Core MSc Lecture Courses
Applied Probability and Stochastics (CMFM01) S1 Compulsory
Probability spaces, random variables, distributions, independence,
product spaces. Expectation and conditional expectation.
Moments, generating functions, characteristic functions.
Random processes, filtrations and stopping times.
Martingales, Brownian motion and the Poisson process.
Elements of Itô integration.
Risk Neutral Valuation: Pricing and Hedging Derivatives (CMFM02) S1 Compulsory
Forward prices, discounting, arbitragefree pricing; binomial trees,
derivatives pricing in discrete time by use of binomial lattices,
geometric Brownian motion, volatility and drift, martingales and
conditional expectation, Itô calculus, hedging portfolios, replication,
BlackScholes model, putcall parity. Option deltas, gammas, vegas, and
other sensitivities. Risk premium, riskneutral measure. Arbitragefree
multiasset financial markets.
Financial Markets (CMFM03) S1 (afternoons)
This course presents an overview of the world's financial markets,
and a concise mathematical formulation of the main characteristics
of financial instruments and trading practice, with an emphasis on
quantitative aspects of options, futures, and other derivatives.
Topics covered include: Spot markets for stocks, bonds, currencies,
commodities. Forward markets. Commodity futures, financial futures.
Stock options, index options, currency options, commodity options,
interest rate options, options on futures. Interest rate swaps,
currency swaps. Corporate bonds, treasury bonds, inflationlinked
products. Energy and credit markets. Structured products, OTC
derivatives.
Stochastic Analysis (CMFM04) S2
Itô's isometry and the Itô integral. Levy's characterisation
theorem and Girsanov's theorem. Random time changes, martingale
representation theorems. Stochastic differential equations and
diffusions. OrnsteinUhlenbeck process, Brownian bridge, Bessel
processes. FokkerPlanck equation and FeynmanKac formula. Cauchy and
Dirichlet problems.
Distribution Theory (CMFM05) S1
Theory of families of discrete and continuous probability distributions.
Binomial, Poisson, geometric, negative binomial, hypergeometric
distributions. Uniform, exponential, normal, gamma, beta, Student and
Cauchy distributions. Compound distributions. Applications of order
statistics. Scale and displacement families. Exponential families.
Elements of information geometry. Properties of fattailed distributions.
Stable families. Applications to mathematical finance.
Numerical and Computational Methods in Finance (CMFM06) S1, material needed for FM12.
Direct solutions of partial differential equations arising in
financial applications by finite difference methods. Binomial and
trinomial tree methods. Monte Carlo simulation techniques in
finance. Times series analysis and discrete time price volatility
models. Practical implementation of numerical and computational
methods in finance by use of spreadsheets.
Interest Rate and Foreign Exchange Dynamics (CMFM07) S2 compulsory
Discount bonds, interest rates, yield curves. Basic state variable
models: Vasicek, HullWhite, CoxIngersollRoss, lognormal short rate,
rational lognormal model and others.
HeathJarrowMorton framework; introduction to market models.
Pricing formulae for caps, floors, swaptions. Multicurrency interest
rate models.
Exotic Derivatives (CMFM08) S2
This course surveys pricing and risk management techniques for a number
of important financial derivatives. Topics covered include: barrier, quanto,
forwardstarting, Asian, lookback, power and compound options; options to
exchange one asset for another, dualasset knockout options and basket
options. Energy and weather derivatives; insurance linked products.
Portfolio Risk Management (CMFM09) S2
Theory of risk aversion and utility, with applications. Trading
strategies, consumption and portfolio value processes, expected utility
from consumption and terminal wealth. Portfolio optimisation, solution of
Merton problem. Transaction costs, market incompleteness, portfolio
constraints.
Credit Risk Management (CMFM10) S2
Default events and survival indicator processes. Price processes
for assets with jump risk. Overview of credit derivative structures.
Lando's formula for risky discount bonds. Applications to credit
default swaps, corporate and sovereign coupon bonds, and structured
loan facilities. DuffieSingleton formula for partial recovery on
default. Introduction to structural models for default. Basic
elements of copula theory and extremal events with applications
to topics in credit risk.
Applied Computational Finance (CMFM12) S2
Computation of relevant special functions. Computation of standard and simple
exotic BlackScholes type results by the use of a formula.
Implementation of NewtonRaphson iteration and other equationsolving methods.
Implementation of binomial and trinomial trees. Numerical solution
of SDEs and their implementation. Implementation of explicit and implicit
finitedifference schemes. Course taught in two programming languages with assessed coursework.
Elective MSc Lecture Courses
Basic analysis (CMMS05)
A selfcontained excursion through some fundamentals of functional analysis.
Topics to be discussed: Banach spaces, open mapping and closed graph theorems,
HahnBanach theorem, dual spaces, BanachAlaoglu theorem.
Operator theory (CMMS08)
Hilbert and Banach spaces. Riesz representation theorem. The adjoint.
Orthogonal projections. Spectral theory of bounded linear operators.
Spectral theorem for bounded linear operators. Spectral theorem for
compact operators. Applications to differential and integral equations.
Spectral theory of Markov chains (CMMS09)
The course will study the spectral properties of operators
associated with Markov Chains and random walks on graphs.
Topics to be discussed: Markov transition matrices, invariant states,
ergodicity, continuous time Markov chains, the infinitesimal generator, etc.
Notes:
Subject to approval by the Programme Director, any course
from the list of courses offered for the
MSc in
Mathematics can be taken as an elective MSc lecture course,
for example:
 Advanced neural nets (CMNN15)
 Information theory in neural networks (CMNN14)
 Statistical mechanics of neural nets (CMNN13)
 Measure theory (CMMS13)
 Partial differential equations (CMMS12)
 Statistical dynamics (CMMS35)
Not all MSc courses that are described here will necessarily
run in a given year.
Most, though not all, of the core financial mathematics lecture
courses are likely to take place in the early
evening. Some courses will run at other times.

