<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amin Guerouah</style></author><author><style face="normal" font="default" size="100%">Bassant Elkalzah</style></author><author><style face="normal" font="default" size="100%">Halim Zeghdoudi</style></author><author><style face="normal" font="default" size="100%">Majdah Mohammed Badr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new Xlindley ARMA model for positive time series</style></title><secondary-title><style face="normal" font="default" size="100%">AIMS Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aimspress.com/journal/Math</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">39</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper proposes a new observation-driven model for continuous positive-valued time series, called the new XLindley autoregressive moving average (NXL–ARMA) model. The model is built by combining an ARMA-type recursion for the conditional mean with the new XLindley distribution as the conditional law of the observations. This framework is suitable for variables such as trading volume, realized volatility, durations, waiting times, precipitation amounts, energy consumption, and other positive measurements, where the data often show persistence, skewness, and variability that changes with the level of the series.&lt;br&gt;The proposed model preserves positivity by construction and remains relatively simple to handle from both theoretical and computational points of view. We derive the conditional moments in closed form and show that the conditional variance is proportional to the square of the conditional mean. This quadratic variance–mean relationship gives a natural motivation for using likelihood and quasi-likelihood estimation methods, including Gaussian, exponential, and geometric quasi-maximum likelihood approaches. We also establish conditions for strict stationarity and ergodicity, and prove the strong consistency of the proposed estimators under suitable regularity assumptions. Monte Carlo simulations are used to study the finite-sample behavior of the estimators, while empirical applications with benchmark comparisons illustrate the practical usefulness of the NXL–ARMA model for positivevalued&lt;br&gt;financial time series.
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</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amin Guerouah</style></author><author><style face="normal" font="default" size="100%">Halim Zeghdoudi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bifurcation-aware optimal control of discrete SIRS epidemic models: suppressing period-doubling and multi-wave outbreaks</style></title><secondary-title><style face="normal" font="default" size="100%">BIOMATH, International Journal on Mathematicul Methods and Models in Biosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://biomath.math.bas.bg/biomath/index.php/biomath</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Houssem Eddine Khochemane</style></author><author><style face="normal" font="default" size="100%">Lamine Bouzettouta</style></author><author><style face="normal" font="default" size="100%">Amin Guerouah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exponential decay and well-posedness for a one dimensional porous-elastic system with distributed delay</style></title><secondary-title><style face="normal" font="default" size="100%">Applicable Analysis (Taylor &amp; Francis)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1080/00036811.2019.1703958</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">14</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Houssem Eddine Khochemane</style></author><author><style face="normal" font="default" size="100%">ABDELOUAHEB ARDJOUNI</style></author><author><style face="normal" font="default" size="100%">Amin Guerouah</style></author><author><style face="normal" font="default" size="100%">SALAH ZITOUNI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Existence and Ulam Stability Results for Nonlinear Fractional Differential Equations</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Applied Nonlinear Dynamics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">:pp. 655-666 | DOI: 10.5890/JAND.2020.12.009</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Halim Zeghdoudi</style></author><author><style face="normal" font="default" size="100%">Guerouah Amine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of GARCH(1,1) Model: Quasi-Maximum Likelihood and Pseudo-Maximum Likelihood</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%"> https://www.researchgate.net/publication/322645190</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amine Guerouah</style></author><author><style face="normal" font="default" size="100%">Halim Zeghdoudi</style></author><author><style face="normal" font="default" size="100%">Fatima Zohra Bouseba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Univariate and Multivariate GARCH Models: Oil Price and Stock market returns volatilities</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Applied Mathematics and Statistics (IJAMAS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ceserp.com/cp-jour/index.php/ijamas/article/view/35</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper investigates the empirical properties ofoil price and Stock market return volatilities using a range of univariate and multivariate GARCH models and monthly data from the U.S. The study relates the period August 1987 to October 2016, a total of 351 observations given. The aim of this paper is to examine the relationship between stock and oil markets. In addition, we evaluate the performance of each model with a range of diagnostic and forecast performance tests using univariate GARCH(1,1) and bivariate BEKK GARCH(1,1), DCC GARCH(1,1) models.&lt;br&gt;Keywords: GARCH models, Volatility, energy prices, Stock prices and Crude oil.
&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">No 1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Zohra Bouseba</style></author><author><style face="normal" font="default" size="100%">Halim Zeghdoudi</style></author><author><style face="normal" font="default" size="100%">Guerouah Amine</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Variance and Volatility Swaps in Oil Markets</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computer Science &amp; Computational Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">DOI: 10.20967/jcscm.2017.02.002</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	This paper focuses on the pricing of variance and volatility swaps in oil markets in some steps, which we considered similarly in the work of A. Swishchuk [1]. In this study a numerical example of oil price (August 1987-October 2016) has been given.&lt;br&gt;Keywords: oil market, stochastic mean-reverting volatility, volatility swaps.
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</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record></records></xml>