On defining long-range dependence

1997 ◽  
Vol 34 (4) ◽  
pp. 939-944 ◽  
Author(s):  
C. C. Heyde ◽  
Y. Yang
Keyword(s):  
Long Range ◽  
Second Order ◽  
Long Range Dependence ◽  
Infinite Variance ◽  
Stationary Processes ◽  
Domain Of Applicability

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.

On defining long-range dependence

1997 ◽  
Vol 34 (04) ◽  
pp. 939-944 ◽  
Author(s):  
C. C. Heyde ◽  
Y. Yang
Keyword(s):  
Long Range ◽  
Second Order ◽  
Long Range Dependence ◽  
Infinite Variance ◽  
Stationary Processes ◽  
Domain Of Applicability

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.

The nature of discrete second-order self-similarity

2003 ◽  
Vol 35 (02) ◽  
pp. 395-416 ◽  
Author(s):  
A. Gefferth ◽  
D. Veitch ◽  
I. Maricza ◽  
S. Molnár ◽  
I. Ruzsa
Keyword(s):  
Fixed Points ◽  
Long Range ◽  
Power Laws ◽  
Complete Classification ◽  
Second Order ◽  
General Definition ◽  
Long Range Dependence ◽  
Self Similarity ◽  
New Treatment ◽  
Definition Of

A new treatment of second-order self-similarity and asymptotic self-similarity for stationary discrete time series is given, based on the fixed points of a renormalisation operator with normalisation factors which are not assumed to be power laws. A complete classification of fixed points is provided, consisting of the fractional noise and one other class. A convenient variance time function approach to process characterisation is used to exhibit large explicit families of processes asymptotic to particular fixed points. A natural, general definition of discrete long-range dependence is provided and contrasted with common alternatives. The closely related discrete form of regular variation is defined, its main properties given, and its connection to discrete self-similarity explained. Folkloric results on long-range dependence are proved or disproved rigorously.

MODELS FOR FRACTIONAL RIESZ-BESSEL MOTION AND RELATED PROCESSES

Fractals ◽  
2001 ◽  
Vol 09 (03) ◽  
pp. 329-346 ◽  
Author(s):  
V. V. ANH ◽  
N. N. LEONENKO ◽  
R. MCVINISH
Keyword(s):  
Long Range ◽  
Second Order ◽  
Long Range Dependence ◽  
Stationary Increment

This paper presents some stationary and stationary increment models for fractional Riesz-Bessel motion which permits the study of the effects of long-range dependence and second-order intermittency simultaneously. These effects are important features of data in finance and turbulence.

scholarly journals Hurst Index of Functions of Long-Range-Dependent Markov Chains

2012 ◽  
Vol 49 (02) ◽  
pp. 451-471
Author(s):  
Barlas Oğuz ◽  
Venkat Anantharam
Keyword(s):  
Markov Chain ◽  
Long Range ◽  
Queueing Networks ◽  
Time Distribution ◽  
Return Time ◽  
Indicator Function ◽  
Long Range Dependence ◽  
Infinite Variance ◽  
Hurst Index ◽  
Positive Recurrent

A positive recurrent, aperiodic Markov chain is said to be long-range dependent (LRD) when the indicator function of a particular state is LRD. This happens if and only if the return time distribution for that state has infinite variance. We investigate the question of whether other instantaneous functions of the Markov chain also inherit this property. We provide conditions under which the function has the same degree of long-range dependence as the chain itself. We illustrate our results through three examples in diverse fields: queueing networks, source compression, and finance.

scholarly journals Hurst Index of Functions of Long-Range-Dependent Markov Chains

2012 ◽  
Vol 49 (2) ◽  
pp. 451-471 ◽  
Author(s):  
Barlas Oğuz ◽  
Venkat Anantharam
Keyword(s):  
Markov Chain ◽  
Long Range ◽  
Queueing Networks ◽  
Time Distribution ◽  
Return Time ◽  
Indicator Function ◽  
Long Range Dependence ◽  
Infinite Variance ◽  
Hurst Index ◽  
Positive Recurrent

A positive recurrent, aperiodic Markov chain is said to be long-range dependent (LRD) when the indicator function of a particular state is LRD. This happens if and only if the return time distribution for that state has infinite variance. We investigate the question of whether other instantaneous functions of the Markov chain also inherit this property. We provide conditions under which the function has the same degree of long-range dependence as the chain itself. We illustrate our results through three examples in diverse fields: queueing networks, source compression, and finance.

scholarly journals Estimation of marginal parameters of SUP-OU processes with long range dependence

2016 ◽  
Vol 4 (2) ◽  
pp. 102
Author(s):  
Emanuele Taufer
Keyword(s):  
Long Range ◽  
Asymptotic Theory ◽  
Marginal Distribution ◽  
Real Data ◽  
Function Technique ◽  
Long Range Dependence ◽  
Stationary Processes ◽  
Marginal Distributions ◽  
Non Gaussian ◽  
Basic Features

Superpositions of Ornstein Uhlenbeck processes provide convenient ways to build stationary processes with given marginal distributions and long range dependence. After reviewing some of the basic features, we present several examples of processes with non Gaussian marginal distributions. Estimation of the parameters of the marginal distribution is undertaken by means of a characteristic function technique. We provide the relevant asymptotic theory as well as results of simulations and real data applications.

The nature of discrete second-order self-similarity

2003 ◽  
Vol 35 (2) ◽  
pp. 395-416 ◽  
Author(s):  
A. Gefferth ◽  
D. Veitch ◽  
I. Maricza ◽  
S. Molnár ◽  
I. Ruzsa
Keyword(s):  
Fixed Points ◽  
Long Range ◽  
Power Laws ◽  
Complete Classification ◽  
Second Order ◽  
General Definition ◽  
Long Range Dependence ◽  
Self Similarity ◽  
New Treatment ◽  
Definition Of

A new treatment of second-order self-similarity and asymptotic self-similarity for stationary discrete time series is given, based on the fixed points of a renormalisation operator with normalisation factors which are not assumed to be power laws. A complete classification of fixed points is provided, consisting of the fractional noise and one other class. A convenient variance time function approach to process characterisation is used to exhibit large explicit families of processes asymptotic to particular fixed points. A natural, general definition of discrete long-range dependence is provided and contrasted with common alternatives. The closely related discrete form of regular variation is defined, its main properties given, and its connection to discrete self-similarity explained. Folkloric results on long-range dependence are proved or disproved rigorously.

scholarly journals A long range dependence stable process and an infinite variance branching system

2007 ◽  
Vol 35 (2) ◽  
pp. 500-527 ◽  
Author(s):  
Tomasz Bojdecki ◽  
Luis G. Gorostiza ◽  
Anna Talarczyk
Keyword(s):  
Long Range ◽  
Stable Process ◽  
Long Range Dependence ◽  
Infinite Variance ◽  
Branching System
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