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Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
Extent3 prognoz practical_approach_lppl_model_2012
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Extent3 prognoz practical_approach_lppl_model_2012

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  • 1. About Prognoz Leading Russian developers of Business Intelligence and Performance Management systems • international company that has been working in the IT market since 1991 • joint team of over 1 200 skilled economists, programmers, analysts • 50% market of BI in Russia • Prognoz Platform, 1-st Russian platform in Magic Quadrant of Gartner
  • 2. CONTENTSTechnical architecture Practical approach  Evolution of bubble and risk management About MMP cluster  Monitoring of financial bubbles MMP cluster architecture  The system of bubble recognitionFinancial bubbles Science and experiment Historical bubbles  Financial bubble experiment Definition of financial bubbles  Market microstructure approachTheory of crashes LPPL model Fitting of the model Models selection 3
  • 3. Financial engineering(Stylized facts)……………………………………….…................…Liquidity of the financial market andassets……………………………………………………….…..Agent-based modeling and simulation……………………………………………………………Market microstructure analysis…………………………..………………………………Bubble detection and diagnosis…………………………………………………………… 4
  • 4. Technical info: Installation Site: Perm state university Supercomputer type: Cluster Number of nodes: 3 Number of Cores per node: 12 CPU type: Intel Xeon 5650 (2.66 GHz) RAM per node: 64 Gb OS: Windows Server 2003 5
  • 5. 6Total: 48 services, 72 CPU, 228 Gb RAM
  • 6.  R is statistical and graphical programming environment Appeared in 1993 and designed by There is more than 4300 Ross Ihaka and Robert Gentleman packages that allow to use specialized statistical R is a GNU project techniques, graphical devices, import/export R – a free implementation of the S capabilities, reporting tools, etc. language It runs on a variety of platforms including Windows, Unix and MacOS It contains advanced statistical routines not yet available in other packages 7
  • 7. Commands Database Batch file R fileTask R file Batch fileRunner Batch file R file 8
  • 8. 9
  • 9. 10
  • 10. 11
  • 11. Mr. Greenspan Thefreedictionary.com Charles Kindleberger, MITProfessor J.Barley Rosser, James Madison University 13
  • 12. Authors A.Johansen, O.Ledoit, D.Sornette (JLS)First publicationLarge financial crashes (1997)Famous bookDidier SornetteWhy Stock Markets Crash (2004)𝑡 𝑐 - critical time when bubble crash orchange to another regime 14
  • 13. 𝑚𝑙𝑛 𝑝 𝑡 = 𝐴 + 𝐵(𝑡 𝑐 − 𝑡) 𝑡𝑐 15
  • 14. 𝐶(𝑡 𝑐 − 𝑡) 𝑚 𝑐𝑜𝑠[𝜔 𝑙𝑜𝑔 𝑡 𝑐 − 𝑡 − 𝜑] 16
  • 15. 𝑙𝑛 𝑝 𝑡 = 𝐴 + 𝐵(𝑡 𝑐 − 𝑡) 𝑚 +𝐶(𝑡 𝑐 − 𝑡) 𝑚 𝑐𝑜𝑠[𝜔 𝑙𝑜𝑔 𝑡 𝑐 − 𝑡 − 𝜑] 17
  • 16. 𝑙𝑛[𝑝(𝑡)] 𝑙𝑛 𝑝 𝑡 = 𝐴 + 𝐵(𝑡 𝑐 − 𝑡) 𝑚 +𝐶(𝑡 𝑐 − 𝑡) 𝑚 𝑐𝑜𝑠[𝜔 𝑙𝑜𝑔 𝑡 𝑐 − 𝑡 − 𝜑] m = 0.01 m = 0.3 m = 0.9 m = 1.7 18
  • 17. 𝑙𝑛 𝑝 𝑡 = 𝐴 + 𝐵(𝑡 𝑐 − 𝑡) 𝑚 +𝐶(𝑡 𝑐 − 𝑡) 𝑚 𝑐𝑜𝑠[𝜔 𝑙𝑜𝑔 𝑡 𝑐 − 𝑡 − 𝜑] =3 =7 𝑡𝑐 − 𝑡 𝑡𝑐 − 𝑡 𝑡𝑐 − 𝑡  = 15  = 30 𝑡𝑐 − 𝑡 𝑡𝑐 − 𝑡 19
  • 18. 𝑙𝑛 𝑝 𝑡 = 𝐴 + 𝐵(𝑡 𝑐 − 𝑡) 𝑚 +𝐶(𝑡 𝑐 − 𝑡) 𝑚 𝑐𝑜𝑠[𝜔 𝑙𝑜𝑔 𝑡 𝑐 − 𝑡 − 𝜑] =7  = 9.5 20
  • 19. For each log periodic curve we fixed: 𝑡0 - start time of the bubbleFirst model 𝑡 𝑐 - critical time when bubble crash or change to another regime Second model Sample of 𝑡 𝑐 𝑡 𝑐1 𝑡 𝑐2 21
  • 20. John von Neumann 22
  • 21. • Main filtration (0<m<1, B<0)• Residuals stationarity tests (ADF test, Phillips–Perron test)• Lomb spectral analysis LOMB PERIODOGRAM 150 m 100 P(omega) 50 0 0 10 20 30 40 omega 23
  • 22. Sample of 𝑡𝑐Distribution of 𝑡 𝑐 QuantilesRisk measure 24
  • 23. 25 25
  • 24. D.Fantazzini, P.Geraskin,Everything You Always Wanted to Knowabout Log Periodic Power Laws for Bubble Modellingbut Were Afraid to Ask (2011) 26
  • 25. Timeframe LPPL• Bubble • Long • Large• Anti - bubble • Short • Small • Parameters Type Size 27
