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[Cryptica 22] Data Driven Computational Finance - Miodrag Janjusevic

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[Cryptica 22] Data Driven Computational Finance - Miodrag Janjusevic

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Financial data are of outmost importance in Algorithmic Trading and proper Risk Management for Investment Banks, Hedge Funds and Financial Industry in General. Central Bank Regulators require Risk Management ability from Commercial Banks and other regulated institutions ( Basel III regulation). For instance, calculation of VAR ( Value at Risk ) and historical VAR critically depend on clean reliable date in order to provide meaningful risk measure. Bank stress tests that determine how much financial institution can loos in certain situations that in return determine the size of capital reserve required as well as historical stress test analysis depend on quality of data used. Algorithmic trading using Neural Networks, or AI in general are driven by reliable time series.

Financial data are of outmost importance in Algorithmic Trading and proper Risk Management for Investment Banks, Hedge Funds and Financial Industry in General. Central Bank Regulators require Risk Management ability from Commercial Banks and other regulated institutions ( Basel III regulation). For instance, calculation of VAR ( Value at Risk ) and historical VAR critically depend on clean reliable date in order to provide meaningful risk measure. Bank stress tests that determine how much financial institution can loos in certain situations that in return determine the size of capital reserve required as well as historical stress test analysis depend on quality of data used. Algorithmic trading using Neural Networks, or AI in general are driven by reliable time series.

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[Cryptica 22] Data Driven Computational Finance - Miodrag Janjusevic

  1. 1. Data Driven Computational Finance DSC – Cryptica Miodrag Janjusevic CEO FIKS
  2. 2. Introductory Remarks Data Science Conference - the international character and size makes it unique venue for all participants. It represents great knowledge sharing platform, networking opportunity and hopefully, and an incubator of new ideas that will generate positive impact on economic development, local and why not, international. We are witnessing a birth of new economy, driven by rapid technological development.
  3. 3. Introductory Remarks Computational speed & computer memory are still growing. This technological advances enabled new way of data processing, where machine learning is used to find patterns and regularities and use them to speed up, enhance and in some cases create decision making process. Financial Industry was in the forefront of innovation, regarding new technology. Neural networks or adaptive filters are used for algorithmic trading. Today, machine driven decision making creates buy and sell signals, and in turn represent the largest portion of daily trading volume. Blockchain technology is signaling a birth of new finance, speeding up velocity of money which always have positive impact on economy.
  4. 4. Data Driven Economy Driving ingredient of this new industry are data. Safe, clean data that are the fastest growing global commodity. Data processing already consumes almost 2-3% of global EE output. That means data centers, AI etc. In order to utilize data we need knowledge that can process data and create platforms for clients to get the products that we designed. Knowing it bring us to knowledge and how to go about acquiring it. I think that technological revolution goes hand in hand with the revolution in acquiring knowledge about this new technologies
  5. 5. Knowledge and its role in modern economy • Knowledge is what can bring societies that were economically devasted into position of economic well being. South Korea, Japan, China etc. • We are seeing signs of it in Serbia with IT industry. Last year 50K or so people in IT created about 2bil USD. And that is just a beginning. With more advanced products and more R&D those numbers will be significantly bigger.
  6. 6. Examples of knowledge innovation - Academia • MCF graduate program in computational finance. • Proper bland of academic fundamentals and practitioners insights in the latest developments in financial industry. • There is a big interest for the program and about 45-50 students, domestic and international are enrolled in the program. • we have between 45-50 people attending from Serbia and Europe. Professors are domestic and foreign. we have between 45-50 people attending from Serbia and Europe. Professors are domestic and fo
  7. 7. Examples of customized knowledge transmission-FIKS • There is also a way to do a customization of knowledge delivery. Attending classes and getting academic degree is sometimes expensive time wise and financially. • A few of us created company FIKS ( Financial Innovations, Knowledge Solutions ) where we in collaboration with experts in fields ( Data Science, AI, Blockchain Technology, Risk Management etc. ) provide knowledge solutions for corporates. • We want to be Innovative in the way we deliver knowledge. We want to provide innovative Financial solutions to clients. Our target audience for software solutions regarding portfolio risk management are Banks, Insurance Companies , Pension Funds and Hedge Funds. • We also develop trading arbitrage algorithms.
  8. 8. We create MRI ( MACRO RISK INDEX ) for specific portfolio that gives rolling history of that specific risk. To create it we use risk indicators for all asset classes ( FX, Commodity, Interest rates Equity) throughout different geographies ( US, Europe, Asia etc ) Implied volatility is the way that markets measure risk. Example one – MRI Index
  9. 9. Example two: Arbitrage trading system Five-year CDS versus Vol-Credit Model 23/04/01 23/05/01 23/06/01 23/07/01 23/08/01 23/09/01 23/10/01 23/11/01 23/12/01 23/01/02 23/02/02 23/03/02 23/04/02 23/05/02 23/06/02 23/07/02 23/08/02 23/09/02 23/10/02 23/11/02 23/12/02 23/01/03 23/02/03 23/03/03 23/04/03 0 100 200 300 400 500 600 700 800 CDS Model Debt/Equity arbitrage system that looks for mispricing in default risk protection . CDS – Credit Default Swap is standard default protection contract Alternative way of creating it default protection is using equity options Credit Risk Reversal(CRR).. One can show that CDS and CRR are equivalent in their insurance properties and market dynamics. That allow us to create a data base of the names that can be traded and we have a way of identifying those discrepancies. e created Debt/Equity arbitrage system that looks
  10. 10. Example two: Arbitrage trading system • The key is weather one can access data needed for construction of arbitrage driven trading system. • In order to ascertain trading opportunity we have to compare current spread to its historical averages. • Prices of accessing historical volatility surfaces surfaces is high. ( check Bloomberg data prices ). • Creating historical volatility surface represents combination of using existing data and extending it via non arbitrage extrapolation to maturities and strikes not seen on the exchanges.
  11. 11. At the end… • Data as the main global commodity • Clean reliable data allow creative solutions • Examples above give you an idea of how to go about value added based on data available…. Good luck and have a great conference!
  12. 12. Contact informations Miodrag Janjusevic CEO FIKS d.o.o +38163238212 +19149530533 www.fiksrs.com • miodrag.janjusevic@fiksrs.com • raisa.khamidullina@fiksrs.com • branko.urosevic@fiksrs.com •

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