Agent-based models of financial markets Scenario-analysis and simulation. October 26, 2015

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1 Agent-based models of financial markets Scenario-analysis and simulation October 26, 2015 Seminar Overview September 14: Introduction September 21: Basic concepts of ABM September 28: Examples of different models October 5: Introduction to the development framework October 12: Development of financial market models October 19: Optimization and overfitting October 26: Scenario-analysis and simulation November 2: Commercialization of models - How to pitch? November 9: No lecture November 16: The agents behavior (Guest lecture Faten Ben Bouheni, ISC Paris) November 23: Pitch of the Models November 30: Pitch of the Models December 7: Pitch of the Models December 14: General Feedback to the Pitch Sessions and some conclusions

2 Presentation November 23 Title Student Presentation Soybean Timo Schaefer Iron ore Mahmud El Saghir Bernhard Süess SSE Composite Index JingyuanTian Sugar Future Jonas Schultz Roman Pfenninger Shanghai Stock Exchange Composite Index Heng Liu Gold Johannes Baltruschat Maurin Manhart Chinese Real Estate Market Yue Qiu Li Wan Gold and US Bond Jiayu Liang Oslo housing market Sebastian Stormbo Gold Alexander Norring Fabian Meister ishares Global Timber & Forestry Index Fund Dominique Burgherr Daniel Schöpflin Gold Laurenz Kaiser Presentation November 30 Title Student Presentation Rice Tom Englaro Luca Zachmann Bond CH Evangelos Kafetzakis Madika Vasiliki Oil Jan Berger Florian Tunger Oil Dominik Tedja Oil Ida Weber Michel Guillet Dow Jones, Euro Stoxx 50, Nikkei Luca Huber Sascha Michaelowa Luxury stocks Benjamin Rhys Müller Erik Eriksson SMI Konstantin Furrer Raphael Huber SPI Nico Kaiser Tommaso Plebani Coal Theodoros Protopsaltis Dimitros Lazaris MSCI World Daniel Salamanca The Powershares WinderHillClean Energy ETF (PBW) Samuele Lombardini

3 Presentation December 7 Title Student Presentation USD, EUR, CHF Exchange rates Kevin Decnaeck EUR/USD Patrick Röthlisberger USD/CAD Ana Kurtanidze Premek Mares USD/RUB Luzius Bein Tim Luginbuehl USD/RUB Christian Schmidiger Marc Staub USD/RUB Andrea Masotti Nikita Ryjov NOK/SEK Michael Kilchenmann Nico Schmid USD/NOK and Oil Kristoffer S. Odegaard USD/ZAR Silvio Leoni Marco Schnüriger Dow Jones Florian Buchs Alexandre Munday Apple Stock Bas Monsewije Shanghai Stock Exchange Composite Index Manuel Burbano Ajla Binz Content Swiss real estate market Simulation environment Adaptation of the development framework Scenarios of the S&P 500 Quantitative easing

4 Model of the Swiss real estate market Real estate prices Investment Real estate prices Private Trend followers Market BUY! Rental fees Interest rates Private residents Stocks SELL! Population Investors ABM of the Swiss real estate market (1) Real Estate Price Index & Modeled Trading Positions The chart shows the development of the SI Investment PR index and the BUY-/SELL-signals generated by the model Hit Rate: h = 0.77 Model Efficiency: r = 0.56 (June 30, 2015) 80 '87 '89 '91 '93 '95 '97 '99 '01 '03 '05 '07 '09 '11 '13 '15 Buy-/Sell-Signal SI Investment PR

5 ABM of the Swiss real estate market (2) Real Estate Price Index & Modeled Trading Positions The chart shows the development of the SI Private PR index and the BUY-/SELL-signals generated by the model Hit Rate: h = 0.77 Model Efficiency: r = 0.56 (June 30, 2015) '87 '89 '91 '93 '95 '97 '99 '01 '03 '05 '07 '09 '11 '13 '15 Buy-/Sell-Signal SI Private PR Scenario rising interest rates Real Estate Price Indices and Forecast (assuming Rising Interest Rates) 80 '87 '89 '91 '93 '95 '97 '99 '01 '03 '05 '07 '09 '11 '13 '15 '17 '19 SI Investment PR SI Private PR Forecast Swiss Bond Yields Szenario Aside from producing trend predictions for the next quarter, the model can be used for long term scenario analysis and stress testing. In the following example, the effects of a longterm rise in interest rates on the SI Investment PR and SI Private PR are analyzed. The simulation is run up to the last quarter s end using historical data, then the interest rates are continuously increased. From this point on, the output time series (SI Investment PR and SI Private PR) are generated by the model. The chart shows the outcome of the simulation for both target indices and also the yield of the Swiss Bond Index as the input variable. For both indices, a long-term increase of interest rates leads to a clearly observable and significant decrease.

6 «Nil-scenario» 180 SI Investment PR 200 SI Private PR In a situation lacking both, positive and negative market forces, the simulated market nevertheless can correct negatively. The reason is that the majority of agents/market participants is already invested in real estate, and their potential to add further assets to their investment portfolio is limited due to a budget constraint. If however negative market impulses prevail, a significant negative correction is to be expected according to the simulation. Therefore, according to the model, a further long-term rise in the Swiss real estate market is to be expected only in a regime of prolonged, strong, and positive market forces. Simulation environment A simulation environment should allow to make scenario analysis and experiments which are impossible in reality because the parameters of the environment cannot be controlled. The simulation environment and the reality should have the same properties. At the same time, the simulation environment ought to be as simple as possible.

7 Adaptation of the Framework (1) The implemented price calculation is based on demand and supply: = + () + +. p is the time series and y is the predicted time series; D is demand and S is supply of all agents. is 0.1. Adaptation of the Framework (2) The implemented price calculation is based on demand and supply: = p is the time series and y is the predicted time series; D is demand and S is supply of all agents. is 0.1.

8 Prices Ticker

9 Scenario Increasing rates Scenario Decreasing rates (1)

10 Scenario Decreasing rates (2) Investment Outlook

11 Quantitative easing (QE) Unconventional monetary policy tool Buying financial assets while increasing the monetary base to prevent/ offset deflation First used by the Bank of Japan in the early 2000s Bank of England started 2009 US Federal Reserve: Different programs European Central Banks: October 2014 Simulation and Scenario of QE

12 Global Macro Model Nikkei Bond Gold JPY DAX Oil USD EUR S&P Bond Bond CHF SPI Bond Performance

13 Simulation Results