  • 26. The Financial Crisis Observatory (FCO) is a scientific platform aimed at testing andquantifying rigorously, in a systematic way and on a large scale the hypothesis thatfinancial markets exhibit a degree of inefficiency and a potential for predictability,especially during regimes when bubbles develop. (http://www.er.ethz.ch/fco/index)Testing two hypotheses:• Hypothesis H1: financial (and other) bubbles can be diagnosed in real-time before they end..• Hypothesis H2: The termination of financial (and other) bubbles can be bracketed using probabilistic forecasts, with a reliability better than chance (which remains to be quantied). D. Sornette, R. Woodard, M. Fedorovsky,S. Reimann, H. Woodard, W.-X. Zhou The Financial Bubble Experiment. First Results (2 November 2009 - 1 May 2010) 28
  • 27.  2 November 2009 – 1 May 2010 [http://www.er.ethz.ch/fco/FBE_report_May_2010]  2 of 4 bubbles detected by model were real bubbles  All of them changed their regimes 12 May 2010 – 1 November 2010 [http://www.er.ethz.ch/fco/fbe_Report_1Nov10_2]  5 of 7 bubbles detected by model were real bubbles  4 of 5 changed their regimes 12 November 2011 – 2 May 2011 [http://www.er.ethz.ch/fco/fbe_20110502_assets_3.pdf]  24 of 27 bubbles detected by model were real bubbles  17 of 24 changed there regime 29
  • 28. NBER Working Group 30
  • 29. Different types of filters at 3 time scales: Hours scale (macro):  Absolute filter  Relative filter Source: Guo-Hua Mu, Wei-Xing Zhou, Wei Chen and J´anos Kert´esz. Order flow dynamics around extreme price changes on an emerging stock market, 2010 Minutes scale (meso):  Filter of minute returns Source: Armand Joulin, Augustin Lefevre, Daniel Grunberg, Jean-Philippe Bouchaud. Stock price jumps: news and volume play a minor role, 2010 Tick scale (micro):  NANEX filter Source: Flash Crash Analysis Continuing Developments http://www.nanex.net/FlashCrashEquities/FlashCrashAnalysis_Equities.html 62 1.855 1.845 61.5 1.835 61 1.825 60.5 price [rub] price, rub. 1.815 60 1.805 59.5 1.795 1.785 59 1.775 58.5 31 12:06:00 12:18:00 12:30:00 12:42:00 12:54:00 14:06:00 14:18:00 14:30:00 14:42:00 14:54:00 16:06:00 16:18:00 16:30:00 16:42:00 16:54:00 17:54:00 18:06:00 18:18:00 18:30:00 11:30:00 11:42:00 11:54:00 13:06:00 13:18:00 13:30:00 13:42:00 13:54:00 15:06:00 15:18:00 15:30:00 15:42:00 15:54:00 17:06:00 17:18:00 17:30:00 17:42:00 11:32:20 11:59:04 12:41:35 12:56:35 13:07:30 13:39:18 14:15:14 14:38:45 14:57:21 15:28:13 16:04:43 16:28:36 17:35:22 18:31:01 11:42:00 11:52:45 12:07:45 12:24:08 12:50:44 12:53:52 13:01:54 13:04:06 13:24:35 13:33:16 13:52:46 14:02:26 14:24:38 14:26:15 14:43:58 14:51:17 15:01:22 15:15:36 15:57:19 16:00:26 16:16:12 16:23:20 16:35:55 17:03:44 17:46:30 18:02:16 time time [ticks]
  • 30. for 16 ticks (61.2 ->61.7) Within 1 second price rose 0.82 % price [rub] 61.5 58.5 59.5 60.5 59 60 61 62 11:32:20 11:42:00 11:52:45 11:59:04 12:07:45 12:24:08 12:41:35 12:50:44 12:53:52 12:56:35 13:01:54 13:04:06 13:07:30 13:24:35 13:33:16 13:39:18 13:52:46 14:02:26 14:15:14 14:24:38 14:26:15 14:38:45 time [ticks] 14:43:58 14:51:17 14:57:21 15:01:22 15:15:36 15:28:13 15:57:19 16:00:26 16:04:43 16:16:12 16:23:20 16:28:36 16:35:55 17:03:44 17:35:2232 17:46:30 18:02:16 18:31:01
  • 31. Statistics: IDENT UP DOWN ALL PMTL 15 36 51 MAGN 31 6 37Stocks analyzed 29 blue chips NOTK OGKC 18 13 18 23 36 36 01.04.2010-30.06.2010; AFLT 9 25 34 RTKM 14 19 33Period 1.09.2010-12.10.2010 MGNT 4 16 20 NLMK 8 12 20Trading days 82 URKA 7 11 18Sample analyzed 20.2 mln. ticks SIBN RASP 6 7 10 8 16 15Trading time 11.30-18.40 MRKH 3 9 12 MSNG 5 7 12 CHMF 3 4 7Shocks found 369 RU14TATN3006 HYDR 3 3 3 2 6 5 TRNFP 3 0 3 IUES 0 2 2 We use a tick dynamics of prices for MTSI 1 1 2 SNGSP 2 0 2 filtering (source: MICEX) ROSN 1 0 1 SNGS 1 0 1 FEES 0 0 0 GAZP 0 0 0 GMKN 0 0 0Total 369 events (13 per stock) LKOH 0 0 0On average 1 shock/7 days per stock SBER03 0 0 0 SBERP03 0 0 0 VTBR 0 0 0 Average 5 7 33 13
  • 32. Science Laboratory of financial modeling and risk management - Prognoz Risk Lab Мagistracy in finance and IT (Master in Finance & IT) in Perm State National Research University mifit.ru Perm Winter School is an annual conference on modeling of financial markets and risk management permwinterschool.ru

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