Approximating long-memory DNA sequences by short-memory process

2009 ◽  
Vol 388 (17) ◽  
pp. 3475-3485 ◽  
Author(s):  
Jie Gao ◽  
Zhen-yuan Xu ◽  
Li-ting Zhang
Keyword(s):  
Dna Sequences ◽  
Long Memory ◽  
Memory Process ◽  
Short Memory ◽  
Short Memory Process

Does the Forward Discount Represent a Long Memory Process or Short Memory Process with Multiple Changes in the Mean?

2015 ◽  
Vol 235 (6) ◽  
pp. 630-641
Author(s):  
Aidil Rizal Shahrin
Keyword(s):  
Long Memory ◽  
Memory Process ◽  
Extant Literature ◽  
Statistical Procedures ◽  
Long Memory Process ◽  
Monetary Authorities ◽  
Short Memory ◽  
The Mean ◽  
Forward Discount ◽  
Short Memory Process

Summary This study aims to determine whether forward discount represents a long memory or short memory process with multiple changes in the mean. Based on the samples of six currencies from November 3, 1986, to March 6, 1998, using Baek and Pipiras’s (2012, 2014) statistical procedures, our findings suggest that forward discount is a short memory process with multiple changes in the mean rather than long memory. These changes in mean are the result of an intervention by monetary authorities in the forex market. Thus, earlier findings of long memory in forward discount, as reported in the extant literature, are questionable.

On the estimation of short memory components in long memory time series models

2016 ◽  
Vol 20 (4) ◽  
Author(s):  
Richard T. Baillie ◽  
George Kapetanios
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Models ◽  
Memory Process ◽  
Long Memory Process ◽  
Fractional Differencing ◽  
Memory Time ◽  
Short Memory ◽  
Short Memory Process ◽  
Memory Parameter

AbstractA substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory

Long memory in the R$ / US$ exchange rate: A robust analysis

2004 ◽  
Vol 24 (1) ◽  
pp. 109 ◽  
Author(s):  
Márcio Poletti Laurini ◽  
Marcelo Savino Portugal
Keyword(s):  
Exchange Rate ◽  
Long Memory ◽  
Structural Breaks ◽  
Monte Carlo Study ◽  
Markov Switching Model ◽  
Switching Model ◽  
Short Memory ◽  
Variance Parameters ◽  
Short Memory Process ◽  
Unconditional Variance

This article shows that the evidence of long memory for the daily R$ /US$ exchange rate series after the implementation of the Real Plan is not robust when we analyze the existence of structural breaks in this series. We demonstrate that the long memory observed is caused by changes in the structure of variance, captured by a Markov Switching model in all the parameters. A Monte Carlo study shows that the long memory structure can be induced by changes in the unconditional variance parameters, and that the data generating mechanism is a short memory process.

scholarly journals A harmonically weighted filter for cyclical long memory processes

2021 ◽  
Author(s):  
Federico Maddanu
Keyword(s):  
Spectral Density ◽  
Long Memory ◽  
Convergence In Probability ◽  
Simulation Methods ◽  
Integrated Process ◽  
Paleoclimatic Data ◽  
Memory Time ◽  
Long Memory Processes ◽  
Short Memory ◽  
Memory Parameter

AbstractThe estimation of the long memory parameter d is a widely discussed issue in the literature. The harmonically weighted (HW) process was recently introduced for long memory time series with an unbounded spectral density at the origin. In contrast to the most famous fractionally integrated process, the HW approach does not require the estimation of the d parameter, but it may be just as able to capture long memory as the fractionally integrated model, if the sample size is not too large. Our contribution is a generalization of the HW model, denominated the Generalized harmonically weighted (GHW) process, which allows for an unbounded spectral density at $$k \ge 1$$ k ≥ 1 frequencies away from the origin. The convergence in probability of the Whittle estimator is provided for the GHW process, along with a discussion on simulation methods. Fit and forecast performances are evaluated via an empirical application on paleoclimatic data. Our main conclusion is that the above generalization is able to model long memory, as well as its classical competitor, the fractionally differenced Gegenbauer process, does. In addition, the GHW process does not require the estimation of the memory parameter, simplifying the issue of how to disentangle long memory from a (moderately persistent) short memory component. This leads to a clear advantage of our formulation over the fractional long memory approach.

Testing for long memory in the presence of a general trend

2001 ◽  
Vol 38 (04) ◽  
pp. 1033-1054 ◽  
Author(s):  
Liudas Giraitis ◽  
Piotr Kokoszka ◽  
Remigijus Leipus
Keyword(s):  
Long Memory ◽  
Change Point ◽  
General Trend ◽  
Quantitative Description ◽  
Stationary Sequence ◽  
Change Points ◽  
Asymptotic Size ◽  
Short Memory ◽  
The Impact ◽  
Do So

The paper studies the impact of a broadly understood trend, which includes a change point in mean and monotonic trends studied by Bhattacharyaet al.(1983), on the asymptotic behaviour of a class of tests designed to detect long memory in a stationary sequence. Our results pertain to a family of tests which are similar to Lo's (1991) modifiedR/Stest. We show that both long memory and nonstationarity (presence of trend or change points) can lead to rejection of the null hypothesis of short memory, so that further testing is needed to discriminate between long memory and some forms of nonstationarity. We provide quantitative description of trends which do or do not fool theR/S-type long memory tests. We show, in particular, that a shift in mean of a magnitude larger thanN-½, whereNis the sample size, affects the asymptotic size of the tests, whereas smaller shifts do not do so.

A Random walk down the Dhaka Stock Exchange lane

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Md. Kamrul Bari ◽  
Dr. Melita Mehjabeen ◽  
Dr. A. K. Enamul Haque
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Market Efficiency ◽  
Long Memory ◽  
Stock Exchange ◽  
Reliable Estimate ◽  
Memory Process ◽  
Long Memory Process ◽  
Study Results ◽  
Adf Test

Market efficiency has always been a matter of keen interest to the researchers of finance. Since the advancement of this concept, researchers are consistently investigating the market efficiency of different financial markets. Bangladesh, being one of the emerging economies, has also attracted the attention of many researchers. The researchers have investigated the realities regarding the market efficiency of both the stock exchanges of the country. Most of their investigations reveal that the Dhaka Stock Exchange (DSE) and the Chittagong Stock Exchange (CSE) are inefficient. This research, however, did not stop at revisiting market efficiency alone. Whether the return series follows a long-memory process, has also been tested. Besides, non-parametric tests have also been conducted to confirm the results of the parametric tests and vice versa. It generated a more reliable estimate of market efficiency for the period under study. Results of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model confirm that the return series does not follow a long memory process, and any shock in the system will eventually vanish. The findings of other tests (the run test, the Augmented Dickey-Fuller (ADF) test, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, and the Kolmogorov-Smirnov (K-S) test) suggest that the return series of the DSE are time-series stationary, non-normal, and do not follow a random walk. Given these results, we must echo the prior researchers to conclude that the stock market of Bangladesh is not efficient for the period of 2015 to 2020. These findings add new knowledge to the existing knowledge pool about market efficiency and long memory of the stock market of Bangladesh.

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