High-Frequency Financial Econometrics using Matlab

Monday 20 April 2020, 9:00am to Tuesday 21 April 2020, 5:30pm

Venue

Online event

Open to

External Organisations, Postgraduates, Prospective Students, Public, Staff

Registration

Cost to attend - booking required

Registration Info

To register and for further details please visit the Matlab course website

Ticket Price

Academic registration – High Frequency: £600 PhD student registration – High Frequency: £300 Practitioner registration – High Frequency: £950

Event Details

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.

Course Content

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.

Specific Objectives

  • 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.

Day 1:

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)

Day 2:

Estimation of Quadratic Variation and its Components

Stylized facts (normality, persistence and noise)

Intra-day periodicity

Leverage effect

Jump estimation and identification

Forecasting using short and long memory specifications

Monte Carlo Simulations

Contact Details

Name Teresa Aldren
Email

t.aldren@lancaster.ac.uk

Telephone number

+44 1524 510906

Website

http://wp.lancs.ac.uk/matlab-2019/