Measuring Financial Markets Stability - “A new, systematic approach for the early detection of financial market crises”

Measuring Financial Markets Stability
“A new, systematic approach for the early detection of financial market crises”
Zurich, August 2019

“In order to protect investors, ensure orderly markets and
safeguard financial stability it is necessary to identify and
assess, at an early stage, trends, potential risks and
vulnerabilities stemming from the micro-prudential level,
across borders and across sectors.”
esma - European Securities and Markets Authority (January 2019)
https://www.esma.europa.eu/market-analysis/financial-stability
2

Abstract
• The effects of the last major financial market crisis in 2008 are still present worldwide today.
Notwithstanding the fact that policymakers, regulators and central banks have jointly managed to
stabilize the financial markets after 2008 and thereby avoided a "meltdown" of the entire financial
system, there is still a constant fear of the next upcoming, severe crisis, with possibly uncontrollable
consequences.
• In this discussion paper, we would like to present a new systematic approach that can help - through
reliable early detection of financial market crises - to take countermeasures before a crisis can fully
develop.
© OpenMetrics Solutions LLC, Zurich (2019) 3

Main Hypothesis
• If the financial market as a whole could embrace a common standard of defining market crises and also a
common concept for measuring it, protective measures could be taken earlier and more efficient than in
the past.
• In addition, financial market participants would have a more consistent perspective on market risk levels,
thus reducing uncertainty within their own exposure in a controlled fashion and therefore indirectly
stabilizing the market.
• If we agree that securities markets are the “anchor” of the financial ecosystem, a concept that allows to
reliably measure the stability of this “anchor” could help to develop the right countermeasures during
market crises, align the activities of all players involved and avoid herding-effects.
• We hope this paper can be a starting point for an open discussion about current industry standards for
measuring market risks and their limitations and also for embracing new mathematical concepts to
better understand how financial markets behave.

Introduction
• At the Institute of Theoretical Physics (ITP) of the ETH Zurich, intensive research has been conducted in the last
10 years on how financial markets can be mathematically described. In this context, not only most of the
current standard approaches have been assessed thoroughly, but also many new and innovative approaches
that originated from other academic domains (e.g. biosciences, physics).
• Already in 2011, a new mathematical approach – the Bayesian Changepoint Analysis (BCP) – has been tested
with financial markets data. The results have been very promising, so that the former head of the
Econophysics group Prof. Dr. Diethelm Würtz and his then PhD student Dr. Tobias Setz studied the practical
applications of this technology.
• Since 2016, the BCP has already been considered by some first movers in the financial industry for identifying
market risks and since then the BCP has gained more and more momentum.
• Finally, with the publication of Dr. Tobias Setz’s dissertation in 2018 at ETH Zurich, the BCP is a recognized as an
academic standard for the analysis of market stability.

A New Approach – The “Structural Break Index (SBX)”
• Based on the Bayesian Changepoint Analysis (BCP), OpenMetrics
Solutions LLC has introduced a completely new approach to
measure regime changes in financial markets, the Structural
Break Index (SBX).
• The SBX adds a third dimension to trend and variance and allows
not only to calculate market stability signals but also - and that
makes it valuable for a wider range of use cases - to improve
already implemented financial standard models.
• The SBX has not only a fully documented academic foundation
but also been thoroughly tested in practice for several years in
cooperation with renowned financial markets participants.
• For all details, pls. read our whitepaper: “Introduction of the SBX
Structural Break Index”

SBX – Potential Industry Impact
• Using the SBX as a basis for improving the calculation of risk signals, hedging/exposure management and
other finance applications generates substantial benefits for the majority of financial industry players:
• The potential industry impact for the SBX could be quite significant, for all data models, which depend on
historical financial data.
• By including the new paradigm of measuring structural breaks, the quality of many standard algorithms could
be improved significantly.
• Better algorithms and models across the industry could help to improve the quality of financial decisions and
derived products overall.

