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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-10-04 15:42
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Restricted Document
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10944
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UniSZA
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[1] J. Galbraith, The great crash, 1929, Mariner Books, 1997 [2] D. Sornette, Critical market crashes, Physics Reports 378 (2003) 1–98 [3] C. Kindleberger, Manias, Panics and Crashes: A History of Financial Crises, 4th Edition, Wiley, 2000. [4] Kindleberger, Charles P.: Manias, Panics and Crashes: A History of Financial Crises. New York: Basic Books, 1978. [5] Statman, M. (1998). Investor psychology and market inefficiencies, eqiuty market and valuation methods. The Institute of Chartered Financial Analysts, California [6] R.J.Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005 [7] Lintner, John, 1969, The aggregation of investors’ diverse judgments and preferences in purely competitive security markets, Journal of Financial and Quantitative Analysis 4, 347–400. [8] Miller, Edward, 1977, Risk, Uncertainty and Divergence of Opinion, Journal of Finance 32, 1151-1168. [9] M. Harrison, D. Kreps, Speculative investor behaviour in a stock market with heterogeneous expectations, Quarterly Journal of Economics 92 (1978) 323-336. [10] M. Harrison, D. Kreps, Speculative investor behaviour in a stock market with heterogeneous expectations, Quarterly Journal of Economics 92 (1978) 323-336. [11] J. Chen, H. Hong, J. C. Stein, Breadth of ownership and stock returns, Journal of Financial Economics 66 (2002) 171–205. [12] ] J. Scheinkman, W. Xiong, Overconfidence and speculative bubbles, Journal of Political Economy 111 (2003) 1183–1219. [13] D. Duffie, N. Garleanu, L. H. Pedersen, Securities lending, shorting, and pricing, Journal of Financial Economics 66 (2002) 307–339. [14] Kyle, Albert S., 1985, Continuous Auctions and Insider Trading, Econometrica 53, 1315-1335. [15] Black, Fischer; Noise, The Journal of Finance, Vol. 41, No. 3, Papers and Proceedings of the Forty-Fourth Annual Meeting of the America Finance Association, New York, New York, December 28-30, 1985. (Jul., 1986), pp.529-543. [16] Shleifer, A. Summers, DeLong, J. B., L. H. and Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98, 703 738. [17] N. Barberis, A. Shleifer, R. Vishny, A model of investor sentiment, Journal of Financial Economics 49(3) (1998) 307–34. [18] K. Daniel, D. Hirshleifer, A. Subrahmanyam, Investor psychology and security market under and overreactions, Journal of Finance 53 (1998) 1839–1885. [19] H. Hong, J. D. Kubik, J. C. Stein, Thy eighbour’s portfolio: Word-of-mouth effects in the holdings and trades of money managers, Journal of Finance 60 (2005) 2801–2824. [20] De Bondt, Werner F. M., and Richard I-I. Thaler, 1985, Does the stock market overreact? Journal of Finance 40, 793-805. [21] N. Jegadeesh, S. Titman, Profitability of momentum strategies: An evaluation of alternative explanations, Journal of Finance 54 (2001) 699–720. [22] Abreu, Dilip, and Markus K. Brunnermeier, 2003, Bubbles and crashes, Econometrica 71, 173–204. [23] Brunnermeier, Markus K. and Stefan Nagel, 2004, Hedge Funds and the Technology Bubble, Journal of Finance 59 No.5, 2013-2040. [24] T. Lux, D. Sornette, On rational bubbles and fat tails, Journal of Money, Credit and Baking 34 (3) (2002) 589–610. [25] R. Gurkaynak, Econometric tests of asset price bubbles: Taking stock, Journal of Economic Surveys 22 (1) (2008) 166–186. [26] W. Yan, D. Sornette, P.Embrechts T.Hens, Identification and forecasts of Financial Bubbles (2011). [27] W.-X. Zhou, D. Sornette, Analysis of the real estate market in Las Vegas: Bubble, seasonal patterns, and prediction of the CSW indexes, Physica A 387 (2008) 243–260. [28] D.Sornette, W.X. Zhou, The Us 2000-2002 Market Descent: How Much Longer and Deeper?, Quantitative Finance 2 (6) (2002) 468-481. [29] N.Vandewale, M.Ausloos, P.Boreroux, A .Minguet, Visualizing The Log Periodic Pattern Before Crashes, European Physics Journal B 9 (1999) 355-359. [30] D. Sornette, Dragon-kings, black swans and the prediction of crises, International Journal of Terraspace Science and Engineering 2 (1) (2009) 1–18. [31] Z.