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Journal articles on the topic 'Long-range dependence'

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Published: 4 June 2021
Last updated: 21 February 2023

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1

Samorodnitsky, Gennady. "Long Range Dependence." Foundations and Trends® in Stochastic Systems 1, no. 3 (2006): 163–257. http://dx.doi.org/10.1561/0900000004.

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2

Beran, Jan. "Long‐range dependence." WIREs Computational Statistics 2, no. 1 (January 2010): 26–35. http://dx.doi.org/10.1002/wics.52.

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3

Joshi, Prashant. "Analyzing Long Range Dependence in Stock Markets of India." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 345–48. http://dx.doi.org/10.15373/2249555x/august2014/87.

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4

Dempster, Arthur P., and Jing-Shiang Hwang. "[Statistical Methods for Data with Long-Range Dependence]: Comment: Short- Range Consequences of Long-Range Dependence." Statistical Science 7, no. 4 (November 1992): 416–20. http://dx.doi.org/10.1214/ss/1177011123.

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5

Heyde, C. C., and Y. Yang. "On defining long-range dependence." Journal of Applied Probability 34, no. 4 (December 1997): 939–44. http://dx.doi.org/10.2307/3215008.

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Long-range dependence has usually been defined in terms of covariance properties relevant only to second-order stationary processes. Here we provide new definitions, almost equivalent to the original ones in that domain of applicability, which are useful for processes which may not be second-order stationary, or indeed have infinite variances. The ready applicability of this formulation for categorizing the behaviour for various infinite variance models is shown.
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6

Heyde, C. C., and Y. Yang. "On defining long-range dependence." Journal of Applied Probability 34, no. 04 (December 1997): 939–44. http://dx.doi.org/10.1017/s0021900200101639.

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Long-range dependence has usually been defined in terms of covariance properties relevant only to second-order stationary processes. Here we provide new definitions, almost equivalent to the original ones in that domain of applicability, which are useful for processes which may not be second-order stationary, or indeed have infinite variances. The ready applicability of this formulation for categorizing the behaviour for various infinite variance models is shown.
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7

Surgailis, Donatas. "Long-range dependence and Appell rank." Annals of Probability 28, no. 1 (January 2000): 478–97. http://dx.doi.org/10.1214/aop/1019160127.

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8

Ma, Chunsheng. "Correlation models with long-range dependence." Journal of Applied Probability 39, no. 2 (June 2002): 370–82. http://dx.doi.org/10.1239/jap/1025131432.

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This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.
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9

Heyde, C. C. "On modes of long-range dependence." Journal of Applied Probability 39, no. 4 (December 2002): 882–88. http://dx.doi.org/10.1239/jap/1037816026.

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This paper aims at enhancing the understanding of long-range dependence (LRD) by focusing on mechanisms for generating this dependence, namely persistence of signs and/or persistence of magnitudes beyond what can be expected under weak dependence. These concepts are illustrated through a discussion of fractional Brownian noise of index H ∈ (0,1) and it is shown that LRD in signs holds if and only if ½ < H < 1 and LRD in magnitudes if and only if ¾ ≤ H < 1. An application to discrimination between two risky asset finance models, the FATGBM model of Heyde and the multifractal model of Mandelbrot, is given to illustrate the use of the ideas.
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10

Csorgo, Sandor, and Jan Mielniczuk. "Density Estimation Under Long-Range Dependence." Annals of Statistics 23, no. 3 (June 1995): 990–99. http://dx.doi.org/10.1214/aos/1176324632.

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11

Heyde, C. C. "On modes of long-range dependence." Journal of Applied Probability 39, no. 04 (December 2002): 882–88. http://dx.doi.org/10.1017/s0021900200022117.

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This paper aims at enhancing the understanding of long-range dependence (LRD) by focusing on mechanisms for generating this dependence, namely persistence of signs and/or persistence of magnitudes beyond what can be expected under weak dependence. These concepts are illustrated through a discussion of fractional Brownian noise of index H ∈ (0,1) and it is shown that LRD in signs holds if and only if ½ &lt; H &lt; 1 and LRD in magnitudes if and only if ¾ ≤ H &lt; 1. An application to discrimination between two risky asset finance models, the FATGBM model of Heyde and the multifractal model of Mandelbrot, is given to illustrate the use of the ideas.
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12

Ma, Chunsheng. "Correlation models with long-range dependence." Journal of Applied Probability 39, no. 02 (June 2002): 370–82. http://dx.doi.org/10.1017/s0021900200022579.

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This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.
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13

Pirino, Davide. "Jump detection and long range dependence." Physica A: Statistical Mechanics and its Applications 388, no. 7 (April 2009): 1150–56. http://dx.doi.org/10.1016/j.physa.2008.12.035.

