# Abdelhakim Aknouche

## Qassim University

H-index: 14

Asia-Saudi Arabia

## About Abdelhakim Aknouche

Abdelhakim Aknouche, With an exceptional h-index of 14 and a recent h-index of 10 (since 2020), a distinguished researcher at Qassim University,

His recent articles reflect a diverse array of research interests and contributions to the field:

Noising the GARCH volatility: A random coefficient GARCH model

A multiplicative thinning‐based integer‐valued GARCH model

Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models

Autoregressive conditional proportion: A multiplicative‐error model for (0, 1)‐valued time series

Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs

Forecasting transaction counts with integer-valued GARCH models

Stationarity and ergodicity of Markov switching positive conditional mean models

Periodic autoregressive conditional duration

### Abdelhakim Aknouche Information

University | Qassim University |
---|---|

Position | and USTHB |

Citations(all) | 534 |

Citations(since 2020) | 241 |

Cited By | 391 |

hIndex(all) | 14 |

hIndex(since 2020) | 10 |

i10Index(all) | 20 |

i10Index(since 2020) | 10 |

University Profile Page | Qassim University |

## Top articles of Abdelhakim Aknouche

### Noising the GARCH volatility: A random coefficient GARCH model

This paper proposes a noisy GARCH model with two volatility sequences (an unobserved and an observed one) and a stochastic time-varying conditional kurtosis. The unobserved volatility equation, equipped with random coefficients, is a linear function of the past squared observations and of the past observed volatility. The observed volatility is the conditional mean of the unobserved volatility, thus following the standard GARCH specification, where its coefficients are equal to the means of the random coefficients. The means and the variances of the random coefficients as well as the unobserved volatilities are estimated using a three-stage procedure. First, we estimate the means of the random coefficients, using the Gaussian quasi-maximum likelihood estimator (QMLE), then, the variances of the random coefficients, using a weighted least squares estimator (WLSE), and finally the latent volatilities through a filtering process, under the assumption that the random parameters follow an Inverse Gaussian distribution, with the innovation being normally distributed. Hence, the conditional distribution of the model is the Normal Inverse Gaussian (NIG), which entails a closed form expression for the posterior mean of the unobserved volatility. Consistency and asymptotic normality of the QMLE and WLSE are established under quite tractable assumptions. The proposed methodology is illustrated with various simulated and real examples.

Authors

Abdelhakim Aknouche,Bader Almohaimeed,Stefanos Dimitrakopoulos

Published Date

2024/3/15

### A multiplicative thinning‐based integer‐valued GARCH model

In this article, we introduce a multiplicative integer‐valued time series model, which is defined as the product of a unit‐mean integer‐valued independent and identically distributed (i.i.d.) sequence, and an integer‐valued dependent process. The latter is defined as a binomial thinning operation of its own past and of the past of the observed process. Furthermore, it combines some features of the integer‐valued GARCH (INGARCH), the autoregressive conditional duration (ACD), and the integer autoregression (INAR) processes. The proposed model has an unspecified distribution and is able to parsimoniously generate very high overdispersion, persistence, and heavy‐tailedness. The dynamic probabilistic structure of the model is first analyzed. In addition, parameter estimation is considered by using a two‐stage weighted least squares estimate (2SWLSE), consistency and asymptotic normality (CAN) of which are …

Authors

Abdelhakim Aknouche,Manuel G Scotto

Journal

Journal of Time Series Analysis

Published Date

2024/1

### Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models

General parametric forms are assumed for the conditional mean λ t (θ 0) and variance υ t of a time series. These conditional moments can for instance be derived from count time series, Autoregressive Conditional Duration or Generalized Autoregressive Score models. In this paper, our aim is to estimate the conditional mean parameter θ 0, trying to be as agnostic as possible about the conditional distribution of the observations. Quasi-Maximum Likelihood Estimators (QMLEs) based on the linear exponential family fulfill this goal, but they may be inefficient and have complicated asymptotic distributions when θ 0 contains boundary coefficients. We thus study alternative Weighted Least Square Estimators (WLSEs), which enjoy the same consistency property as the QMLEs when the conditional distribution is misspecified, but have simpler asymptotic distributions when components of θ 0 are null and gain in efficiency …

