This module focuses on quantitative methods in finance and economics and their application to investment, risk management and trading. The module will introduce students to state-of-the-art statistical modelling of financial markets and will give an overview of the quantitative framework that is necessary to advance to other CCFEA modules.
The first part of this module covers a review of statistical concepts, allowing students to analyse stylised facts such as fat tails, skewness, volatility clustering or long memory. An introduction to financial econometrics will follow, where emphasis will be given to the analysis of financial time series models such as moving average, ARIMA and GARCH. Applying these methods to empirical financial problems, students will investigate topics like value-at-risk, portfolio optimisation, index tracking, pairs trading and statistical arbitrage. The module will also give an overview of the most popular computational methods in quantitative finance, in particular Bootstrapping and Monte Carlo Simulation.
In the computer lab sessions, students will be engaged in MATLAB exercises and financial case studies that will illustrate the practical implementation of the models introduced in the lectures.
On successful completion of the module students are expected
(1) to have a solid knowledge of financial econometric methods,
(2) to be able to model stylized facts of financial asset returns, and
(3) to grasp the intuition behind the arsenal of quantitative techniques that attempt to capture them.
The first part of this module covers a review of statistical concepts, allowing students to analyse stylised facts such as fat tails, skewness, volatility clustering or long memory. An introduction to financial econometrics will follow, where emphasis will be given to the analysis of financial time series models such as moving average, ARIMA and GARCH. Applying these methods to empirical financial problems, students will investigate topics like value-at-risk, portfolio optimisation, index tracking, pairs trading and statistical arbitrage. The module will also give an overview of the most popular computational methods in quantitative finance, in particular Bootstrapping and Monte Carlo Simulation.
In the computer lab sessions, students will be engaged in MATLAB exercises and financial case studies that will illustrate the practical implementation of the models introduced in the lectures.
On successful completion of the module students are expected
(1) to have a solid knowledge of financial econometric methods,
(2) to be able to model stylized facts of financial asset returns, and
(3) to grasp the intuition behind the arsenal of quantitative techniques that attempt to capture them.
- Module Supervisor: Michael Kampouridis