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14

Cajueiro, Daniel O., and Benjamin M. Tabak. "Long-range dependence and market structure." Chaos, Solitons & Fractals 31, no. 4 (February 2007): 995–1000. http://dx.doi.org/10.1016/j.chaos.2005.10.077.

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15

Hall, Peter, and Jeffrey D. Hart. "Nonparametric regression with long-range dependence." Stochastic Processes and their Applications 36, no. 2 (December 1990): 339–51. http://dx.doi.org/10.1016/0304-4149(90)90100-7.

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16

Butka, Argjir, Llukan Puka, and Ilir Palla. "Bootstrap Testing for Long Range Dependence." International Journal of Mathematics Trends and Technology 8, no. 3 (April 25, 2014): 164–72. http://dx.doi.org/10.14445/22315373/ijmtt-v8p522.

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17

Vera-Valdés, J. Eduardo. "Nonfractional Long-Range Dependence: Long Memory, Antipersistence, and Aggregation." Econometrics 9, no. 4 (October 19, 2021): 39. http://dx.doi.org/10.3390/econometrics9040039.

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This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent phenomenon is not present for the cross-sectionally aggregated process. We proved that this has implications for estimators of long-range dependence in the frequency domain, which will be misspecified for nonfractional long-range-dependent processes with negative degrees of persistence. As an application, we showed how we can approximate a fractionally differenced process using theoretically-motivated cross-sectional aggregated long-range-dependent processes. An example with temperature data showed that our framework provides a better fit to the data than the fractional difference operator.
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18

Nüßgen, Ines, and Alexander Schnurr. "Ordinal Pattern Dependence in the Context of Long-Range Dependence." Entropy 23, no. 6 (May 26, 2021): 670. http://dx.doi.org/10.3390/e23060670.

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Ordinal pattern dependence is a multivariate dependence measure based on the co-movement of two time series. In strong connection to ordinal time series analysis, the ordinal information is taken into account to derive robust results on the dependence between the two processes. This article deals with ordinal pattern dependence for a long-range dependent time series including mixed cases of short- and long-range dependence. We investigate the limit distributions for estimators of ordinal pattern dependence. In doing so, we point out the differences that arise for the underlying time series having different dependence structures. Depending on these assumptions, central and non-central limit theorems are proven. The limit distributions for the latter ones can be included in the class of multivariate Rosenblatt processes. Finally, a simulation study is provided to illustrate our theoretical findings.
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19

Ray, Bonnie K., and Ruey S. Tsay. "Long-Range Dependence in Daily Stock Volatilities." Journal of Business & Economic Statistics 18, no. 2 (April 2000): 254. http://dx.doi.org/10.2307/1392562.

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20

Chakrabarty, Arijit, Rajat Subhra Hazra, and Deepayan Sarkar. "From random matrices to long range dependence." Random Matrices: Theory and Applications 05, no. 02 (April 2016): 1650008. http://dx.doi.org/10.1142/s2010326316500088.

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Random matrices whose entries come from a stationary Gaussian process are studied. The limiting behavior of the eigenvalues as the size of the matrix goes to infinity is the main subject of interest in this work. It is shown that the limiting spectral distribution is determined by the absolutely continuous component of the spectral measure of the stationary process. This is similar to the situation where the entries of the matrix are i.i.d. On the other hand, the discrete component contributes to the limiting behavior of the eigenvalues after a different scaling. Therefore, this helps to define a boundary between short and long range dependence of a stationary Gaussian process in the context of random matrices.
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21

MA, CHUNSHENG. "VECTOR RANDOM FIELDS WITH LONG-RANGE DEPENDENCE." Fractals 19, no. 02 (June 2011): 249–58. http://dx.doi.org/10.1142/s0218348x11005312.

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It is well-known that the crucial ingredient for a vector Gaussian random function is its covariance matrix, where a diagonal entry termed a direct covariance is simply the covariance function of a component but it seems no simple interpretation for an off-diagonal entry termed a cross covariance, which often make it hard to specify. In this paper we employ three approaches to derive vector random functions in space and/or time, which are not homogeneous (stationary) in general but contain the stationary case as a special case, and have long-range or short-range dependence.
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22

Vesilo, R. A. "Long-Range Dependence of Markov Renewal Processes." Australian New Zealand Journal of Statistics 46, no. 1 (March 2004): 155–71. http://dx.doi.org/10.1111/j.1467-842x.2004.00321.x.