-Q. Jiang, W.-X. Zhou, D. Sornette, R. Woodard, K. Bastiaensen, P. Cauwels, Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles, Journal of Economic Behavior and Organization 74 (2010) 149–162. [32] A. Clark, Evidence of log-periodicity in corporate bond spreads, Physica A 338 (2004) 585–595. [33] P. Gnaci´nski, D. Makowiec, Another type of log-periodic oscillations on Polish stock market, Physica A 344 (2004) 322–325. [34] M. Bartolozzi, S. Dro˙zd˙z, D. Leinweber, J. Speth, A. Thomas, Self-similar log periodic structures in Western stock markets from 2000, International Journal of Modern Physics C 16 (2005) 1347–1361. [35] W.-X. Zhou, D. Sornette, A case study of speculative financial bubbles in the South African stock market 2003-2006, Physica A 388 (2009) 869–880. [36] D. Sornette, R. Woodard, M. Fedorovsky, S. Reimann, H. Woodard, W.-X. Zhou, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations (the financial crisis observatory), http://arxiv.org/ abs/0911.0454 (2010). [37] D. Sornette, R. Woodard, M. Fedorovsky, S. Reimann, H. Woodard, W.-X. Zhou, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations volume II (master document), http://arxiv.org/abs/ 1005.5675 (2010). [38] R. Woodard, D. Sornette, M. Fedorovsky, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations volume III (beginning of experiment + post-mortem analysis), http://arxiv.org/abs/1011. 2882 (2011). [39] O. Blanchard, M.Watson, Bubbles, rational expectations and speculative markets, NBER Working Paper 0945, http://papers.ssrn.com/sol3/papers. cfm?abstract_id=226909 (1983). [40] D.Sornette, Discrete Scale Invariance and Complex Dimension, Physics Reports 297 (5) (1998) 239-270. [41] R.Barro, E.Fama, D.Fischel,R.R.AH.Meltzer, L.Telser, Black Monday and The Future Of Financial Markets, in: J.R.W. Kamphuis, R.Kormendi, J.Watson (EDS.), Mid American Institute for Public Policy Research, Richard D Irwan, 1989.
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10944
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10944 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10944 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 628 2024-10-04 15:42 1339x628 1339 5089-01-FH02-FIK-14-00737.jpg UniSZA Private Access An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model Applied Mathematical Sciences Economic bubbles can be defined as transient upward movements of prices above intrinsic value. The Standard Johansen-Ledoit-Sornette (SJLS) model and Generalized Johansen-Ledoit-Sornette (GJLS) models have been developed as flexible tools to detect bubble and forecasts the possible time of crash, tc. These models combines the economic theory of rational expectation bubbles with finite-time singular crash hazard rates, behavioural finance on imitation and herding of investors and traders as well as mathematical statistical physics of bifurcations and phase transitions. It has been employed successfully to a large variety of economic bubbles in many different markets. This study focused on the analytical differences between these two models to point out the best model to be used in forecasting time of crash and bubble detection. By doing so we are able to evaluate the differences and similarities of the methods and results in a practical way. The results appears that the two models are most appropriate to use for identify and predict financial bubbles and crash. But, the GJLS models selected as best model due to the limitation on the outputs of SJLS. The SJLS model only can detect and forecasts the financial bubble, but the GJLS models not only detect the time of crash but estimate the intrinsic value and the crash non-linearity as well. With the estimated intrinsic value, the unexplained problem which is differentiation between exponentially growing fundamental price and an exponentially growing bubble price are overcome. Moreover, the standard JLS model just describes the dynamics of the price during the bubble formation but the GJLS model can determines the dynamics of crash after the bubble by specifying how the price evolves towards the intrinsic value during crash. 