Long-range correlations and nonstationarity in the Brazilian stock market

Costa, R.; Vasconcelos, Giovani L.

doi:10.1016/s0378-4371(03)00607-1

Cited by 120 publications

(71 citation statements)

References 17 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results are in agreement with the previous findings in Ref. [15]. The situation looks different for other indices: well defined periods in which H significantly differs from 0.5 can be observed.…”

Section: Dfa For Futures Indicessupporting

confidence: 92%

“…Moreover, market models should be bounded to reproduce the timescale variability of the Hurst exponent, as already pointed out in Ref. [15].…”

Section: Discussionmentioning

confidence: 98%

“…In the present work we investigate the temporal evolution at different scales of the local Hurst exponent [14,15,16,17,18], where by the term "local" we indicate the Hurst exponent calculated at time t over a certain temporal window L that extends backward in time. This concept is very important for non-stationary and multiscale systems such as the stock market.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Multi-scale correlations in different futures markets

Bartolozzi

Mellen²,

Matteo

et al. 2007

Eur. Phys. J. B

View full text Add to dashboard Cite

Abstract. In the present work we investigate the multiscale nature of the correlations for high frequency data (1 minute) in different futures markets over a period of two years, starting on the 1 st of January 2003 and ending on the 31st of December 2004. In particular, by using the concept of local Hurst exponent, we point out how the behaviour of this parameter, usually considered as a benchmark for persistency/antipersistency recognition in time series, is largely time-scale dependent in the market context. These findings are a direct consequence of the intrinsic complexity of a system where trading strategies are scale-adaptive. Moreover, our analysis points out different regimes in the dynamical behaviour of the market indices under consideration.

show abstract

Section: Dfa For Futures Indicessupporting

confidence: 92%

“…Moreover, market models should be bounded to reproduce the timescale variability of the Hurst exponent, as already pointed out in Ref. [15].…”

Section: Discussionmentioning

confidence: 98%

mentioning

confidence: 99%

See 1 more Smart Citation

Multi-scale correlations in different futures markets

Bartolozzi

Mellen²,

Matteo

et al. 2007

Eur. Phys. J. B

View full text Add to dashboard Cite

show abstract

“…We adopt the Detrended Fluctuation Analysis (DFA) methodology as described in Peng et al (1994), Moreira et al (1994) and Vasconcelos (2004) for estimating the Hurst exponent. Costa and Vasconcelos (2003) find the DFA more reliable than the RS analysis for estimation of the Hurst exponent. Various studies have been carried out to estimate the Hurst exponent to determine the existence of fractional Brownian motion and multi fractality.…”

Section: Detrended Fluctuation Analysismentioning

confidence: 84%

Portuguese Stock Market: A Long-Memory Process?

Rege

Martin

2011

Business: Theory and Practice

View full text Add to dashboard Cite

Abstract. This paper gives a basic overview of the various attempts at modelling stochastic processes for stock markets with a specific application to the Portuguese stock market data. Long-memory dependence in the stock prices would completely alter the data generation process and econometric models not considering the long-range dependence would exhibit poor forecasting abilities. The Hurst exponent is used to identify the presence of long-memory or fractal behaviour of the data generation process for the daily returns to ascertain if the process follows a fractional brownian motion. Detrended fluctuation analysis (DFA) using linear and quadratic trends and the Geweke Porter-Hudak methods are applied to detect the presence of long-memory or persistence. We find that the daily returns exhibit a small amount of long memory and that the quadratic trend used in the DFA overestimates the value of the Hurst exponent. These findings are corroborated by the use of the Geweke Porter-Hudak method wherein the Hurst exponent is close to the DFA using the linear trend. Keywords

show abstract

“…From a practitioner viewpoint, many reasons justify the use of the MPRE as a model of the financial dynamics; detailed discussions can be found in [5], [6], [7], [8] and [9]. Here we restrict ourselves to mention the capability the process has to (a) provide a rationale for the trading Pointwise Regularity Exponents and Well-Behaved Residuals in Stock Markets Sergio Bianchi and Alexandre Pantanella mechanism, and (b) replicate the stylized facts observed in finance.…”

Section: Introductionmentioning

confidence: 99%

Pointwise Regularity Exponents and Well-Behaved Residuals in Stock Markets

Bianchi¹,

Pantanella²

2011

IJTEF

View full text Add to dashboard Cite

Abstract-The article deals with a class of stochastic processes, the Multifractional Processes with Random Exponent (MPRE), recently introduced to gain flexibility in modeling many complex phenomena. We claim that MPRE can capture in a very parsimonious way most of the well known financial stylized facts. In particular, we prove that the process unconditional distributions are fat-tailed and high-peaked and show that, as it occurs for asset returns, the empirical autocorrelation functions of the process increments are close to zero whereas significant values are exhibited by squared (or absolute) increments. Furthermore, we provide evidence that the sole knowledge of functional parameter of the MPRE allows to calculate residuals that perform much better than those obtained by other discrete models such as the GARCH family.

show abstract

Long-range correlations and nonstationarity in the Brazilian stock market

Cited by 120 publications

References 17 publications

Multi-scale correlations in different futures markets

Multi-scale correlations in different futures markets

Portuguese Stock Market: A Long-Memory Process?

Pointwise Regularity Exponents and Well-Behaved Residuals in Stock Markets

Contact Info

Product

Resources

About