Olsen

The Future Of Digital Markets

Richard Olsen

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Outline

• Distributed ledger technology• One global Internet market• Price-spread-time matching engine• Complete automation of decisions• Event-based intrinsic time• WikiFinClimate

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Today

• Financial system architecture is a pile of spaghetti....• Historically grown - step by step computerization of

manual business processes. • Delivery and settlement of trades is batch based at t+2

days from time of trade.• Every bank relies on its own book keeping. Institutions

are islands – verification of trades is cumbersome.• High uncertainty, multiplication of risk, big transaction

costs and lack of liquidity and transparency.

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Bank of England Report Q3 2014:

Distribututed Ledger Technology (DLT) is biggest innovation since discovery of double bookkeeping.

Proof of concept:Bitcoin (2009) Variations (Litecoin, Ethereum, Ripple, etc).

Distributed Ledger Technology

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……………….

Distributed Ledger Technology

A

Internet broadcast

B

Bookkeeper

Bookkeeper

Bookkeeper

Bookkeeper

Bookkeeper

Validation

The ledger

The ledger

The ledger

The ledger

The ledger

……………….10 minute cylces

Trad

e

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Benefits of DLT

• DLT for any financial asset• Distributed bookkeeping• N eyes are better than 2 eyes• No central authority• Within 10 minutes validated transaction ledger• No double spending• The ledger is the ledger is the ledger is the ledger...• Transparency, certainty and clarity

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DLT For All Assets And Issuers

Distributed Ledger Technology (DLT) with

colored coins

Transactions

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Internet Exchange

Distributed Ledger Technology (DLT)

Internet Exchange:matching system for orders,

revenue x % of average spread

Hourly Fixings WikiFinClimate

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Matching Engine

Objective: efficient price discovery• Minimizing transaction costs• Consolidation of liquidity• Orderly queueing by all market participants• Optimizing information flow

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80% Flow With <20 Min Duration

• Short-term market dynamics dictate price action.• Spread valuable indicator of private information• Expectation and price discovery

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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)

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Effects Of Price-Spread-Time Queue

• Not same as minimum lifetime of quotes• Continuous reshuffling of queue, more stochastic• Changes speed race• Crossing of spread with one-sided prices• Low micro volatility and efficient price discovery

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Benefits Of Internet Exchange

• 10 minute settlement• Low transaction costs • Any ticket size• All asset classes and issuers• All cross rates available• Emergent money• Liquidity• Efficient, fair, transparent

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Complete Automation Of Decisions

• Today, models need handholding.• Trading glitches (Flash crash, Knight Capital, etc.)• Future full automation• Shift of academic work to real time testing• We need to come up with powerful models!

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Necessity To Rethink Time

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Relativity Theoy: Twin Paradox

Is economics is a multi-system problem?

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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?

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Physical Time Is Static

Mapping of data with physical time grid loses information about extremes.

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Frequency Of Sampling

Increasing Frequency Of Observations Adds Noise

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Increased Sampling Reduces Signal Quality

• Basic problem: information is in tails.• Signal to noise ratio deteriorates with increased sampling.

Signal

Noise

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Time Issues• Sampling and testing in physical time • Uniqueness of events• Sampling frequency and length of coastline• Consistent time aggregation across time scales • Impact of seasonality and heatwave effects

Model quality is as good as definition of time.

10 reaserch papers or so discuss time...low citation number.

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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.

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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.

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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.

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Empirical Evidence

Overshoots are on average equal to threshold; this is true for all observed thresholds: scaling law.

Valuable ex ante information

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Schemata Event Time

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Physical versus Event Time

Physical timee.g. [hours]

1 2 3 4 5 6

Intrinsic time[events]

1 2 3 4

Dx

-Dx

-Dx

Dx

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Static Versus Dynamic View

Physical timee.g. [hours]

1 2 3 4 5 6

Intrinsic time[events]

1 2 3 4

Dx

-Dx

-Dx

Dx

Assymetric uncertainty

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Established scaling laws

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

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Scaling laws

Decomposing total price move into directional-change and overshoot:

Leads to 9 additional scaling laws:

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Tick-count scaling law

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Benefits Of Scaling Laws

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

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Extrema

Exposure

1% 1%

Observe Manage

Coastline TradeTake Profit

Take Profit

Average

Open

Average Overshoot

Agent-based Models

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Agent Models With Scaling Laws

• Dynamic investment strategies• Liquidity provision• Price stabilizing • Outlook: 85 percent dynamic strategies

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WikiFinClimate

• Wikipedia for keywords: 4.6 Mio articles in English • WikiFinClimate for time series• Complete Automation Of Decisions• Crowd-based modeling• Real time information service• Graphical visualization, etc.

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WikiFinClimate

Distributed Ledger Technology

Time Series

Internet Exchange

Crowd-based information system

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WikiFinClimate

Contributors

modeling, simulation, real time operation

Model A Model … …………… Lego block 4 ……………..

Users

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Conclusion

• One global Internet market with DLT• Price-spread-time matching engine• Complete Automation Of Decisions• Event-based intrinsic time• Agent-based models with scaling laws•WikiFinClimate• Lots of open questions, work....