Examples of recent courses

Empirical Finance with Equity Data

Lecturer:   Prof Johannes Ruf

Time, dates and location: Jan-Mar 2023, Tuesdays, 17:00 - 20:00, OLD.4.10

Prerequisites: (a) Basic knowledge of Python (you should have installed it on your laptop, be able to run simple programs, and know about Jupyter notebooks). (b) Access via your institution to the CRSP dataset in the Wharton Research database (e.g., @Bayes, LSE, Imperial). (c) Understanding of basic concepts in finance (trading strategies, stocks, dividends, ...) (d) Interest in working with real data and motivation to participate actively in the course.

Course summary: In this module we run several empirical experiments with (US) equity data. First, we introduce the Pandas library of Python and the CRSP dataset of the Wharton Research Database. We then discuss the cleaning of this dataset. Next we study several characteristics of the equity market (e.g., capital distribution curves, size factor, etc.) and implement and backtest various trading strategies. In this context, we also discuss possible pitfalls of such empirical analyses (e.g., survivorship bias; data leakage; handling of stocks that default). Time permitting, we shall look at other datasets (e.g., OptionMetrics, Global Financial Data, ...). This course is very hands-on and requires active participation. Upon completion of the course, the participants will have acquired the skills needed to augment their own research with sound and high-quality data analyses.

List of past LGS courses

MF0 Stochastic integrals: an introduction to the Itō calculus 

MF1 Information and finance: filtration modelling, stochastics filtering and asset pricing

MF2 Computational finance

MF3 Time Changes in Asset Price and Volatility Modelling

MF4 Portfolio optimisation (Albina Danilova) 

MF5 Interest rates

MF6 Counterparty risk, collateral and funing across asset classes with arbitrage-free dynamical models

MF7 Risk and Insurance

MF8 SDE, optimal stopping and quickest detection problems with applications to finance and control (guest lectures)

MF9 Introduction to Markov processes and their applications

MF10 High frequency statistics for financial data

MF11 Functional Itō calculus and Path-dependent Kolmogorov Equations

MF12 Convex optimisation and illiquid markets

MF13 Lévy processes and applications in finance

MF14 Quadratic hedging and its applications

MF15 Introduction to Malliavin calculus

MF16 Advanced topics of mathematical finance: Monetary utility functions and risk measures (guest lectures)

MF17 Nonlinear valuation under credit gap risk, initial and variation margins and funding costs

MF18 Forward-Backward SDEs and applications

MF19 Backward stochastic differential equations: theory and applications in mathematical finance (guest lectures)

MF20 Quantitative modelling for operational risk and insurance analysis

MF21 A unified approach to quadrature pricing in equity derivatives models: theory and practice

MF22 Empirical market microstructure

MF23 Advanced Probability Theory

MF24 Markov Processes: Characterization and Convergence

MF26 Local Martingales and the Martingale Properties

MF27 Empirical Finance with Equity Data