SBX – Multiple Use Cases I the Financial Industry
• The SBX can add significant value to current and new risk management models in the financial industry.
• The usage is very simple! The SBX helps to improve the assessment process about upcoming market
moves by re-calibrating existing quantitative models:
1. A low SBX value suggests to use a longer data history
2. A higher SBX implies to rely on more recent data
Structural Break Index
(SBX)
VaR, CVar
Portfolio
Optimization
Hedging
Stability
Signals
And many
more…
Re-calibration

SBX – The Case for Index Providers
• The SBX is a model case for index providers, as the centralized provision of complex indices maximizes
the economies of scale for all market participants. This is especially valid for the SBX - Structural Break
Index.
• The SBX calculation is mathematically complex and computationally rather intensive, especially when
higher calculation frequencies are required. Therefore, offering the calculated index for a range of
products relieves the market participants from implementing the calculation process for themselves and
reduces time-to-market as well as implementation and operating costs.
• Market participants can not only benefit from the economies of scale of a centralized index calculation
but also benefit from a reliable quality through standardization of models, data and processes.

SBX – Significant Revenue Potential
• To estimate the revenue potential is currently not in scope of this paper. However, the number of
potential users could be quite large. Assuming that the SBX would be used by just 20% of the
professional financial industry participants, we could assume already a large revenue base.
• In addition, the SBX business case can scale with the number of instruments covered, the delivery
frequency and additional functionality (e.g. calculation of stability signals as a service).
• The SBX - Structural Break Index from OpenMetrics represents a groundbreaking new paradigm for
measuring risks in financial markets. The current range of developed applications is just the “tip of the
iceberg” and already shows significant potential for adding value to the market and how to benefit from
it.

SBX – Use Case I - Stability Signal
• The basic use case is the calculation of the market stability signals, which can be directly applied to manage
exposure across many markets.
• The Stability Indicator is calculated from the most recent estimation of the SBX (which is based on the BCP
probabilities p), the trend (BCP Mean µ) and the risk (BCP Variance σ)).
• Subsequently, we use a sigmoid function (as used in neural networks) to transform the distance of the indicator into
a BCP Signal between 0 and 1. Thus, calculating the Stability Signal

Sample Use Case: Hedging the STOXX® EUROPE 600 Index
• The application of the stability
signals in this example could be
described as a dynamic hedge (the
green line).
• The black line represents a passive
hedge of 50%, which halves the
risks of the market (blue line) but
also halves the profit.
• As we can observe, the dynamic
hedge protects reliably against
market crises and improves the
risk/return parameters
significantly.

Summary
• The Structural Break Index (SBX) from OpenMetrics Solutions represents a
groundbreaking new paradigm for measuring risks in financial markets.
• The current range of developed applications is just the “tip of the iceberg” and already
shows significant potential for adding value to the market and how to benefit from it.

ABOUT US
• ETH Spin-Off
• Proven Technology & Business Experience
• Large Repository of Tested Algorithms
• Open Source
14© OpenMetrics Solutions LLC, Zurich (2019)

Who We Are
15© OpenMetrics Solutions LLC, Zurich (2019)
ETH Spin-Off
Academic Bridge
Solution Provider
1
2
3
• Founded in 2016 and building upon more than a decade of academic research at
ETH Zurich and affiliated universities
• Engaged in ongoing transfer of academic research into commercially viable
products and services
• Providing financial markets professionals access to the most advanced statistical
methods and financial engineering solutions

Solid Foundation
With more than 13 years of academic research at ETH Zurich, a large set of leading-edge open source
packages for finance and countless number of involved industry experts, OpenMetrics Solutions relies
upon a rich set of experience, advanced algorithms and robust implemented methodologies.
2005
First publication of
“fPortfolio” by ETH
Econophysics group.
A comprehensive
open source package
for portfolio
optimization with R
2007
ETH/Rmetrics Association
founded by
Prof. Dr. Diethelm Würtz
First Rmetrics
Whitepaper
2009
2011
First ETH paper on
stability analysis of
financial markets based
on BCP
BCP for DNA analysis
(H.Xing, W. Liao, Y. Mo & M. Q. Zang)
2012
2014
ETH paper on BCP based
exposure management
for financial markets
Founding of OpenMetrics and
recognition as ETH Spin-Off
Oct
2016
Jan
2018
Production ready quant
libraries for financial
markets