Authors

Abdelhakim Aknouche,Christian Francq

Journal

Journal of Econometrics

Published Date

2023/12/1

### Autoregressive conditional proportion: A multiplicative‐error model for (0, 1)‐valued time series

We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)‐valued time series, in the spirit of GARCH (generalized autoregressive conditional heteroscedastic) and ACD (autoregressive conditional duration) models. In particular, our underlying process is defined as the product of a (0,1)‐valued independent and identically distributed (i.i.d.) sequence and the inverted conditional mean, which, in turn, depends on past reciprocal observations in such a way that is larger than unity. The probability structure of the model is studied in the context of the stochastic recurrence equation theory, while estimation of the model parameters is performed with the exponential quasi‐maximum likelihood estimator (EQMLE). The consistency and asymptotic normality of the EQMLE are both established under general regularity assumptions. Finally, the usefulness of our proposed model is illustrated with …

Authors

Abdelhakim Aknouche,Stefanos Dimitrakopoulos

Journal

Journal of Time Series Analysis

Published Date

2023/7

### Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs

A common approach to analyze count time series is to fit models based on random sum operators. As an alternative, this paper introduces time series models based on a random multiplication operator, which is simply the multiplication of a variable operand by an integer-valued random coefficient, whose mean is the constant operand. Such operation is endowed into auto-regressive-like models with integer-valued random inputs, addressed as RMINAR. Two special variants are studied, namely the N0-valued random coefficient auto-regressive model and the N0-valued random coefficient multiplicative error model. Furthermore, Z-valued extensions are considered. The dynamic structure of the proposed models is studied in detail. In particular, their corresponding solutions are everywhere strictly stationary and ergodic, a fact that is not common neither in the literature on integer-valued time series models nor real-valued random coefficient auto-regressive models. Therefore, the parameters of the RMINAR model are estimated using a four-stage weighted least squares estimator, with consistency and asymptotic normality established everywhere in the parameter space. Finally, the new RMINAR models are illustrated with some simulated and empirical examples.

Authors

Abdelhakim Aknouche,Sonia Gouveia,Manuel Scotto

Journal

arXiv preprint arXiv:2312.11137

Published Date

2023/12/18

### Forecasting transaction counts with integer-valued GARCH models

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.

Authors

Abdelhakim Aknouche,Bader S Almohaimeed,Stefanos Dimitrakopoulos

Journal

Studies in Nonlinear Dynamics & Econometrics

Published Date

2022/10/4

### Stationarity and ergodicity of Markov switching positive conditional mean models

A general Markov‐Switching autoregressive conditional mean model, valued in the set of non‐negative numbers, is considered. The conditional distribution of this model is a finite mixture of non‐negative distributions whose conditional mean follows a GARCH‐like dynamics with parameters depending on the state of a Markov chain. Three different variants of the model are examined depending on how the lagged‐values of the mixing variable are integrated into the conditional mean equation. The model includes, in particular, Markov mixture versions of various well‐known non‐negative time series models such as the autoregressive conditional duration model, the integer‐valued GARCH (INGARCH) model, and the Beta observation driven model. For the three variants of the model, conditions are given for the existence of a stationary and ergodic solution. The proposed conditions match those already known for …

Authors

Abdelhakim Aknouche,Christian Francq

Journal

Journal of Time Series Analysis

Published Date

2022/5

### Periodic autoregressive conditional duration

We propose an autoregressive conditional duration (ACD) model with periodic time‐varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate the model parameters, using (profile and two‐stage) Gamma quasi‐maximum likelihood estimates (QMLEs), the asymptotic properties of which are examined under general regularity conditions. Our estimation method encompasses the exponential QMLE, as a particular case. The proposed methodology is illustrated with simulated data and two empirical applications on forecasting Bitcoin trading volume and realized volatility. We found that the PACD produces better in‐sample and out‐of‐sample forecasts than the standard ACD.

