University of Zurich Seminar Overview Agent-based models of financial markets

University of Zurich
Agent-based models of financial markets
Development of empirical models
October 20, 2014
Seminar Overview
• September 15:
Introduction
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September 22:
September 29:
October 6:
October 13:
October 20:
November 27:
Basic concepts of ABM
Examples of different models
Introduction to the development framework
Fundamentals of financial market modelling
Development of empirical models
Optimization and over-fitting
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November 3:
November 10:
November 17:
November 24:
December 1:
December 8:
The agents’ behavior
Scenario-analysis and simulation
Commercialization of models
How to pitch?
Pitch of the Models
Pitch of the Models
• December 15:
Pitch of the Models
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Content
• Q&A case study
• Questions & decisions
• Purpose of the model
• Market
• Agents
• Economic foundation
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Q&A case study
• Deadline for the model, documentation and presentation slides is
Friday before the presentation date (ta@avaco.ch).
• The back up is used for an additional presentation date.
• Appointments are available upon request. The meeting place is next
the lecture hall (Begegnungszimmer).
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Case studies
Title
Beyond valuation
CHFUSD
CHFYEN
EURCHF
EURCHF
EURCHF (SPI)
Gold
Gold
Gold
tbd
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
Ankit Doshi
Jan-Thore Hünecke
Basil Odermatt
Luca Tonizzo
Nicola Lei Ravallo
Agata Gareishina
Jonas Elmer
Christian Scheitlin
Vincent Rime
Ewelina Laskowska
Valerio Frison
Raphael Dosch
Marco Rudin
Cesare Scherrer
Dario Messi
Qian Cao
Lukas Hauri
Presentation
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www.avaco.ch
Case studies
Title
Bitcoin
Bitcoin
Bitcoin
Bitcoin
Corporate Bond Market
Dax
Dax
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
Dzemo Facli
Laura Oberbörsch
Jan Kaiser
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
08.12.2014
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Case studies
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
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Elements of Financial Markets
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Presentation
15.12.2014
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Do not forget ☺!
• Economics is a social science dealing with the interactions of human
beings.
• Need to abstract from reality reduce complexity!
• Economic processes are complex: It is difficult to decompose them into
different parts that can be studied separately and then be aggregated to
yield a complete picture.
• Model financial markets as multivariate, non linear, deterministic systems
with stochastic shocks and a drift component (Neither normally
distributed nor stationary)
• In an ABM the agents don’t need to be profitable or rational The real
world market’s results stem from the interaction of real world market
participants!
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Questions & decisions
• Purpose of the model
• System definition and boundaries
• Market
• Agents
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Input
Decision rules
Individual learning
Initialization
• Economic foundation
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Elements of Agent-Based Models
Agents
• Market participants
• Behavioral disposition
• Microeconomics
• Explicit knowledge in the form of
agents’ behavioral structures
• Individual basic decision structures
with bounded rationality
• Decentralized, heterogeneous and
local
• Adaptive, individual learning
• Analytical
Environment
• Market
• Market structure
• Macroeconomics
• Implicit knowledge in the form of
time series analyses
• Complex, non-linear, path-dependent
• Collective behavior leads to a selforganizing system
• Social learning and changing market
structures (regulation, …)
• Computational
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Purpose of the model
• Calibration, Validation, Benchmark
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Trend direction
Trading
Distribution
Correlation
Stylized facts
• Frequency
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Purpose of the model
• Trading
• Portfolio management
• Risk management
• Impact studies for regulators
•…
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Decision based on the purpose
What decision do you make based on the purpose of the model?
Please take 10 minutes to think about this question.
Briefly prepare for a discussion in class - discuss it with your
neighbors.
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Market
• Assets
• The time series should span different cycles of the market.
• Market structure
• Order processing
• Price calculation
• Clearing and settlement
• Evolution and social learning
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Assets
3.0000
350.00
2.5000
300.00
2.0000
250.00
1.5000
200.00
1.0000
150.00
100.00
0.5000
50.00
0.0000
0.00
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Agents
• Input
• Decision rules
• Individual learning
• Initialization
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Agents behavior
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Market participants
• Traders
• Outright
• Arbitrage
• Investors
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Pension funds
Insurance companies
Asset Managers
Private
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• Different profiles
• Time
• Risk
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Outright
• Arbitrage in the time dimension
• Decision under partial uncertainty
• Forecasting models
• Risk management
Arbitrage
• Profit from different prices on different markets for similar products
• Geographic differences
• Cash – Forward markets
• Options, CFDs, …
• Theoretically zero risk position
• Real time program trading
• Small profits
How do algorithms shape our world?
www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world
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Economic foundation
• Explicit input from observations
• Explicit input from data time series
• Implicit input from time series
• Be careful with macroeconomic time series.
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Iterative modeling process
• Formulation of hypothesis and specification
• Market environment
• Market participants (agents)
• Modelling and experimentation
• Calibration and validation
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