University of Zurich Content Agent-based models of financial markets Commercialization of models

University of Zurich
Agent-based models of financial markets
Commercialization of models
November 17, 2014
Content
• Q&A case study
• Starting Point
• Research
• Consulting
• Risk Management
• Asset Management
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Presentation December 1
Title
Beyond valuation
CHFUSD
CHFYEN
EURCHF
EURCHF
EURCHF (SPI)
Gold
Gold
Corporate Bond Market
Dax
Dax
USDCNY
USDCNY, USDJPY
VIX Future
Comparsion of Stock Market models
Herd Behavior in High-Volatility Markets (US Tech Markets)
Interest rate curve USD
Private Equity/LBO Performances
WIG20 Index
Student
Silvan Fischer
Mischa Haberthür
Patrik Wittenwiller
Daniel Auer
Matteo Pianta
Vanessa Kummer
Stefan Betschart
Florian Reeh
Dzemo Fazli
Laura Oberbörsch
Jan Kaiser
Basil Odermatt
Luca Tonizzo
Nicola Lei Ravallo
Agata Gareishina
Jonas Elmer
Christian Scheitlin
Vincent Rime
Ewelina Laskowska
Presentation
13.00
Valerio Frison
Raphael Dosch
Marco Rudin
14.00
Cesare Scherrer
Dario Messi
15.00
Qian Cao
Lukas Hauri
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Presentation December 8
Title
Bitcoin
Bitcoin
Bitcoin
Bitcoin
Nikkei225
Gold
DJ Transportation Average Index
Hong Kong Real Estate Market
Hotel accomodation Switzerland
Oil
S&P 500
S&P 500
S&P 500 (QE)
S&P BSE Health Care index
US Stocks
Wheat Futures
Wheat Futures
Student
Michael Petersen
Karim Attia
Tobias Bertschinger
Romano Gruber
Kristijan Milosavljevic Daniel Fuchs
Carlo Coppetti
José Parra Moyano
Jan-Thore Hünecke
Ankit Doshi
Simone Huber
Chuanfang Di
Ziyao V. Zhou
Moritz Fischer
Ivan Mihailovic
Fabio Isler
Alexander Thomas
Roger Böhler
Flavio Schönholzer
Yacine Brahmi
Jan Iten
Maria Grigorkina
Fabienne Noll
Jovan Vifian
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Presentation
13.00
14.00
15.00
Presentation December 15
Title
BRLUSD
Corporate Bond Market
Dow Jones Equity REIT Total Return Index
Oil
Oil
SMI
SMI
SMI/SPI
SPI/SMI
Subprime market
Swiss Real Estate Market
US residential real estate market
Chinese stock market
Demographics and Financial Markets
Dow Jones Luxury Index (DJLUX)
High Frequency Stock Market
Silver
Student
Wei Qiu
Samuel Annen
Xiaoxu Yu
Min Feng
Thomas Füglister
David Beck
Ulrich Schmid
Christoph Hartmeier
Alain Flury
Jonathan Krakow
Manuel Pilla
Daniela Rigert
Vuk Stocanic
Velimir Gordic
Hanna Zechner
Matthias Hafner
Hao Xing
Luzius Meisser
Sandra Natour
Sabrina Realini
Jean-Paul van Brakel
Wiliam Isenring
Guillaume Ruch
Presentation
13.00
14.00
15.00
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Case Study
• The case study consists of an agent-based model. It is realized in MS Excel with a short
documentation. The case study should
• motivate and formulate a research question
• present a model of any subset of a financial market and its participants (agents)
• outline and discuss the results
• Deadline for the model, documentation and presentation slides is Friday before the
presentation date ([email protected]).
• The documentation is expected 3 to 5 pages
• The presentation is expected 3 to 5 slides; you have 5 minutes to present
• The grade depends on
•
•
•
•
•
Originality
Economic and academic foundation
Quality of documentation
Quality of presentation
Results
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Starting Point
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What is the idea of our approach?
• Gap the bridge between academic often theoretical, highly complex
models and the relatively «simple» models of the industry
• Model financial markets as multivariate, non linear, deterministic
systems with stochastic shocks and a drift component (Neither
normally distributed nor stationary)
• Determine distributed, decentralized, interacting assemblies of
agents that can lead to an ordered, coherent collective behavior
without control or authority Virtual worlds for controlled
experiments (scenario analyses, stress testing, impact studies, etc.)
and forecasting
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Research
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Consulting
• A modeling project includes the following iterative steps:
•
•
•
•
Defining the market area of the customer
Drafting and presenting a base model
Defining the agents involved and the scenario analysis through the client
Implementation and presentation of the results
• The results are confidential
• Clients are the Financial and IT industry
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Risk Management
Only when the tide goes out do you discover who's been swimming
naked. (Warren Buffett)
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Asset Management
Assets
• Equity
• Debt
• Commodity
• Private/Public
• ….
Investors
• Family Offices
• Pension Funds
• Retail
• ….
