COMP0040 Financial Data and Statistics

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Financial Data and Statistics

Code

COMP0040

Module delivery

1819/A7P/T2/COMP0040 Postgraduate

Related deliveries

None

Prior deliveries

COMPG001

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Aste, Tomaso

Contributors

Aste, Tomaso

Livan, Giacomo

Module administrator

Nolan, Martin

Aims

The module is aimed at introducing to financial data analytics. The module is primarily focused on the understanding of financial market dynamics of both individual assets and collective group of assets. Students will learn how to individuate regularities, patterns and laws from a statistical perspective. Instruments to analyse, characterize, validate, parameterize and model complex financial datasets will be introduced. Practical issues on data analysis and statistics of high frequency and low frequency financial data will be covered.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. analyse main statistical features of complex financial datasets;
  2. identify the probability distributions of financial returns;
  3. understand how to characterize, parameterize and validate these distributions;
  4. understand the quantify inter-dependency/causality structure between financial assets;
  5. understand how to use the outcome of data-analytics to develop better tools for forecasting, valuation and risk management;
  6. validate modeling and forecasting tools quantifying performances.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MSc Computational Finance
  • MSc Financial Risk Management
  • MSc Web Science and Big Data Analytics
  • MRes Financial Computing
  • MRes Web Science and Big Data Analytics
  • MSc Data Science

Prerequisites:

In order to be eligible to select this module, students must:

  • be familiar with fundamental probability and statistics concepts; and
  • be familiar with mathematical analysis.

Content

Empirical investigation of financial market-data

Essential practical familiarization with financial data. Typical challenges with real financial data. Basics on data acquisition, manipulation, filtering, graphical representation and plotting.

Statistical properties single financial asset

Statistical distribution of returns. Moments of the distribution. Non-Normal distributions and fat-tails. Large fluctuations and tail risk. Stable distributions. Generalized extreme value distribution. Estimation methods to characterize the tails of the distributions. Calibration and validation. Applications to measures of risk.

Scaling laws

Random walks. Stochastic processes with non-defined variance. Fractal and multi-fractal nature of financial signals. Scaling laws. Persistence, anti-persistence and autocorrelation in financial signals. Hurst exponent, definition and characterization of multiscaling signals.

Statistical properties of groups of financial assets

Marginal probabilities, joint probability, and conditional probability. Measures of dependency: linear and non-linear correlations. Lagged correlations and causality. Information theoretic perspective: mutual information, transfer entrophy. Spurious correlations. Correlation filtering through networks. Calibration, validation and application issues.

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through classroom-based lectures.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

50

 

2

Data Analytics report

15

 

3

Coursework

35

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.