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The hidden treasure of high frequency dynamics:  from intrinsic time to 12 new scaling laws Richard Olsen OLSEN Liquidity Investing
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
You only see what you look at Within a 24 hour time window, the world is more or less the same, so one day of tick data is more valuable than 100 years of daily data.  One day of high frequency tick by tick data is equivalent 100 years of daily data. Daily Data  High-Frequency Data  1 day 1 25‘000 1 year 250 5‘000‘000 10 years 2‘500 50‘000‘000 100 years 25’000 500’000’000
Tick data contains information Risk capacity Market maker Price change
Market maker sends signals Market maker Buyer Seller
Market moves are non-linear price change liquidations margin call Large price trends are the result of  cascading margin calls.  Example: recent USD upmove is result of everyone getting it  wrong....
Example: EUR_USD price action Friday 9th Jan 2009 Non-Farm Payroll data: 598‘000 jobs lost.
Example: trader action on Friday 9th Jan 2009 Orders Positions Jan 9, 2009 12.30 Jan 9, 2009 13.30 Jan 9, 2009 14.30 Jan 9, 2009 12.30 Jan 9, 2009 13.30 Jan 9, 2009 14.30 Complimentary OANDA service, see http://fxlabs.oanda.com
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
EUR-USD, Nov 21 2008, daily return A significant part of the activity is disregarded
EUR-USD, Nov 21 2008, daily return Some activity is still disregarded, especially in the afternoon
EUR-USD, Nov 21 2008, 5 minutes return 0.3 % 1.2% Is market time passing by at a constant pace?
In need of an event-based approach? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Intrinsic time :  an insight (1)  Physical time e.g. [hours] 1 2 3 4 5 6 Intrinsic time [events] 1 2 3 4  x  x  x  x
Intrinsic  time : an insight (2)
Intrinsic time: pros and cons ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Existing 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
High-frequency FX data  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1. Tick-count scaling law
Other laws at a glance (1) 2. Average yearly number of price moves 3. Average maximal price move during a time interval 4. Average duration of a price move  5. Average duration of a directional  change
Other laws at a glance (2) Decomposition of total price move into directional-change and overshoot 6-14. L eads to 9 additional scaling laws
Other laws at a glance (3) 15-17. Cumulative price moves We find  12  independent new scaling laws.
All the scaling laws at a glance
The coastline of prices (1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The coastline of prices (2) ,[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Outlook (1) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Outlook (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Finance as the next computer technology ·  other industries have succeeded, why not economics and finance? · human beings, complexity, unforeseen events. · high frequency finance,  new concepts, significant financial resouces.  · Yes, it is feasible ·  Thank you.
Directional change algorithm
Length and duration of overshoots An interesting feature a lso found by considering a binomial tree And the time is twice in an overshoot than in a directional change
Comparison with a GRW ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Exploring the space of scaling law (1) where Simililarly The average time for a price move to occur should be the yearly number of seconds divided by the average number of price moves indeed
Exploring the space of scaling law (2) T he empirical values lead to  Hence there is a tick to be expected every 279 seconds. The average duration of price moves indicate an 0.02% price move every 258 seconds. Scaling laws can be assembled to produce additional laws. For instance

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HTHFD

  • 1. The hidden treasure of high frequency dynamics: from intrinsic time to 12 new scaling laws Richard Olsen OLSEN Liquidity Investing
  • 2.
  • 3. You only see what you look at Within a 24 hour time window, the world is more or less the same, so one day of tick data is more valuable than 100 years of daily data. One day of high frequency tick by tick data is equivalent 100 years of daily data. Daily Data High-Frequency Data 1 day 1 25‘000 1 year 250 5‘000‘000 10 years 2‘500 50‘000‘000 100 years 25’000 500’000’000
  • 4. Tick data contains information Risk capacity Market maker Price change
  • 5. Market maker sends signals Market maker Buyer Seller
  • 6. Market moves are non-linear price change liquidations margin call Large price trends are the result of cascading margin calls. Example: recent USD upmove is result of everyone getting it wrong....
  • 7. Example: EUR_USD price action Friday 9th Jan 2009 Non-Farm Payroll data: 598‘000 jobs lost.
  • 8. Example: trader action on Friday 9th Jan 2009 Orders Positions Jan 9, 2009 12.30 Jan 9, 2009 13.30 Jan 9, 2009 14.30 Jan 9, 2009 12.30 Jan 9, 2009 13.30 Jan 9, 2009 14.30 Complimentary OANDA service, see http://fxlabs.oanda.com
  • 9.
  • 10. EUR-USD, Nov 21 2008, daily return A significant part of the activity is disregarded
  • 11. EUR-USD, Nov 21 2008, daily return Some activity is still disregarded, especially in the afternoon
  • 12. EUR-USD, Nov 21 2008, 5 minutes return 0.3 % 1.2% Is market time passing by at a constant pace?
  • 13.
  • 14. Intrinsic time : an insight (1) Physical time e.g. [hours] 1 2 3 4 5 6 Intrinsic time [events] 1 2 3 4  x  x  x  x
  • 15. Intrinsic time : an insight (2)
  • 16.
  • 17.
  • 18. Existing 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
  • 19.
  • 21. Other laws at a glance (1) 2. Average yearly number of price moves 3. Average maximal price move during a time interval 4. Average duration of a price move 5. Average duration of a directional change
  • 22. Other laws at a glance (2) Decomposition of total price move into directional-change and overshoot 6-14. L eads to 9 additional scaling laws
  • 23. Other laws at a glance (3) 15-17. Cumulative price moves We find 12 independent new scaling laws.
  • 24. All the scaling laws at a glance
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31. Finance as the next computer technology · other industries have succeeded, why not economics and finance? · human beings, complexity, unforeseen events. · high frequency finance, new concepts, significant financial resouces. · Yes, it is feasible · Thank you.
  • 33. Length and duration of overshoots An interesting feature a lso found by considering a binomial tree And the time is twice in an overshoot than in a directional change
  • 34.
  • 35. Exploring the space of scaling law (1) where Simililarly The average time for a price move to occur should be the yearly number of seconds divided by the average number of price moves indeed
  • 36. Exploring the space of scaling law (2) T he empirical values lead to Hence there is a tick to be expected every 279 seconds. The average duration of price moves indicate an 0.02% price move every 258 seconds. Scaling laws can be assembled to produce additional laws. For instance

