Corpus ID: 203626722

Stationarity of the detrended time series of S&P500

@article{AriasCalluari2019StationarityOT,
  title={Stationarity of the detrended time series of S\&P500},
  author={Karina Arias-Calluari and M N Najafi and Michael S. Harr'e and Fernando Alonso-marroquin},
  journal={arXiv: Statistical Finance},
  year={2019}
}
Our study presents the analysis of stock market data of S&P500 before and after been detrended. The analysis is based on two types of returns, simple return and log-return respectively. Both of them are non-stationary time series. This means that their statistical distribution change over time. Consequently a detrended process is made to neutralize the non-stationary effects. The detrended process is obtained by decomposing the financial time series into a deterministic trend and random… Expand

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References

SHOWING 1-10 OF 56 REFERENCES
Time-dependent scaling patterns in high frequency financial data
Abstract We measure the influence of different time-scales on the intraday dynamics of financial markets. This is obtained by decomposing financial time series into simple oscillations associatedExpand
Distribution of detrended stock market data
For stock market data, the empirical distribution of the return for stock price and the empirical distribution of the return for stock market index are well known. However, for the detrended dataExpand
Transition from lognormal to χ2-superstatistics for financial time series
Share price returns on different time scales can be well modelled by a superstatistical dynamics. Here we provide an investigation which type of superstatistics is most suitable to properly describeExpand
Statistical properties of the volatility of price fluctuations.
TLDR
The cumulative distribution of the volatility is consistent with a power-law asymptotic behavior, characterized by an exponent mu approximately 3, similar to what is found for the distribution of price changes. Expand
The near-extreme density of intraday log-returns
The extreme event statistics plays a very important role in the theory and practice of time series analysis. The reassembly of classical theoretical results is often undermined by non-stationarityExpand
Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series
We develop a method for the multifractal characterization of nonstationary time series, which is based on a generalization of the detrended fluctuation analysis (DFA). We relate our multifractal DFAExpand
Drift in Transcation-Level Asset Price Models
We study the effect of drift in pure-jump transaction-level models for asset prices in continuous time, driven by point processes. The drift is as-sumed to arise from a nonzero mean in the efficientExpand
On the different forms of returns from moving average buy-sell trading rule in the stock market
Purpose The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving averageExpand
Scaling of the distribution of fluctuations of financial market indices.
TLDR
Estimates of alpha consistent with those describing the distribution of S&P 500 daily returns are found, and for time scales longer than (deltat)x approximately 4 d, the results are consistent with a slow convergence to Gaussian behavior. Expand
Second-order moving average and scaling of stochastic time series
Abstract:Long-range correlation properties of stochastic time series y(i) have been investigated by introducing the function σ2MA = [y(i) - (i)]2, where (i) is the moving average of y(i), defined asExpand
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