Authors

Abdelhakim Aknouche,Bader Almohaimeed,Stefanos Dimitrakopoulos

Journal

Journal of Time Series Analysis

Published Date

2022/1

### Count and duration time series with equal conditional stochastic and mean orders

We consider a positive-valued time series whose conditional distribution has a time-varying mean, which may depend on exogenous variables. The main applications concern count or duration data. Under a contraction condition on the mean function, it is shown that stationarity and ergodicity hold when the mean and stochastic orders of the conditional distribution are the same. The latter condition holds for the exponential family parametrized by the mean, but also for many other distributions. We also provide conditions for the existence of marginal moments and for the geometric decay of the beta-mixing coefficients. We give conditions for consistency and asymptotic normality of the Exponential Quasi-Maximum Likelihood Estimator of the conditional mean parameters. Simulation experiments and illustrations on series of stock market volumes and of greenhouse gas concentrations show that the multiplicative-error …

Authors

Abdelhakim Aknouche,Christian Francq

Journal

Econometric Theory

Published Date

2021/4

### Ordinal-response models for irregularly spaced transactions: A forecasting exercise

We propose a new model for transaction data that accounts jointly for the time duration between transactions and for the discreteness of the intraday stock price changes. Duration is assumed to follow a stochastic conditional duration model, while price discreteness is captured by an autoregressive moving average ordinal-response model with stochastic volatility and time-varying parameters. The proposed model also allows for endogeneity of the trade durations as well as for leverage and in-mean effects. In a purely Bayesian framework we conduct a forecasting exercise using multiple high-frequency transaction data sets and show that the proposed model produces better point and density forecasts than competing models.

Authors

Stefanos Dimitrakopoulos,Mike G Tsionas,Abdelhakim Aknouche

Published Date

2020/10/1

### Bayesian analysis of periodic asymmetric power GARCH models

In this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

Authors

Abdelhakim Aknouche,Nacer Demmouche,Stefanos Dimitrakopoulos,Nassim Touche

Journal

Studies in Nonlinear Dynamics & Econometrics

Published Date

2020/7/28

### On an integer-valued stochastic intensity model for time series of counts

We propose a broad class of count time series models, the mixed Poisson integer-valued stochastic intensity models. The proposed specification encompasses a wide range of conditional distributions of counts. We study its probabilistic structure and design Markov chain Monte Carlo algorithms for two cases; the Poisson and the negative binomial distributions. The methodology is applied to simulated data as well as to various data sets. Model comparison using marginal likelihoods and forecast evaluation using point and density forecasts are also considered.

Authors

Abdelhakim Aknouche,Stefanos Dimitrakopoulos

Published Date

2020/1/1

## Abdelhakim Aknouche FAQs

### What is Abdelhakim Aknouche's h-index at Qassim University?

The h-index of Abdelhakim Aknouche has been 10 since 2020 and 14 in total.

### What are Abdelhakim Aknouche's top articles?

The articles with the titles of

Noising the GARCH volatility: A random coefficient GARCH model

A multiplicative thinning‐based integer‐valued GARCH model

Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models

Autoregressive conditional proportion: A multiplicative‐error model for (0, 1)‐valued time series

Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs

Forecasting transaction counts with integer-valued GARCH models

Stationarity and ergodicity of Markov switching positive conditional mean models

Periodic autoregressive conditional duration

...

are the top articles of Abdelhakim Aknouche at Qassim University.

### What is Abdelhakim Aknouche's total number of citations?

Abdelhakim Aknouche has 534 citations in total.