scholarly journals Multiscale behavior of a simple model for stock markets

2005 ◽  
Vol 2005 (2) ◽  
pp. 111-117
Author(s):  
Juan R. Sánchez
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Power Law ◽  
Real World ◽  
Stock Markets ◽  
Time Range ◽  
Scaling Exponents ◽  
World Markets ◽  
Long Time ◽  
With Memory

The multiscale behavior of a recently reported model for stock markets is presented. It has been shown that indexes of real-world markets display absolute returns with memory properties on a long-time range, a phenomenon known as cluster volatility. The multiscale characteristics of an index are studied by analyzing the power-law scaling of the volatility correlations which display nonunique scaling exponents. Here such analysis is done on an artificial time series produced by a simple model for stock markets. After comparison, excellent agreements with the multiscale behavior of real-time series are found.

Turn-on and Charge Build-up Dynamics in Polymer Field Effect Transistors

2005 ◽  
Vol 871 ◽  
Author(s):  
Yohai Roichman ◽  
Nir Tessler
Keyword(s):  
Power Law ◽  
Field Effect ◽  
Field Effect Transistors ◽  
Time Range ◽  
Gate Bias ◽  
Current Dependence ◽  
Turn On ◽  
Long Time ◽  
Source Current ◽  
Short Time

AbstractTurn-on dynamics of polymer field effect transistors were examined experimentally over a wide timescale. We found that the source current dependence on time following switch on of the gate bias exhibits a power law at the short time range, and an exponential decay at the intermediate to long time range. We demonstrate that the transistor dynamic behavior is governed by the channel charge build-up, and can be described accurately by a simple capacitor-resistor distributed line model.

scholarly journals On the long-run interdependence of stock markets: A tale of correlations, autoregressions and decompositions

2002 ◽  
Vol 5 (3) ◽  
pp. 526-548
Author(s):  
Elna Moolman ◽  
Suzanne McCoskey
Keyword(s):  
Time Series ◽  
Latin America ◽  
Robust Estimation ◽  
Stock Markets ◽  
Estimation Procedure ◽  
Var Model ◽  
Long Run ◽  
World Markets ◽  
The Us ◽  
As If

It seems as if national stock markets within certain groups of countries, for example within Europe and Asia, are interdependent. But to what extent are stock markets between these groups interdependent? Is it still possible to diversify among these groups, or have globalization tied world markets together to such an extent that diversification is no longer feasible? In this study we use time series techniques to analyze the interdependence among four of the most important groups of economies, namely Europe, Latin America, Asia and the US. This will show whether it is still possible to diversify between the stock markets of these groups of economies, since stock markets within these groups seem to be interdependent to such an extent that diversification within these groups is no longer possible. On a methodological level, we compare the results of the OLS-VAR with an FM-VAR model, which is a more robust estimation procedure in the presence of non-stationary or cointegrated series.

scholarly journals Fractional virus epidemic model on financial networks

2016 ◽  
Vol 14 (1) ◽  
pp. 1074-1086 ◽  
Author(s):  
Mehmet Ali Balci
Keyword(s):  
Time Series ◽  
Epidemic Model ◽  
Stock Markets ◽  
Global Memory ◽  
Transform Method ◽  
Data Set ◽  
Theoretical Concepts ◽  
Differential Transform ◽  
Long Time ◽  
Fractional Differential

AbstractIn this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.

scholarly journals Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 381
Author(s):  
Raoul Nigmatullin ◽  
Semyon Dorokhin ◽  
Alexander Ivchenko
Keyword(s):  
Time Series ◽  
Power Law ◽  
Communication Systems ◽  
Quantitative Description ◽  
Asymptotic Region ◽  
Specific Response ◽  
Power Law Exponent ◽  
Reduced Parameters ◽  
Long Time ◽  
The Given

In this paper, we focus on the generalization of the Hurst empirical law and suggest a set of reduced parameters for quantitative description of long-time series. These series are usually considered as a specific response of a complex system (economic, geophysical, electromagnetic and other systems), where successive fixations of external factors become impossible. We consider applying generalized Hurst laws to obtain a new set of reduced parameters in data associated with communication systems. We analyze three hypotheses. The first one contains one power-law exponent. The second one incorporates two power-law exponents, which are in many cases complex-conjugated. The third hypothesis has three power-law exponents, two of which are complex-conjugated as well. These hypotheses describe with acceptable accuracy (relative error does not exceed 2%) a wide set of trendless sequences (TLS) associated with radiometric measurements. Generalized Hurst laws operate with R/S curves not only in the asymptotic region, but in the entire domain. The fitting parameters can be used as the reduced parameters for the description of the given data. The paper demonstrates that this general approach can also be applied to other TLS.

VISIBILITY GRAPHS FOR TIME SERIES CONTAINING DIFFERENT COMPONENTS

2011 ◽  
Vol 10 (04) ◽  
pp. 371-379 ◽  
Author(s):  
JINGCHAO QI ◽  
JIANYONG WANG ◽  
JIANBO WANG ◽  
QIN XIAO ◽  
HUIJIE YANG
Keyword(s):  
Time Series ◽  
Power Law ◽  
Real World ◽  
Brownian Motions ◽  
Fractional Brownian Motions ◽  
Visibility Graphs ◽  
Hurst Exponents

We consider the visibility graphs for superpositions of fractional Brownian motions with different Hurst exponents. It is found that the degree distributions obey power-law. The components with lower Hurst exponents dominate the heterogeneity behaviors of the visibility graphs. These findings are helpful for us to understand the characteristics of visibility graphs for real-world time series.

