SMS scnews item created by Anna Aksamit at Tue 19 Apr 2022 1107
Type: Seminar
Modified: Fri 13 May 2022 1127; Mon 16 May 2022 0546
Distribution: World
Expiry: 31 May 2022
Calendar1: 24 May 2022 1400-1500
CalLoc1: zoom talk
Auth: aksamit@27.96.193.9 (aaks9559) in SMS-SAML

# Stochastics and Finance: Sester -- Robust statistical arbitrage strategies and their detection with neural networks

On Tuesday May 24 at 2pm Julian Sester will give a talk via Zoom.

Speaker: Julian Sester (Nanyang Technological University)

Title: Robust statistical arbitrage strategies and their detection with neural networks

Abstract: In this talk, we discuss the notion of robust statistical arbitrage, which
refers to profitable trading strategies that take into account ambiguity about the
underlying time-discrete financial model.  Our investigations rely on the mathematical
characterization of (non-robust) statistical arbitrage, which was originally introduced
by Bondarenko in 2003.  In contrast to pure arbitrage strategies, statistical arbitrage
strategies are not entirely risk-free, but the notion allows to identify strategies
which are profitable on average, given the outcome of a specific sigma-algebra.  In
particular, such strategies may exist even in arbitrage-free markets.  Besides a
characterization of robust statistical arbitrage, we also provide a
super-/sub-replication theorem for the construction of statistical arbitrage strategies
based on path-dependent options.

Relying on these theoretical results, we then discuss an approach, based on deep neural
networks, that allows identifying robust statistical arbitrage strategies in real-world
financial markets.  The presented novel methodology does not suffer from the curse of
dimensionality nor does it depend on the identification of cointegrated pairs of assets
and is therefore applicable even on high-dimensional financial markets or in markets
where classical pairs trading approaches fail.  Moreover, we provide a method to build
an ambiguity set of admissible probability measures that can be derived from observed
market data.  Thus, the approach can be considered as being model-free and entirely
data-driven.  We showcase the applicability of our method by providing empirical
investigations with highly profitable trading performances even in 50 dimensions, during
financial crises, and when the cointegration relationship between asset pairs stops to
persist.

(based on joint works with Eva Lutkebohmert, Ariel Neufeld and Daiying Yin)

https://www.maths.usyd.edu.au/u/SemConf/Stochastics_Finance/seminar.html

Best wishes,

Anna


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