8 45 2257-2267 [1] J. Galbraith, The great crash, 1929, Mariner Books, 1997 [2] D. Sornette, Critical market crashes, Physics Reports 378 (2003) 1–98 [3] C. Kindleberger, Manias, Panics and Crashes: A History of Financial Crises, 4th Edition, Wiley, 2000. [4] Kindleberger, Charles P.: Manias, Panics and Crashes: A History of Financial Crises. New York: Basic Books, 1978. [5] Statman, M. (1998). Investor psychology and market inefficiencies, eqiuty market and valuation methods. The Institute of Chartered Financial Analysts, California [6] R.J.Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005 [7] Lintner, John, 1969, The aggregation of investors’ diverse judgments and preferences in purely competitive security markets, Journal of Financial and Quantitative Analysis 4, 347–400. [8] Miller, Edward, 1977, Risk, Uncertainty and Divergence of Opinion, Journal of Finance 32, 1151-1168. [9] M. Harrison, D. Kreps, Speculative investor behaviour in a stock market with heterogeneous expectations, Quarterly Journal of Economics 92 (1978) 323-336. [10] M. Harrison, D. Kreps, Speculative investor behaviour in a stock market with heterogeneous expectations, Quarterly Journal of Economics 92 (1978) 323-336. [11] J. Chen, H. Hong, J. C. Stein, Breadth of ownership and stock returns, Journal of Financial Economics 66 (2002) 171–205. [12] ] J. Scheinkman, W. Xiong, Overconfidence and speculative bubbles, Journal of Political Economy 111 (2003) 1183–1219. [13] D. Duffie, N. Garleanu, L. H. Pedersen, Securities lending, shorting, and pricing, Journal of Financial Economics 66 (2002) 307–339. [14] Kyle, Albert S., 1985, Continuous Auctions and Insider Trading, Econometrica 53, 1315-1335. [15] Black, Fischer; Noise, The Journal of Finance, Vol. 41, No. 3, Papers and Proceedings of the Forty-Fourth Annual Meeting of the America Finance Association, New York, New York, December 28-30, 1985. (Jul., 1986), pp.529-543. [16] Shleifer, A. Summers, DeLong, J. B., L. H. and Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98, 703 738. [17] N. Barberis, A. Shleifer, R. Vishny, A model of investor sentiment, Journal of Financial Economics 49(3) (1998) 307–34. [18] K. Daniel, D. Hirshleifer, A. Subrahmanyam, Investor psychology and security market under and overreactions, Journal of Finance 53 (1998) 1839–1885. [19] H. Hong, J. D. Kubik, J. C. Stein, Thy eighbour’s portfolio: Word-of-mouth effects in the holdings and trades of money managers, Journal of Finance 60 (2005) 2801–2824. [20] De Bondt, Werner F. M., and Richard I-I. Thaler, 1985, Does the stock market overreact? Journal of Finance 40, 793-805. [21] N. Jegadeesh, S. Titman, Profitability of momentum strategies: An evaluation of alternative explanations, Journal of Finance 54 (2001) 699–720. [22] Abreu, Dilip, and Markus K. Brunnermeier, 2003, Bubbles and crashes, Econometrica 71, 173–204. [23] Brunnermeier, Markus K. and Stefan Nagel, 2004, Hedge Funds and the Technology Bubble, Journal of Finance 59 No.5, 2013-2040. [24] T. Lux, D. Sornette, On rational bubbles and fat tails, Journal of Money, Credit and Baking 34 (3) (2002) 589–610. [25] R. Gurkaynak, Econometric tests of asset price bubbles: Taking stock, Journal of Economic Surveys 22 (1) (2008) 166–186. [26] W. Yan, D. Sornette, P.Embrechts T.Hens, Identification and forecasts of Financial Bubbles (2011). [27] W.-X. Zhou, D. Sornette, Analysis of the real estate market in Las Vegas: Bubble, seasonal patterns, and prediction of the CSW indexes, Physica A 387 (2008) 243–260. [28] D.Sornette, W.X. Zhou, The Us 2000-2002 Market Descent: How Much Longer and Deeper?, Quantitative Finance 2 (6) (2002) 468-481. [29] N.Vandewale, M.Ausloos, P.Boreroux, A .Minguet, Visualizing The Log Periodic Pattern Before Crashes, European Physics Journal B 9 (1999) 355-359. [30] D. Sornette, Dragon-kings, black swans and the prediction of crises, International Journal of Terraspace Science and Engineering 2 (1) (2009) 1–18. [31] Z.-Q. Jiang, W.-X. Zhou, D. Sornette, R. Woodard, K. Bastiaensen, P. Cauwels, Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles, Journal of Economic Behavior and Organization 74 (2010) 149–162. [32] A. Clark, Evidence of log-periodicity in corporate bond spreads, Physica A 338 (2004) 585–595. [33] P. Gnaci´nski, D. Makowiec, Another type of log-periodic oscillations on Polish stock market, Physica A 344 (2004) 322–325. [34] M. Bartolozzi, S. Dro˙zd˙z, D. Leinweber, J. Speth, A. Thomas, Self-similar log periodic structures in Western stock markets from 2000, International Journal of Modern Physics C 16 (2005) 1347–1361. [35] W.