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“probability” distribution PDF

26 Pages·2015·7.3 MB·English
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From Quantum Randomness to Fat-Tails and High-Peaked Distributions in Financial Markets Espen Ga arder Haug www.espenhaug.com NASDAQ MARKET FORUM MILANO 21 Mai 2015 1 Property of (cid:2)probability(cid:3) distribution • Fat-Tail/High-peaked distribution • Heavy Tailed distribution • Leptokurtic distribution • Non-Gasussian distribution 2 Relay too much on what we think we know! •  The Turkey Problem/ Juleribbe problemet •  Focus (also) on what you do not know! 3 The Gauss Curve/Math •  1733 Abraham de Moivre •  1783 Laplace extended on Moivre •  1794 Carl Friedrich Gauss ( published 1809 ) 4 Esprit Jouffret coined it the Bell curve in 1872 5 Swedish astronomer and mathematician Carl Vilhelm Ludwig Charlier 1920: “The responsibility for stagnation in the development of mathematical statistics until recent years rests principally upon Gauss. The great mathematician believed it possible to demonstrate that the fluctuations of the items of a statistical series – he was concerned chiefly with astronomical and geodesic observations – followed the simple law which was called after him, the Gaussian law of error. He believed the deviations from the law were accidental and would disappear if the observations increased.” 6 The Distribution of Galaxies 1990: finds high peak (and likely fat-tails). Claims data must be wrong! 2000: Finds out model is wrong 7 Arne Fisher 1922 “The great German mathematician -- or rather the dogmatic faith in his authority as a mathematician -- proved thus for a number of years a veritable stumbling block to a fruitful development of mathematical statistics. `` Fisher Kurtosis 8 Arne Fisher 1922 “The Gaussian dogma held sway despite the fact that the Danish actuary, Opperman, and the French mathematicians, Binemaye and Cournot, have pointed out that several statistical series, despite all efforts to the contrary, offered a persistent defiance to the Gaussian law.” 9 10 Figure 1: Electricity Spot Daily Returns Daily data from Nov 5- 2001 to Nov 3- 2004 Volatility 114.99%, Skewness 0.96, Pearson kurtosis 11.12 105 85 s n o ati 65 Real returns v er Normal s b O 45 of r be 25 m u N 5 % % % % % % % % % % % % % % % % % % % % % % % 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5. 1. 7. 3. 9. 5. 1. 7. 3. 9. 5. 1. 3. 7. 1. 5. 9. 3. 7. 1. 5. 9. 3. 4 4 3 3 2 2 2 1 1 - - - 1 1 1 2 2 3 3 3 4 - - - - - - - - - Daily Returns 11 Figure 2: Year 2004 Forward/Swap Daily Returns Daily data from Jan 3 -2002 to Nov 3 -2004, Volatility 15.4%, Skewness 0.15, Pearson kurtosis 4.9 100 90 s n o 80 ati 70 Real returns v r Normal e 60 s b 50 o of 40 er 30 b m 20 u N 10 0 % % % % % % % % % % % % % % % % % % % % % % % 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 5. 5. 4. 4. 3. 3. 2. 2. 1. 1. 0. 0. 0. 1. 1. 2. 2. 3. 3. 4. 4. 5. 5. - - - - - - - - - - - Daily returns 12 KURTOSIS was used by Karl Pearson in 1905 Pearson Kurtosis (Normal =3) If more flat-topped I term them platykurtic, if less flat-topped leptokurtic, and if equally flat-topped mesokurtic(cid:2) Fischer Kurtosis = Pearson - 3 (Normal =0) Excess Kurtosis Kurtosis in practice very unstable: Mandelbrot 13 NOT ALL TAIL EVENTS ARE EQUAL Probability : low, but much higher then most people expect. Impact: Typical very low for most real life phenomena. Finance: tail events typically MASSIVE IMPACT ( Black Swan ) 14 FAT-TAILS BIG IMPACT EVENTS: Tulip Mania - Crash 1637 South Sea Bubble - Crash 1720 Panic of 1819 Panic of 1873 Long Depression Florida Real Estate Crash 1925 Crash of 1929 Great Depression 1973 Oil Crisis a quadrupling of oil prices Crash of 1987 Asian financial crisis 1997, Thai currency collapse Argentina Peso Crash 2001 Financial crisis 2008 – 2011 Ruble 2014, Oil 2014… 15 Fat-Tails in Price Data Wesley Clair Mitchell 1874-1948 National Bureau of Economics Research “The Making and Using of Index Numbers” published in 1915" 16 17 Mills rejects the Gaussian hypothesis." " “A distribution may depart widely from the Gaussian type because the influence of one or two extreme price changes.” " " Mills, F. C. (1927): The Behaviour of Prices. New York: National" Bureau of Economic Research, Albany: The Messenger Press. 18 Professor Bronzin 1908, Option Pricing: 19 Professor Bronzin 1908, Option Pricing: 20

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offered a persistent defiance to the. Gaussian law Figure 2: Year 2004 Forward/Swap Daily Returns Sharpe Ratio: Based on Gaussian! options. Hedging Risk/Management: Avoid blow-ups due to fat-tails. Antifragile strategy.
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