USPs
Latest Technology
Model Transparency
Time-to-Market
Open Source
1
2
3
4
• OpenMetrics Solutions offers a broad portfolio of – partially unique – financial
engineering technologies.
• In contrast to many other players, we fully disclose the mathematical concepts
behind our solutions.
• Our IP is built upon owning the „last mile“ of implementing these concepts.
• Based on our rich technology framework, we are capable of implementing
complex solutions in very short time.
• We work mainly with open source industry standards (e.g. R, Python, C++).

Management Team
Felix Fernandez holds a diploma in Electronics & Information Technology from the University of Applied Sciences in
Frankfurt with a specialization in software simulation environments. He has a long track-record in the exchange industry
with Deutsche Börse AG and joined the Rmetrics Association Zurich as a research fellow in April 2016. In this role he was
engaged in the transfer of academic research results into real-world applications within the financial industry. Since
November 2016 Felix is CEO of OpenMetrics Solutions and responsible for developing the business and implementing a
product framework, which creates sustainable value for our clients.
Tobias Setz holds a master degree in Computational Science and Engineering (with a major specialization in theoretical
physics and a minor specialization in financial engineering) and a doctoral degree in Physics from the Swiss Federal
Institute of Technology in Zurich. He wrote his thesis at the Institute for Theoretical Physics within the Econophysics
Group. He is (co)author of several articles and books and maintainer of the Rmetrics software libraries since 2011. Since
November 2016 Tobias is the main architect of the OpenMetrics Solutions technology framework and engaged in
implementing state-of-the-art solutions for our clients.
Felix Fernandez,
CEO
Dr. Tobias Setz,
CTO

Client Base
• Other references available on request

Publications
“Drawdown-Killer”
Press article from the
Institutional Money
Magazine about
OpenMetrics Solutions
Risk Management
Technology
“Safety on Board”
How to Protect Equity
Portfolios with Risk
Signals Based on Bayesian
Change Point Models
Article in cooperation
with STOXX Ltd
“A Mathematical
Approach to Risk
Hedging That Works”
Article in STOXX Pulse
Online
“Digital Eye”
Article in Private Banking
Magazin about the
application of image
recognition technology
for portfolio optimization
“Bayessche
Stabilitätsanalyse”
Article in Private Banking
Magazin about Bayesian
stability analysis

Disclaimer & Contact
This document is copyrighted, and its content is confidential and may not be reproduced without the express written permission of the authors. This material has been prepared
solely for informational purposes only and it is not intended to be and should not be considered as an offer, or a solicitation of an offer, or an invitation or a personal
recommendation to buy or sell any stocks and bonds, or any other fund, security, or financial instrument, or to participate in any investment strategy, directly or indirectly. It is
intended for use in research only by those recipients to whom it was made directly available by the authors of the document.
All information for an investment strategy prior to its launch date is back-tested, based on the methodology that was in effect on the launch date. Back-tested performance, which
is hypothetical and not actual performance, is subject to inherent limitations because it reflects application of a methodology and selection of constituents in hindsight. No
theoretical approach can take into account all of the factors in the markets in general and the impact of decisions that might have been made during the actual operation of an
investment strategy. Actual returns may differ from, and be lower than, back-tested returns.
OpenMetrics Solutions LLC
Dufoursstrasse 47
CH-8008 Zurich
+41 44 552 4909
contact@openmetrics.ch
www.openmetrics.ch
OpenMetrics Solutions LLC is an approved ETH Zurich Spin-off
OpenMetrics© is a registered trademark at IPI (Swiss Federal Institute of Intellectual Property), Berne
swiss made software is the nationally and
internationally recognized symbol for
Swiss quality in software development.
https://www.swissmadesoftware.org/

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