COMPG001 - Financial Data and Statistics
This database contains the 2016-17 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.
Syllabuses from the 2015-16 session are available here.
|Prerequisites||Students must be familiar with basic probability and statistics concepts. They must also have some familiarity with mathematical analysis.|
|Taught By||Tomaso Aste (100%)|
|Aims||The course is aimed at introducing to financial data analytics. The course is primarily focused on the observation of financial market dynamics of both individual assets and collective group of assets and the individuation of 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.|
Students will become able to analyse main statistical features of complex financial datasets. On successful completion of the course, a student should have a good understanding on: 1) the empirical probability distributions of financial returns; 2) how to characterize, parameterize and validate these distributions; 3) the quantification inter-dependency/causality structure between financial assets; 4) how to use the outcome of data-analytics to develop better tools for forecasting, valuation and risk management.
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.
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.
Method of Instruction
3 hours of lectures per week.