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Power law models of stock indices.

Tse, Man Kit.

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Power law models of stock indices.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Power law models of stock indices./
作者:	Tse, Man Kit.
面頁冊數:	210 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.
Contained By:	Dissertation Abstracts International76-03B(E).
標題:	Theoretical physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3642276
ISBN:	9781321290035

Power law models of stock indices.
Tse, Man Kit.

Power law models of stock indices. - 210 p.

Source: Dissertation Abstracts International, Volume: 76-03(E), Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2014.

Viewing the stock market as a self-organized system, Sornette and Johansen introduced physics-based models to study the dynamics of stock market crashes from the perspective of complex systems. This involved modeling stock market Indices using a mathematical power law exhibiting log-periodicity as the system approaches a market crash, which acts like a critical point in a thermodynamic system. In this dissertation, I aim to investigate stock indices to determine whether or not they exhibit log-periodic oscillations, according to the models proposed by Sornette, as they approach a crash. In addition to analyzing stock market crashes in the frequency domain using the discrete Fourier transform and the Lomb-Scargle periodogram, I perform a detailed analysis of the stock market crash models through parameter estimation and model testing. I find that the probability landscapes have a complex topography and that there is very little evidence that these phase transition-based models accurately describe stock market crashes.

ISBN: 9781321290035Subjects--Topical Terms:

2144760
Theoretical physics.

Power law models of stock indices.
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502 $a Thesis (Ph.D.)--State University of New York at Albany, 2014.
520 $a Viewing the stock market as a self-organized system, Sornette and Johansen introduced physics-based models to study the dynamics of stock market crashes from the perspective of complex systems. This involved modeling stock market Indices using a mathematical power law exhibiting log-periodicity as the system approaches a market crash, which acts like a critical point in a thermodynamic system. In this dissertation, I aim to investigate stock indices to determine whether or not they exhibit log-periodic oscillations, according to the models proposed by Sornette, as they approach a crash. In addition to analyzing stock market crashes in the frequency domain using the discrete Fourier transform and the Lomb-Scargle periodogram, I perform a detailed analysis of the stock market crash models through parameter estimation and model testing. I find that the probability landscapes have a complex topography and that there is very little evidence that these phase transition-based models accurately describe stock market crashes.
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