berk and DeMarzo, Corporate Finance.
- Cochrane, Asset Pricing.
- Hull, Options, Futures, and Other Derivatives.
- Campbell, Lo and MacKinlay, The Econometrics of Financial Markets.
- Tuckman and Serrat, Fixed Income Securities.
- McNeil, Frey and Embrechts, Quantitative Risk Management.