Editor's Notes

  1. langle |Delta x | angle_p = left( frac{ Delta t } { C_x (p)} ight)^{E_x (p)} ; extrm{where} ; langle x angle_p = left( 1/n sum_{j=1}^n x_{j}^p ight)^{1/p} , p={1;2} 24 extsf{N} (Delta x_{dc}) = left( frac{ Delta x_{dc}}{ C_{ extsf{ iny N},dc} } ight)^{E_{ extsf{ iny N},dc}}
  2. langle extsf{N} (Delta x_{tck}) angle = left( frac{Delta x} {C_{ extsf{ iny N},tck}} ight)^{E_{ extsf{ iny N},tck}} quad extrm{where} , Delta x_{tck}=0.02\%
  3. extsf{N} (Delta x) = left( frac{ Delta x} { C_{ extsf{ iny N},x} } ight)^{E_{ extsf{ iny N},x}} langle Delta x_{m} angle_p = left( frac{Delta t}{C_{m}(p)} ight)^{E_{m}(p)} langle Delta t_x angle = left( frac{Delta x}{C_{t,x}} ight)^{E_{t,x}} langle Delta t_{dc} angle = left( frac{Delta x_{dc}}{C_{t,dc}} ight)^{E_{t,dc}}
  4. langle | Delta x^{tm} | angle &=& langle | Delta x^{dc} | angle + langle | Delta x^{os} | angle , \langle Delta t^{tm} angle &=& langle Delta t^{dc} angle + langle Delta t^{os} angle, \langle extsf{N}( Delta x^{tm}_{tck}) angle & =& langle extsf{N}( Delta x^{dc}_{tck}) angle + langle extsf{N} (Delta x^{os}_{tck}) angle label{slcondx1}langle | Delta x^* | angle &=& left( frac{{Delta x}_{dc}}{C_{x,*}} ight)^{E_{x,*}} \label{slcondx2}langle Delta t^* angle &=& left( frac{{Delta x}_{dc}}{C_{t,*}} ight)^{E_{t,*}} \label{slcondx3}langle extsf{N}( Delta x^{*}_{tck}) angle &=& left( frac{{Delta x}_{dc}}{C_{ extsf{ iny N},*}} ight)^{E_{ extsf{ iny N},*}}
  5. Delta x_{cum}^* & = & sum_{i=1}^n | Delta x_i^{*} | \& = & left( frac{{Delta x}_{dc}}{C_{cum,*}} ight)^{E_{cum,*}}
  6. langle | Deltax^{os} | angle approx Delta x_{dc};; extrm{as} ;; E_{x,os}^{av} approx 1.04; extrm{and}; C_{x,os}^{av} approx 1.06 0.5 ; Delta x + 0.5^2 ; 2 Delta x + 0.5^3 ; 3 Delta x + ldots = Delta x sum_i^n i ; 0.5^i xrightarrow{n ightarrow infty} 2 Delta x langle | Deltat^{os} | angle approx 2 langle | Delta t^{dc} | angle langle extsf{N} ( Delta x^{os}_{tck}) angleapprox 2 langle extsf{N} ( Delta x^{dc}_{tck}) angle
  7. left( frac{Delta x}{C_{t,x}} ight)^{E_{t,x}} = langle Delta t_x angle = Y /mathsf{N}(Delta x) = frac{Y}{left( frac{ Delta x} { C_{ extsf{ iny N},x} } ight)^{E_{ extsf{ iny N},x}}} E_{t,x}^{} = 1.93 =-E_{ extsf{ iny N},x}^{} ; extrm{and} ; C_{t,x}^{} = 1.23 cdot 10^{-3}= Y^{1/E_{ extsf{ iny N},x}^{}} C_{ extsf{ iny N},x}^{} E_{t,dc}^{} & = & 1.88 approx 1.91 =-E_{ extsf{ iny N},dc}^{} \ C_{t,dc}^{} & = & 1.05 cdot 10^{-3} approx1.11 cdot 10^{-3}= Y^{1/E_{ extsf{ iny N},dc}^{}} C_{ extsf{ iny N},dc}^{} Y = 31'553'280 ; extrm{seconds}
  8. E_{t,tck}^{} = 0.96 ; extrm{and} ;C_{t,tck}^{} = 279