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23

Ray, Bonnie K., and Ruey S. Tsay. "Long-range Dependence in Daily Stock Volatilities." Journal of Business & Economic Statistics 18, no. 2 (April 2000): 254–62. http://dx.doi.org/10.1080/07350015.2000.10524867.

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24

Maria Caporale, Guglielmo, and Luis A. Gil-Alana. "Long range dependence in daily stock returns." Applied Financial Economics 14, no. 6 (March 15, 2004): 375–83. http://dx.doi.org/10.1080/09603100410001673603.

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25

Christodoulou-Volos, Christos, and Fotios M. Siokis. "Long range dependence in stock market returns." Applied Financial Economics 16, no. 18 (December 2006): 1331–38. http://dx.doi.org/10.1080/09603100600829519.

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26

Vašata, Daniel. "On long-range dependence of random measures." Advances in Applied Probability 48, no. 4 (December 2016): 1235–55. http://dx.doi.org/10.1017/apr.2016.72.

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AbstractThis paper deals with long-range dependence of random measures on ℝd. By examples, it is demonstrated that one must be careful in order to define it consistently. Therefore, we define long-range dependence by a rather specific second-order condition and provide an equivalent formulation involving the asymptotic behaviour of the Bartlett spectrum near the origin. Then it is shown that the defining condition may be formulated less strictly when the additional isotropy assumption holds. Finally, we present an example of a long-range dependent random measure based on the 0-level excursion set of a Gaussian random field for which the corresponding spectral density and its asymptotics are explicitly derived.
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27

Liu, Zhen. "Long range dependence and heavy tail distributions." Performance Evaluation 61, no. 2-3 (July 2005): 91–93. http://dx.doi.org/10.1016/j.peva.2005.04.001.

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28

Abry, Patrice, Darryl Veitch, and Patrick Flandrin. "Long-range Dependence: Revisiting Aggregation with Wavelets." Journal of Time Series Analysis 19, no. 3 (May 1998): 253–66. http://dx.doi.org/10.1111/1467-9892.00090.

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29

Gupta, Sat N., Joel Goldstein, and Chang Yu. "Time Domain Estimation of Long Range Dependence." Journal of Information and Optimization Sciences 18, no. 3 (September 1997): 493–98. http://dx.doi.org/10.1080/02522667.1997.10699355.

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30

Robinson, P. M., and F. J. Hidalgo. "Time series regression with long-range dependence." Annals of Statistics 25, no. 1 (February 1997): 77–104. http://dx.doi.org/10.1214/aos/1034276622.

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31

Robinson, P. M. "Gaussian Semiparametric Estimation of Long Range Dependence." Annals of Statistics 23, no. 5 (October 1995): 1630–61. http://dx.doi.org/10.1214/aos/1176324317.

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32

Willinger, Walter, Murad S. Taqqu, and Vadim Teverovsky. "Stock market prices and long-range dependence." Finance and Stochastics 3, no. 1 (January 1, 1999): 1–13. http://dx.doi.org/10.1007/s007800050049.

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33

Liseo, B., D. Marinucci, and L. Petrella. "Bayesian semiparametric inference on long-range dependence." Biometrika 88, no. 4 (December 1, 2001): 1089–104. http://dx.doi.org/10.1093/biomet/88.4.1089.

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34

Frías, M. P., F. J. Alonso, M. D. Ruiz-Medina, and J. M. Angulo. "Semiparametric estimation of spatial long-range dependence." Journal of Statistical Planning and Inference 138, no. 5 (May 2008): 1479–95. http://dx.doi.org/10.1016/j.jspi.2007.07.005.

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35

Iliopoulou, Theano, Simon Michael Papalexiou, Yannis Markonis, and Demetris Koutsoyiannis. "Revisiting long-range dependence in annual precipitation." Journal of Hydrology 556 (January 2018): 891–900. http://dx.doi.org/10.1016/j.jhydrol.2016.04.015.

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36

Park, Cheolwoo, Félix Hernández-Campos, Long Le, J. S. Marron, Juhyun Park, Vladas Pipiras, F. D. Smith, Richard L. Smith, Michele Trovero, and Zhengyuan Zhu. "Long-range dependence analysis of Internet traffic." Journal of Applied Statistics 38, no. 7 (September 30, 2010): 1407–33. http://dx.doi.org/10.1080/02664763.2010.505949.

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37

Andrade, P., and L. Rifo. "Long-range dependence and approximate Bayesian computation." Communications in Statistics - Simulation and Computation 46, no. 2 (November 4, 2016): 1219–37. http://dx.doi.org/10.1080/03610918.2014.995816.

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38

Wang, Ling, and Hoi Ying Wong. "Time-consistent longevity hedging with long-range dependence." Insurance: Mathematics and Economics 99 (July 2021): 25–41. http://dx.doi.org/10.1016/j.insmatheco.2021.03.004.