-X. Zhou, D. Sornette, A case study of speculative financial bubbles in the South African stock market 2003-2006, Physica A 388 (2009) 869–880. [36] D. Sornette, R. Woodard, M. Fedorovsky, S. Reimann, H. Woodard, W.-X. Zhou, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations (the financial crisis observatory), http://arxiv.org/ abs/0911.0454 (2010). [37] D. Sornette, R. Woodard, M. Fedorovsky, S. Reimann, H. Woodard, W.-X. Zhou, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations volume II (master document), http://arxiv.org/abs/ 1005.5675 (2010). [38] R. Woodard, D. Sornette, M. Fedorovsky, The financial bubble experiment: Advanced diagnostics and forecasts of bubble terminations volume III (beginning of experiment + post-mortem analysis), http://arxiv.org/abs/1011. 2882 (2011). [39] O. Blanchard, M.Watson, Bubbles, rational expectations and speculative markets, NBER Working Paper 0945, http://papers.ssrn.com/sol3/papers. cfm?abstract_id=226909 (1983). [40] D.Sornette, Discrete Scale Invariance and Complex Dimension, Physics Reports 297 (5) (1998) 239-270. [41] R.Barro, E.Fama, D.Fischel,R.R.AH.Meltzer, L.Telser, Black Monday and The Future Of Financial Markets, in: J.R.W. Kamphuis, R.Kormendi, J.Watson (EDS.), Mid American Institute for Public Policy Research, Richard D Irwan, 1989.
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An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| summary |
Economic bubbles can be defined as transient upward movements of prices above intrinsic value. The Standard Johansen-Ledoit-Sornette (SJLS) model and Generalized Johansen-Ledoit-Sornette (GJLS) models have been developed as flexible tools to detect bubble and forecasts the possible time of crash, tc. These models combines the economic theory of rational expectation bubbles with finite-time singular crash hazard rates, behavioural finance on imitation and herding of investors and traders as well as mathematical statistical physics of bifurcations and phase transitions. It has been employed successfully to a large variety of economic bubbles in many different markets. This study focused on the analytical differences between these two models to point out the best model to be used in forecasting time of crash and bubble detection. By doing so we are able to evaluate the differences and similarities of the methods and results in a practical way. The results appears that the two models are most appropriate to use for identify and predict financial bubbles and crash. But, the GJLS models selected as best model due to the limitation on the outputs of SJLS. The SJLS model only can detect and forecasts the financial bubble, but the GJLS models not only detect the time of crash but estimate the intrinsic value and the crash non-linearity as well. With the estimated intrinsic value, the unexplained problem which is differentiation between exponentially growing fundamental price and an exponentially growing bubble price are overcome. Moreover, the standard JLS model just describes the dynamics of the price during the bubble formation but the GJLS model can determines the dynamics of crash after the bubble by specifying how the price evolves towards the intrinsic value during crash.
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| title |
An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| title_full |
An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| title_fullStr |
An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| title_full_unstemmed |
An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| title_short |
An analytical comparison between Standard Johansen-Ledoit-Sornette (SJLS) Model and Generalized Johansen-Ledoit-Sornette (GJLS) model
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| title_sort |
analytical comparison between standard johansen-ledoit-sornette (sjls) model and generalized johansen-ledoit-sornette (gjls) model
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