P. Gopikrishnan, M. Meyer, L. A. N. Amaral, and H. E. Stanley. Inverse cubic law for the distribution of stock price variations. Eur. Phys. J. B, 3:139--40, 1998. Home/Search Document Details and Download
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Empirical Properties of Asset Returns: Stylized Facts and.. - Cont (2001) (2 citations) (Correct)
....stable L evy distributions [41] but compatible with a power law (Pareto) tail with (the same) exponent #(T ) 1 # . These studies seem to validate the power law nature of the distribution of returns, with an exponent around three, using a direct log log regression on the histogram of returns [52]. Note however that these studies do not allow us to pinpoint the exponent with more than a single significant digit. Also, a positive value of # does not imply power law tails [12] but is compatible with any regularly varying tail with exponent 1 # [38] F #t (x) # x## L(x) x (13) where ....
Gopikrishnan P, Meyer M, AmaralLANandStanley H E 1998 Inverse cubic law for the distribution of stock price variations Euro. Phys. J. B 3 139--40
Large Stock Market Price Drawdowns Are Outliers - Johansen, Sornette (2000) (Correct)
....fat tailed distributions. More formally, it has been shown that the tails of the distribution of returns follow approximately a power law P (return x) # C x , with estimates of the tail index falling in the range 2 to 4 (cf. de Vries, 1994; Lux, 1996; Pagan, 1996; Guillaume et al. 1997; Gopikrishnan et al. 1998). This implies that the second and probably the third moment of the distribution are finite. Extrapolating this distribution to infinite values, the fourth and higher moments are predicted to be mathematically infinite. This approximate law seems to hold for returns calculated over time scales ....
....and higher moments are predicted to be mathematically infinite. This approximate law seems to hold for returns calculated over time scales ranging from a few minutes to about three weeks while the distributions are consistent with a slow convergence to Gaussian behavior at larger time scales (Gopikrishnan et al. 1998; Plerou et al. 1999) An alternative description with finite moments of all orders but still with fat tails has been suggested in terms of stretched exponential, also known as sub exponential or Weibull (with exponent less than one) distributions (Laherrere and Sornette, 1998) This one point ....
Gopikrishnan, P., Meyer, M., Amaral, L.A.N. & Stanley, H.E., 1998, Inverse Cubic Law for the Distribution of Stock Price Variations, European Physical Journal B 3, 139-140.
General framework for a portfolio theory with non-Gaussian .. - Malevergne, Sornette (2001) (Correct)
....(see for instance [18] In this framework, increasing return comes with risk and return may remunerate a larger risk. However, the variance (volatility) of portfolio returns provides at best a limited quantification of incurred risks, as the empirical distributions of returns have fat tails [16, 8] and the dependences between assets are only imperfectly accounted for by the covariance matrix [15] Value at Risk [11] and other measures of risks [3, 23, 4, 26] have been developed to account for the larger moves allowed by non Gaussian distributions and nonlinear correlations. In section 2, we ....
Gopikrishnan, P., M. Meyer, L.A. Nunes Amaral and H.E. Stanley, Inverse cubic law for the distribution of stock price variations, European Physical Journal B 3, 139-140 (1998).
Large Stock Market Price Drawdowns Are Outliers - Johansen, Sornette (2000) (Correct)
....fat tailed distributions. More formally, it has been shown that the tails of the distribution of returns follow approximately a power law P (return x) C=x , with estimates of the tail index falling in the range 2 to 4 (cf. de Vries, 1994; Lux, 1996; Pagan, 1996; Guillaume et al. 1997; Gopikrishnan et al. 1998). This implies that the second and probably the third moment of the distribution are nite. Extrapolating this distribution to in nite values, the fourth and higher moments are predicted to be mathematically in nite. This approximate law seems to hold for returns calculated over time scales ....
....fourth and higher moments are predicted to be mathematically in nite. This approximate law seems to hold for returns calculated over time scales ranging from a few minutes to about three weeks while the distributions are consistent with a slow convergence to Gaussian behavior at larger time scales (Gopikrishnan et al. 1998; Plerou et al. 1999) An alternative description with nite moments of all orders but still with fat tails has been suggested in terms of stretched exponential, also known as sub exponential or Weibull (with exponent less than one) distributions (Laherr ere and Sornette, 1998) This one point ....
Gopikrishnan, P., Meyer, M., Amaral, L.A.N. & Stanley, H.E., 1998, Inverse Cubic Law for the Distribution of Stock Price Variations, European Physical Journal B 3, 139-140.
The Nasdaq crash of April 2000: Yet another example of.. - Johansen, Sornette (2000) (Correct)
....loss in about a thousand year. Furthermore, the unconditional volatility on various emergent markets is much higher than on developed equity markets [8] These empirical observations has led to the development of more sophisticated models than the Gaussian, for instance involving power law tails [9, 10, 11, 12, 13] or stretched exponentials [14] as well as models allowing for non stationary of volatility such as ARCH and GARCH models [15] which better reproduces the statistics of the market uctuations. Crashes on the other side are the most extreme events and there are two possibilities to describe them ....
Gopikrishnan P, Meyer M, Amaral LAN, Stanley HE. 1998. Inverse Cubic Law for the Distribution of Stock Price Variations. European Physical Journal B 3: 139-140.
Scale Invariance and Universality of Economic.. - Stanley, Amaral.. (2000) (1 citation) Self-citation (Gopikrishnan Amaral Stanley) (Correct)
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P. Gopikrishnan, M. Meyer, L.A.N. Amaral, H.E. Stanley, Inverse cubic law for the distribution of stock price variations, Eur. Phys. J. B 3 (1998) 139.
Quantifying Fluctuations in Economic Systems By.. - Stanley.. (2000) Self-citation (Gopikrishnan Amaral Stanley) (Correct)
No context found.
P. Gopikrishnan, M. Meyer, L.A.N. Amaral, H.E. Stanley, Inverse cubic law for the distribution of stock price variations, Eur. Phys. J. B 3 (1998) 139--140.
On the Nature of the Stock Market: Simulations and Experiments - Blok (2000) (Correct)
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P. Gopikrishnan, M. Meyer, L. A. N. Amaral, and H. E. Stanley. Inverse cubic law for the distribution of stock price variations. Eur. Phys. J. B, 3:139--40, 1998.
Modeling Economic Randomness: Statistical Mechanics Of Market.. - Cont (1999) (Correct)
No context found.
P. Gopikrishnan, M. Meyer, L.A.N. Amaral and H.E. Stanley, Inverse cubic law for the distribution of stock price variations, Eur. Phys. J. B 3, 139-140 (1998).
Statistical Properties of Financial Time Series - Cont (1999) (Correct)
No context found.
P. Gopikrishnan, M. Meyer, L.A.N. Amaral, H.E. Stanley (1998) \Inverse cubic law for the distribution of stock price variations", European Physical Journal B, 3(2):139-140.
Statistical Properties of Financial Time Series - Cont (1999) (Correct)
No context found.
P. Gopikrishnan, M. Meyer, L.A.N. Amaral, H.E. Stanley (1998) \Inverse cubic law for the distribution of stock price variations", European Physical Journal B, 3(2):139-140.
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