Siarhei Niaborski

Executive Vice President, Head of Risk at CompatibL

Role: Contributor at Autoencoder Market Models Aemm
Location: Boston, MA, US
LinkedIn followers: 500 followers

Information TechnologyView LinkedIn profile

About Siarhei Niaborski

Head of risk software development, architect and technical lead with core responsibility…

Experience

Contributor
Autoencoder Market Models Aemm
Jul 2022 — Present

Education

Belarusian State University of Informatics and Radioelectronics
Master's degree, Computer Science
Belarusian State University of Informatics and Radioelectronics
Doctor of Philosophy (Ph.D.), Computer Science
2007 — 2010
Belarusian State University of Informatics and Radioelectronics
Bachelor's degree, Computer Science

Skills

Sql
Jira
Algorithms
Java
Software Development
Scrum
Microsoft Sql Server
Subversion
Object-Oriented Programming (Oop)
Software Engineering
Xml
Design Patterns
Web Services
Agile Methodologies
Javascript
.net Framework
C#
.net
Oop

Contact information

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Work email
•••••@aemm.org
Personal email
•••••@•••••.com
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Current company

Autoencoder Market Models Aemm

IT Services and IT Consulting · 7 employees · Princeton, New Jersey, United States

This page is for the open-source project on Autoencoder Market Models (AEMM). The code is provided as a Python library using PyTorch. An autoencoder market model combines an autoencoder with a stochastic process in a latent space. First, the autoencoder is pretrained to map historical data for the yield curve (and, optionally, also the volatilities) to a well-regularized, low-dimensional latent space with minimal reconstruction loss. Next, a classical stochastic process or a generative machine learning model is inserted between the encoder and the decoder and calibrated to produce the desired probability distribution in the latent space, which the decoder then converts to future interest rates and volatilities. Encoding interest rates alone produces a deterministic volatility AEMM, while encoding interest rates and their volatilities together produce a stochastic volatility AEMM. Under the Q-measure, the decoder is rewritten as an equivalent stochastic process so that classical drift adjustments can be used to satisfy no-arbitrage constraints; this last step is not required for P-measure models.

aemm.org

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About Siarhei Niaborski

Experience

Contributor

Education

Belarusian State University of Informatics and Radioelectronics

Belarusian State University of Informatics and Radioelectronics

Belarusian State University of Informatics and Radioelectronics

Skills

Find verified contacts for anyone on LinkedIn