Goodness of fit assessment for a fractal model of stock markets

Frezza, Massimiliano

doi:10.1016/j.chaos.2014.05.005

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…Some studies show, in the financial markets, people are not equal to digest and confirm information. It often leads to the biased random walk of stock price, or called Fractional Brown Motion (FBM) [4]. So, we have no reason to believe that markets must be balanced and effective [5].…”

Section: The Biased Random Walk and The Fractal Analysismentioning

confidence: 99%

Fractal Analysis on the Stock Price of C to C Electronic Commerce Enterprise

Chen¹

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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To avoid the status that the stock value of C to C electronic commerce enterprises change radically, we have induced the fractal method to make an empirical analysis on the typical case of listed C to C e-commerce enterprises, and studied the structural factors which behind them and influence the market pricing of them. Based on the fractal analysis of EBAY, our study has found that the stock price volatility of C to C e-commerce enterprise appears as a weak trend with cyclical fluctuations. And furthermore, we have provided a new method for this separation.

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Section: The Biased Random Walk and The Fractal Analysismentioning

confidence: 99%

Fractal Analysis on the Stock Price of C to C Electronic Commerce Enterprise

Chen¹

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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“…Abarbanel, 1994), to financial markets (e.g. Hsieh, 1991;DeCoster, 1992;Cornelis, 2000;Frezza, 2014), and electrical circuits (e.g. Yim et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Chaoticity Properties of Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes

Yilmaz

Ünal

2016

BMSA

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Abstract. Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations.In this work, we have studied the chaoticity properties of FIGARCH (p,d,q) processes by computing mutual information, correlation dimensions, FNNs (False Nearest Neighbour), the largest Lyapunov exponents (LLE) for both the stochastic difference equation and for the financial time series by applying Wolf's algorithm, Kant'z algorithm and Jacobian algorithm. Although Wolf's algorithm produced positive LLE's, Kantz's algorithm and Jacobian algorithm which are subsequently developed methods due to insufficiency of Wolf's algorithm generated negative LLE's constantly.So, as well as experimenting Wolf's methods' inefficiency formerly pointed out by Rosenstein (1993) and more recently Dechert and Gencay (2000), based on Kantz's and Jacobian algorithm's negative LLE outcomes, we concluded that it can be suggested that FIGARCH (p,d,q) is not deterministic chaotic process.

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