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The Future Of Digital Markets

The Future Of Digital Markets. Richard Olsen. Outline. Distributed l edger t echnology One global Internet market Price- spread -time matching engine Complete automation o f d ecisions Event- based intrinsic time WikiFinClimate. Today.

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The Future Of Digital Markets

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  1. The Future Of Digital Markets Richard Olsen

  2. Outline • Distributed ledgertechnology • One global Internet market • Price-spread-time matchingengine • Completeautomationofdecisions • Event-basedintrinsic time • WikiFinClimate

  3. Today • Financial systemarchitectureis a pileofspaghetti.... • Historicallygrown - stepbystepcomputerizationofmanualbusinessprocesses. • Deliveryandsettlementoftradesisbatchbasedat t+2daysfrom time oftrade. • Every bankrelies on its own book keeping. Institutions are islands – verification of trades is cumbersome. • High uncertainty, multiplicationofrisk, bigtransactioncostsand lack ofliquidityandtransparency.

  4. Bank of England Report Q3 2014: DistribututedLedger Technology (DLT) isbiggestinnovationsincediscoveryof double bookkeeping. Distributed Ledger Technology Proofofconcept: Bitcoin (2009) Variations (Litecoin, Ethereum, Ripple, etc).

  5. Distributed Ledger Technology Bookkeeper The ledger A Bookkeeper The ledger Internet broadcast Validation Trade Bookkeeper The ledger Bookkeeper The ledger B Bookkeeper The ledger ………………. ………………. 10 minute cylces

  6. Benefitsof DLT • DLT foranyfinancialasset • Distributed bookkeeping • Neyesarebetterthan 2 eyes • Nocentralauthority • Within 10 minutesvalidatedtransactionledger • No double spending • The ledgeristheledgeristheledgeristheledger... • Transparency, certaintyandclarity

  7. DLT For All AssetsAndIssuers Distributed Ledger Technology (DLT) withcoloredcoins Transactions

  8. Internet Exchange Internet Exchange: matchingsystemfororders, revenue x % ofaveragespread Hourly Fixings WikiFinClimate Distributed Ledger Technology (DLT)

  9. Matching Engine Objective: efficientpricediscovery • Minimizingtransactioncosts • Consolidationofliquidity • Orderlyqueueingby all marketparticipants • Optimizinginformationflow

  10. 80% Flow With <20 Min Duration • Short-term marketdynamicsdictatepriceaction. • Spreadvaluableindicatorof private information • Expectationandpricediscovery

  11. Price-Spread-Time Queueing Spread is indicator for private information and powerful tool to shape expectations. Matching engines need to reward market participants for revealing private information. See Golub, A., Dupuis, A., Olsen R. B., "High Frequency Trading Strategies in FX Markets", In High-Frequency Trading - New Realities for Traders, Markets and Regulators, Edited by Easley, D., Lopez de Prado, M., O'Hara, M., 2013, (Riskbooks)

  12. EffectsOf Price-Spread-Time Queue • Not same asminimumlifetimeofquotes • Continuousreshufflingofqueue, morestochastic • Changesspeedrace • Crossingofspreadwithone-sidedprices • Low microvolatilityandefficientpricediscovery

  13. BenefitsOf Internet Exchange • 10 minutesettlement • Low transactioncosts • Any ticket size • All assetclassesandissuers • All crossratesavailable • Emergentmoney • Liquidity • Efficient, fair, transparent

  14. Complete Automation OfDecisions • Today, modelsneed handholding. • Trading glitches(Flash crash, Knight Capital, etc.) • Future full automation • Shiftofacademicworkto real time testing • Weneedtocomeupwith powerful models!

  15. Necessity To Rethink Time

  16. RelativityTheoy: Twin Paradox Iseconomicsis a multi-systemproblem?

  17. How To Sample A Time Series? • Tick-by-tick? • Every second? • Every minute, hour, day, week, month…data? • How to interpolate, if no data is available?

  18. Physical Time Is Static Mapping of data with physical time grid loses information about extremes.

  19. Frequency Of Sampling Increasing Frequency Of Observations Adds Noise

  20. Increased Sampling Reduces Signal Quality Noise Signal • Basic problem: information is in tails. • Signal to noise ratio deteriorates with increased sampling.

  21. Time Issues • Sampling and testing in physical time • Uniqueness of events • Sampling frequencyandlengthofcoastline • Consistent time aggregationacross time scales • Impact ofseasonalityandheatwaveeffects Model qualityisasgoodasdefinitionof time. 10 reaserchpapersor so discuss time...lowcitationnumber.

  22. Physical Time • Physical time maps the rotation of the earth. • It is a uniform scale: X = (X_1, X_2, X_3 …. ) • Events have equal weights. • There are fixed equidistant time intervals of 1 minutes, 1 hour, 1 day, 1 week.

  23. Event Time: Reversal From Extreme An event is defined as a price reversal from extreme by x %. In our papers we call a price reversal a directional change.

  24. Intrinsic Event Time • Uniform intrinsic time scale for a given reversal of x percent: X = (X_1, X_2, X_3 …. ) • Events have equal weights in intrinsic time • Events occur after reversal of x percent from extreme. • Intrinsic time scales of different thresholds are analogous to 1 minute, 1 hour, 1 day, etc. time scale.

  25. Empirical Evidence Valuable ex ante information Overshoots are on average equal to threshold; this is true for all observed thresholds: scaling law.

  26. Schemata Event Time

  27. Physical versus Event Time Physical time e.g. [hours] 1 2 3 4 5 6 -Dx -Dx Dx Dx Intrinsic time [events] 2 3 4 1

  28. Static Versus Dynamic View Physical time e.g. [hours] 1 2 3 4 5 6 -Dx -Dx Dx Dx Intrinsic time [events] 2 3 4 1 Assymetric uncertainty

  29. Establishedscalinglaws Müller et al., J. Bank Finance, 1990: Mean absolute change of mid-price to time Guillaume et al., Finance Stoch. 1997: Number of directional changes to thresholds

  30. Scalinglaws • Decomposingtotal pricemoveintodirectional-change andovershoot: • Leadsto 9 additional scalinglaws:

  31. Tick-count scalinglaw

  32. BenefitsOfScaling Laws • Short data samples for estimation • Dynamic frame of reference • Grid of scaling laws – combine information • Event language as function of overshoot

  33. Agent-based Models Observe Open Manage Extrema Average Overshoot Average Exposure Take Profit Coastline Trade Take Profit 1% 1%

  34. Agent Models WithScaling Laws • Dynamic investmentstrategies • Liquidityprovision • Price stabilizing • Outlook: 85 percentdynamicstrategies

  35. WikiFinClimate • Wikipedia forkeywords: 4.6 Mio articles in English • WikiFinClimatefor time series • Complete Automation OfDecisions • Crowd-basedmodeling • Real time informationservice • Graphicalvisualization, etc.

  36. WikiFinClimate Crowd-basedinformationsystem Time Series Internet Exchange Distributed Ledger Technology

  37. WikiFinClimate Contributors Users Model A Model … …………… Lego block 4 …………….. modeling, simulation, real time operation

  38. Conclusion • One global Internet marketwith DLT • Price-spread-time matchingengine • Complete Automation OfDecisions • Event-basedintrinsic time • Agent-basedmodelswithscalinglaws • WikiFinClimate • Lots of open questions, work....

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