HighFrequencyCovariance: Estimating Covariance Matrices in Julia

07/29/2021, 7:30 PM7:40 PM UTC
Green

Abstract:

High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialised estimators have been developed, they have had limited availability in open source software. HighFrequencyCovariance is the first Julia package which implements specialised estimators for volatility, correlation and covariance using high frequency financial data.

Description:

This talk will briefly cover the challenges of using high frequency data for covariance matrix estimation. Then a number of algorithms will be discussed. Then we will demonstrate the use of the HighFrequencyCovariance package to estimate covariance matrices.

General content is in this paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3786912 And this package: https://github.com/s-baumann/HighFrequencyCovariance.jl

Platinum sponsors

Julia Computing

Gold sponsors

Relational AI

Silver sponsors

Invenia LabsConningPumas AIQuEra Computing Inc.King Abdullah University of Science and TechnologyDataChef.coJeffrey Sarnoff

Media partners

Packt Publication

Fiscal Sponsor

NumFOCUS