Profit, Risk, Correlation, Duration, Liquidity, Size, Cost,
Transparency,…
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From a model to the product
• Depending on the client’s needs and market chance
• Trading strategy
• Risk management
• Legal structure
•
•
•
•
Managed account
Fund
Structured Product (AMC)
Social Trading
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Trading Strategy
• Bridge the gap between agent-based model and tradable/investible
products
• Trading frequency
• Transaction cost
• Slippage
• Tracking error
• Leverage
• Systematic – discretionary
• Active - passive
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Risk Management
• Market Risk
• Limits
• Alpha and beta
• Stop Loss Rules – Reduction of the drawdown
• Liquidity Risk
• Depending on the strategy
• Depending on the legal setup (access to financing, reputation, ...)
• Depending on the market conditions (uncertainty about future cash flows, …)
• Credit Risk
• Depending on the legal structure
• Diversification
• Operational Risk
• Limits
• Systemic risk
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Managed Accounts
• Managed accounts provide benefits such as enhanced transparency,
liquidity and control
•
•
•
•
Legal and operational control of assets
Transparent on contract level and ability to liquidate assets as desired
Independent valuation of assets
Individual risk management and cost management
• High minimum investment size and significant scale required
• Operationally complex
• High fix costs to run a platform
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Investment Fund
• Collective investment vehicles – Legal entity
• Mutual fund, Exchange Traded Fund, Hedge Fund, Private Equity
Fund
• Professional management, low(er) transaction costs, diversification
• Separation of duties – investment manager, fund administrator
• Different funds domiciles
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Structured Product
• A market linked investment strategy
• Issuer risk
• High flexibility
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Social Trading
• Combination of social media and managed account platforms
• Mirroring/ copying trades of other users
• Transparency, liquidity and control
• Legal and operational control of assets
• Transparent on contract level and ability to liquidate assets as desired
• Independent valuation of assets
• Operationally complex, high fix costs to run a platform, limitations
• Low quality control
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Trading Strategy
• Short only strategy
• SWX IAZI Investment Real Estate Price Index (SI Investment PR) and SWX
IAZI Private Real Estate Price Index (SI Private PR) are not
tradable/investable
• Not common to directly short real estate
• No relevant derivative market for Swiss real estate
• Short-strategy implemented through equity lending of real estate stocks
and funds
• Transaction costs depend on lending pool
• Systematic
• Leverage based on output of agent-based model
• Rebalancing of components is systematically based on market liquidity
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Risk Management
• Market Risk
• Use leverage for timing and optimizing risk/return profile; it also optimizes
borrowing costs
• No use of stop loss rules for reduction of drawdowns
• Liquidity Risk
• Rebalancing of the components systematically based on market liquidity and
borrowing fees; stock picking is not an issue
• Collateral management matrix approach in use
• Volume limits
• Daily, weekly and monthly limits
• for every component and for the total exposure
• Credit Risk
• Financial standing of the issuer
• Operational Risk
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Legal structure - Leonteq
Source: Half-Year Report 2014
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Roger Hilty
Roger Hilty is a Partner at LGT Capital Partners and the Co-Head of
the Trading Competence Centre. Mr. Hilty is responsible for
CTA/Macro manager research and portfolio management. Mr. Hilty
joined LGT CP in September 2004 from LGT Bank in Liechtenstein,
where he started his career in January 2000 in the Trading/Treasury
division of LGT Bank. From 2000 to 2004, his responsibilities
involved activities in equity, fixed-income, currency and commodity
exchange traded and OTC products as well as processing of
transactions for Asian, European and US markets. Mr. Hilty was also
involved in business management projects for the Trading/Treasury
group. Mr. Hilty holds a Bachelors degree in Business
Administration and an MBA in Finance from University of Applied
Sciences, Liechtenstein. Mr. Hilty lectures at the Universities of
Applied Sciences HTW Chur, Switzerland and Vaduz, Liechtenstein
in his spare time. Before starting in Finance, Mr. Hilty was a
professional road racing cyclist in the international cycling team GS
Ericsson Villiger (ERI).
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Disclaimer
This presentation has been prepared to provide general information about certain types of investment products to a limited number of sophisticated prospective
investors, in order to assist them in determining whether they may have an interest in the types of products described herein. Nothing in this presentation
constitutes an offer or solicitation for the purchase or sale of any financial instrument or a commitment by AVACO AG or any of its subsidiaries or affiliates to enter
into any transaction referenced herein.
When considering whether to purchase any financial instrument, or otherwise participate in any transaction, referenced herein, no reliance should be placed on
the information in this presentation. Such information is preliminary and subject to change without notice and does not constitute all the information necessary to
evaluate the consequences of purchasing any financial instrument, or otherwise participating in any transaction, referenced herein. In addition, this presentation
includes information obtained from sources believed to be reliable, but AVACO AG does not warrant its completeness or accuracy. Accordingly, any decision to
purchase any financial instrument, or otherwise participate in any transaction, referenced herein should be based solely on the final documentation related to such
financial instrument or transaction, which will contain the definitive terms and conditions thereof.
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and is intended only to illustrate hypothetical results under those assumptions (not all of which will be specified herein). Actual events or conditions are unlikely to
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