Wednesday 27 May 2020, 9:00am to Thursday 28 May 2020, 5:30pm
Open toExternal Organisations, Postgraduates, Public, Staff
RegistrationCost to attend - booking required
To register and for further details please visit the Matlab course website
Ticket PriceAcademic registration – High Frequency: £600 PhD student registration – High Frequency: £300 Practitioner registration – High Frequency: £950
The purpose of this online course is to provide an update treatment of the core topics in the modeling of high-frequency data.
Advances in computing and data technology make it possible to observe markets at very fine intervals of time. Using high-frequency data permits the calculation of realized measures which are superior to volatility measures generated from GARCH and stochastic volatility models. However, the processing and financial modeling of high-frequency data remains a challenge to both researchers and practitioners. This course aims to provide guidance on the techniques involved in processing, filtering and modeling such data. Using data from TAQ and TICK- DATA databases, the attendees will have an intensive introduction to both the theoretical and empirical aspects of high-frequency data.
The object of the 2-day course is to demonstrate the empirical techniques and methods employed to analyze high-frequency data with special emphasis on the calculation of realized measures, forecasting and Monte Carlo methods and design.
- Familiarize with Matlab syntax, functions and write own functions.
- Computation of realized measures of volatility.Introductions to theoretical foundations and mathematical models of continuous/discontinuous time modeling.
- Forecasting techniques.
- Monte Carlo Simulations: Design and implementation.
Fundamentals of programming in Matlab
Importing and exporting data
Descriptive statistics and Density/log-density estimation
Inter and intra-daily plots
Time stamp, frequency conversion and data aggregation
Data bases comparison Tick vs TAQ
Data Types (Equity, Forex and Indices)
Estimation of Quadratic Variation and its Components
Stylized facts (normality, persistence and noise)
Jump estimation and identification
Forecasting using short and long memory specifications
Monte Carlo Simulations
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