A SIMPLE MODEL FOR STOCKS MARKETS

2002 ◽  
Vol 13 (05) ◽  
pp. 639-644 ◽  
Author(s):  
JUAN R. SANCHEZ
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Time Evolution ◽  
Stock Markets ◽  
Stock Price ◽  
New Model ◽  
Simulated Time ◽  
Simulated Time Series

A new model for stock markets using integer values for each stock price is presented. In contrast with previously reported models, the variables used in the model are not of binary type, but of more general integer type. It is shown how the behavior of the noise and fundamentalists traders can be taken into account simultaneously in the time evolution of each stock price. The simulated time series generated by the model is analyzed in different ways order to compare parameters with those of real markets.

scholarly journals AUG-Segmenter: a user-friendly tool for segmentation of long time series

2010 ◽  
Vol 12 (3) ◽  
pp. 318-328 ◽  
Author(s):  
Abdullah Gedikli ◽  
Hafzullah Aksoy ◽  
N. Erdem Unal
Keyword(s):  
Time Series ◽  
Dynamic Programming ◽  
Linear Regression ◽  
Computer Program ◽  
Branch And Bound ◽  
Real World ◽  
The Third ◽  
Time Series Segmentation ◽  
Long Time ◽  
User Friendly

In this study, three algorithms are presented for time series segmentation. The first algorithm is based on the branch-and-bound approach, the second on the dynamic programming while the third is a modified version of the latter into which the remaining cost concept of the former is introduced. A user-friendly computer program called AUG-Segmenter is developed. Segmentation-by-constant and segmentation-by-linear-regression can be performed by the program. The program is tested on real-world time series of thousands of terms and found useful in performing segmentation satisfactorily and fast.

Effects of the durations of crosscorrelated microtremor records on broadband dispersion measurements using seismic interferometry

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. Q11-Q19 ◽  
Author(s):  
Kosuke Chimoto ◽  
Hiroaki Yamanaka
Keyword(s):  
Time Series ◽  
Power Law ◽  
Signal To Noise Ratio ◽  
Wave Dispersion ◽  
Theoretical Work ◽  
Seismic Interferometry ◽  
Surface Wave Dispersion ◽  
Period Range ◽  
Long Time ◽  
The Time Domain

For ambient noise, a long time series is typically used for measuring surface-wave dispersion in seismic interferometry. It is preferable to measure dispersions with a broad period range. The reliability of such measurements is often studied using the signal-to-noise ratio (S/N) of the crosscorrelation function (CCF). While many studies have revealed that the S/N evolves as the length of a time series increases, the required conditions for such measurements remain unclear. We maximized the period range suitable for dispersion measurements by examining variations in the amplitudes of the signals and noise of CCFs. For these purposes, and to preserve the broadband amplitude information, we do not apply filtering in the frequency domain or signal normalization in the time domain. The preserved signals and the trailing noise levels of the CCFs exhibit different time-varying features that agree with the predictions of theoretical work on amplitudes. Specifically, as the duration of the crosscorrelated time series increases, the amplitude of the signal remains constant while the trailing noise decreases. Moreover, the trailing noise exhibits a power-law dependence on the period. The period range in which the maximum CCF amplitude exceeds the level expected for this power law corresponds to the period range in which dispersion measurements can be made appropriately with frequency-time analysis (FTAN). This approach can be used to quantitatively determine the optimal period range for dispersion measurements. Results obtained with this method indicate that long-duration records used for crosscorrelation provide not only high S/Ns but also broaden the period range in which dispersion measurements can be made.

scholarly journals RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

2019 ◽  
Vol 33 ◽  
pp. 5409-5416 ◽  
Author(s):  
Qingsong Wen ◽  
Jingkun Gao ◽  
Xiaomin Song ◽  
Liang Sun ◽  
Huan Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Abrupt Change ◽  
Decomposition Algorithm ◽  
Real World Data ◽  
Regression Problem ◽  
Sparse Regularization ◽  
Least Absolute Deviations ◽  
Long Time ◽  
Non Local

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

Allometric Control of Human Gait

Fractals ◽  
1998 ◽  
Vol 06 (02) ◽  
pp. 101-108 ◽  
Author(s):  
Bruce J. West ◽  
Lori Griffin
Keyword(s):  
Time Series ◽  
Power Law ◽  
Relative Dispersion ◽  
Human Gait ◽  
Interval Time ◽  
Inverse Power Law ◽  
Time Correlations ◽  
Stride Interval ◽  
Interval Time Series ◽  
Long Time

The stride interval in normal human gait is not strictly constant, but fluctuates from step to step in a random manner. These fluctuations have traditionally been assumed to be uncorrelated random errors with normal statistics. Herein we show that, contrary to thes assumption these fluctuations have long-time correlations. Further, these long-time correlations are interpreted in terms of a scaling in the fluctuations indicating an allometric control process. To establish this result we measured the stride interval of a group of five healthy men and women as they walked for 5 to 15 minutes at their usual pace. From these time series we calculate the relative dispersion, the ratio of the standard deviation to the mean, and show by systematically aggregating the data that the correlation in the stride-interval time series is an inverse power law similar to the allometric relations in biology. The inverse power-law relative dispersion shows that the stride-interval time series scales indicating long-time self-similar correlations extending for hundreds of steps, which is to say that the underlying process is a random fractal. Furthermore, the power-law index is related to the fractal dimension of the time series. To determine if walking is a nonlinear process the stride-interval time series were randomly shuffled and the differences in the fractal dimensions of the surrogate time series from those of the original time series were determined to be statistically significant. This difference indicates the importance of the long-time correlations in walking.

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