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39

Künsch, H. "Discrimination between monotonic trends and long-range dependence." Journal of Applied Probability 23, no. 4 (December 1986): 1025–30. http://dx.doi.org/10.2307/3214476.

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We show that the periodogram behaves differently for a weakly dependent process with a small monotonic trend and a stationary strongly dependent process. In the former case it has a non-central -distribution with noncentrality parameter tending to 0 uniformly outside intervals . In the latter case it has λα −1 times a central -distribution, 0 < α < 1.
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40

Kulik, Rafal, and Evgeny Spodarev. "Long range dependence of heavy-tailed random functions." Journal of Applied Probability 58, no. 3 (September 2021): 569–93. http://dx.doi.org/10.1017/jpr.2020.107.

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AbstractWe introduce a definition of long range dependence of random processes and fields on an (unbounded) index space $T\subseteq \mathbb{R}^d$ in terms of integrability of the covariance of indicators that a random function exceeds any given level. This definition is specifically designed to cover the case of random functions with infinite variance. We show the value of this new definition and its connection to limit theorems via some examples including subordinated Gaussian as well as random volatility fields and time series.
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41

Hidalgo, Javier. "Nonparametric Test for Causality with Long-range Dependence." Econometrica 68, no. 6 (November 2000): 1465–90. http://dx.doi.org/10.1111/1468-0262.00168.

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42

INOUE, Akihiko, and Vo V. ANH. "Prediction of fractional processes with long-range dependence." Hokkaido Mathematical Journal 41, no. 2 (June 2012): 157–83. http://dx.doi.org/10.14492/hokmj/1340714411.

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43

Achard, Sophie, and Irène Gannaz. "Multivariate Wavelet Whittle Estimation in Long-range Dependence." Journal of Time Series Analysis 37, no. 4 (November 10, 2015): 476–512. http://dx.doi.org/10.1111/jtsa.12170.

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44

Iacone, Fabrizio, and Štěpána Lazarová. "Semiparametric Detection of Changes in Long Range Dependence." Journal of Time Series Analysis 40, no. 5 (February 5, 2019): 693–706. http://dx.doi.org/10.1111/jtsa.12448.

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45

Kechagias, Stefanos, and Vladas Pipiras. "Modeling bivariate long‐range dependence with general phase." Journal of Time Series Analysis 41, no. 2 (December 15, 2019): 268–92. http://dx.doi.org/10.1111/jtsa.12504.

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46

Cox, D. R. "LONG-RANGE DEPENDENCE, NON-LINEARITY AND TIME IRREVERSIBILITY." Journal of Time Series Analysis 12, no. 4 (July 1991): 329–35. http://dx.doi.org/10.1111/j.1467-9892.1991.tb00087.x.

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47

TAQQU, MURAD S., VADIM TEVEROVSKY, and WALTER WILLINGER. "ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY." Fractals 03, no. 04 (December 1995): 785–98. http://dx.doi.org/10.1142/s0218348x95000692.

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Various methods for estimating the self-similarity parameter and/or the intensity of long-range dependence in a time series are available. Some are more reliable than others. To discover the ones that work best, we apply the different methods to simulated sequences of fractional Gaussian noise and fractional ARIMA (0, d, 0). We also provide here a theoretical justification for the method of residuals of regression.
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48

Dawson, P. J. "Do wheat futures returns exhibit long-range dependence?" Agricultural Economics 42, no. 1 (September 6, 2010): 111–20. http://dx.doi.org/10.1111/j.1574-0862.2010.00480.x.

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49

Künsch, H. "Discrimination between monotonic trends and long-range dependence." Journal of Applied Probability 23, no. 04 (December 1986): 1025–30. http://dx.doi.org/10.1017/s0021900200115955.

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We show that the periodogram behaves differently for a weakly dependent process with a small monotonic trend and a stationary strongly dependent process. In the former case it has a non-central-distribution with noncentrality parameter tending to 0 uniformly outside intervals. In the latter case it has λα−1times a central-distribution, 0 &lt;α&lt; 1.
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50

Künsch, H. "Discrimination between monotonic trends and long-range dependence." Journal of Applied Probability 23, no. 04 (December 1986): 1025–30. http://dx.doi.org/10.1017/s0021900200118819.

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Abstract:
We show that the periodogram behaves differently for a weakly dependent process with a small monotonic trend and a stationary strongly dependent process. In the former case it has a non-central -distribution with noncentrality parameter tending to 0 uniformly outside intervals . In the latter case it has λ α −1 times a central -distribution, 0 &lt; α &lt; 1.
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