In this article, daily CO2 emissions for the years 2019-2022 are examined using fractional integration for Brazil, China, EU-27 (and the UK), India, and the USA. According to the findings, all series exhibit long memory mean-reversion tendencies, with orders of integration ranging between 0.22...

This paper introduces a new predictive analytics model for forecasting stock price trends in financial assets traded on major stock exchanges worldwide and the Colombian Stock Exchange. The model is built on a probability space definition that consists of a measurable space derived from...

This note puts forward a new modelling approach that includes both fractional integration and autoregressive processes in a unified framework. The proposed model is very general and includes other more standard approaches such as the AR(F)IMA models. Some Monte Carlo evidence shows that the...

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We...

Purpose - The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen's economic reconstruction efforts. Design/methodology/approach - This study utilized a time-series dataset encompassing remittance inflows into Yemen's...

The COVID-19 outbreak in 2020 has fostered in many countries the development of new weekly economic indices for the timely tracking of pandemic-related turmoils and other forms of rapid economic changes. Such indices often utilise information from daily and weekly economic time series that...

The objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database. Using non-seasonal ARIMA (Autoregressive Integrated...

A few recent publications on interrupted time series analysis only conduct preintervention modelling and use it to illustrate postintervention deviation without quantifying the amount lost during the intervention period. Thus, this study aims to illustrate how to estimate and quantify the actual...

A few recent publications on interrupted time series analysis only conduct preintervention modelling and use it to illustrate postintervention deviation without quantifying the amount lost during the intervention period. Thus, this study aims to illustrate how to estimate and quantify the actual...

The urgent and pressing challenge posed by global climate change underscores the critical need for immediate and decisive action, particularly in the mitigation of greenhouse gas (GHG) emissions to facilitate a trajectory towards sustainability. The significance of the aluminium industry,...

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their...

The current study aims to model the South African crude oil prices using the hybrid of Box-Jenkins autoregressive integrated moving average (ARIMA) and Neural Networks (NNs). This study introduces a hybrid approach to forecasting methods aimed at resolving the issues of lack of precision in...

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration framework. The proposed model is based on a specific test on fractional integration that is more general than the standard methods, which allow for only...

The objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database. Using non-seasonal ARIMA (Autoregressive Integrated...

This paper explores the evolution of non-working time in Spain over recent years by analysing the results of two surveys conducted by the National Statistics Institute: the Quarterly Survey on Labor Costs and the Labor Force Survey. Using time series models and intervention analysis, potential...

The equivalence of the Beveridge-Nelson decomposition and the trend-cycle decomposition is well established. In this paper we argue that this equivalence is almost immediate when a Gaussian score-driven location model is considered. We also provide a natural extension towards heavy-tailed...

Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive...

Sustainability is a key topic nowadays, mostly because in the last decade the pollution levels have reached an all-time high. National governments are searching for sustainable and environmentally friendly solutions to decrease the amount of pollution. This study is a cross-sectional study on 27...

Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate,...

This study employs machine learning models to forecast and comprehend the implied volatility of China ETF50. We develop a hybrid model named LSTM-ML, leveraging historical implied volatility, moneyness, and time-to-maturity as input features. The LSTM component captures dynamic hidden...

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value-weighted US decile portfolios sorted by market size, book-to-market, and...

Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive...

The proper forecasting of listed companies' earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of...

Zimbabwe is blessed with tourist attractions that draw visitors from all over the world. However, there are no quantitative models available for tourism stakeholders to utilize in decision-making and planning. The country is experiencing foreign currency shortages, which may be alleviated if the...