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The purpose of this paper is to answer the following two questions: Will Saudi Arabia get older? Will its pension system be sustainable?

The methodology/approach is to forecast KSA’s population with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson-Durbin recursion, then relies on an infinite impulse response prediction error filter.

Spectral analysis projections of Saudi age groups are more optimistic than the Bayesian probabilistic model sponsored by the United Nations Population Division: Saudi Arabia will not get older as fast as projected by the United Nations model. The KSA’s pension system will stay sustainable based on spectral analysis, whereas it will not based on the U.N. model.

Spectral analysis will provide better insight and understanding of population dynamics for Saudi government policymakers, as well as economic, health and pension planners.

The purpose of this paper is to present forecasts of fossil fuels prices until 2030 with spectral analysis to provide a clearer picture of this energy sector.

Fossil fuels prices time series are decomposed in simpler signals called approximations and details in the framework of the one-dimensional discrete wavelet analysis. The simplified signals are recomposed after Burg extension.

In 2019-2030 average price forecasts of: West Texas intermediate (WTI) oil ($58.67) is above its 1986-2030 long-term mean of $47.83; and coal ($81.01) is above its 1980-2030 long-term mean of $60.98. On the contrary, 2019-2030 average of price forecasts of: Henry Hub natural gas ($3.66) is below its 1997-2030 long-term mean of $4; heating oil ($0.64) is below its 1986-2030 long-term mean of $1.16; propane ($0.26) is below its 1992-2030 long-term mean of $0.66; and regular gasoline ($1.45) is below its 2003-2030 long-term mean of $1.87.

Fossil fuels prices projections may relieve participants of WTI oil and coal markets but worry participants of Henry Hub, heating oil, propane and regular gasoline markets including countries whose economy is tied to energy prices.

The purpose of this paper is to estimate the years the European Muslim population will be majority among 30 European countries.

The methodology/approach is to forecast the population of 30 European countries with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion, then relies on an infinite impulse response prediction error filter. Three scenarios are considered: the zero-migration scenario where the authors assume that the Muslim population has a higher fertility (one child more per woman, on average) than other Europeans, mirroring a global pattern; a 2017 migration scenario: to the Muslim population obtained in the zero-migration scenario, the authors add a continuous flow of migrants every year based on year 2017; the mid-point migration scenario is obtained by averaging the data of the two previous scenarios.

Among three scenarios, the most likely mid-point migration scenario identifies 13 countries where the Muslim population will be majority between years 2085 and 2215: Cyprus (in year 2085), Sweden (2125), France (2135), Greece (2135), Belgium (2140), Bulgaria (2140), Italy (2175), Luxembourg (2175), the UK (2180), Slovenia (2190), Switzerland (2195), Ireland (2200) and Lithuania (2215). The 17 remaining countries will never reach majority in the next 200 years.

The growing Muslim population will change the face of Europe socially, politically and economically. This paper will provide a better insight and understanding of Muslim population dynamics to European governments, policymakers, as well as social and economic planners.

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent economy between 2014 and 2017. Trades and investments have plummeted, leading to significant budget deficits. In response, the government unveiled a plan called Saudi Vision 2030 in 2016 which has triggered structural economic reforms leading to an unprecedented strategy of transition from an oil-driven economy to a modern market economy.

This paper forecasts with spectral analysis economic indicators of the Saudi economy up to 2030 to provide a clearer picture of the future economy assuming that the effects of recent reforms have not yet been traced by most of the economic indicators.

2018–2030 forecasts are all bearish except West Texas Intermediate (WTI) oil price expected to average $64.40 during the period 2019–2030. Two additional exceptions are the Saudi population that should grow to 40 million in 2030 and the swelling gross domestic product (GDP) generated by the non-oil sector resulting from bold actions of the Saudi government who is willing to become less dependent on revenues generated by the oil sector.

Government policymakers, economists and investors would have with spectral forecasts better insight and understanding of the Saudi economy dynamics at the early stage of major economic reforms implemented in the country. In 2020, the COVID-19 pandemic has brutally hurt the Saudi economy following a collapse in the global demand for oil and an oversupplied industry. The impact on the Saudi economy will depend on the optimal response brought by its government.

Saudi Vision 2030 plan has already triggered a deep transformation of the Saudi society that is reviewed in this paper.

The forecast of Saudi economic indicators is a timely topic considering the challenges facing the economy and reforms being undertaken. Applying an original forecasting technique to economic indicators adds to the originality of the paper.

The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

The purpose of this paper is to understand, which internal processes (mental, emotional, cultural, ethical and spiritual) Afghan business leaders use when making managerial decisions.

Primary data were collected through 1-h face-to-face interviews with Afghan business leaders. Interviews were conducted through open-ended questions in a semi-structured format. This method was considered most appropriate to acquire an understanding of senior executives’ interpretation and usage of decision-making processes. The method of analyzing data was thematic analysis where the researchers identified common themes, topics, ideas and patterns of meaning that come up repetitively. The objective of the analysis was to determine the most frequent decision-making processes by business leaders and the reasons for using these processes.

Although the usage of internal processes in decision-making are not homogeneous among Afghan business leaders, some of the processes are used more frequently than others such as mental, cultural and ethical processes. During the mental process of decision-making, the majority of leaders use intuitional decision-making, the minority using logic. Regarding the cultural dimension, the majority of leaders stated that they have an open, friendly, caring organization for each employee and horizontal culture in their organization. The minority indicated that they have a friendly culture but they also considered the processes and hierarchy in their organization. Considering the ethical process of decision-making, leaders stated that their priorities are more ethical than getting extra profit. They believe that profit will be generated while considering ethical values. As a leader noted: when you consider ethics and fulfill your obligations, the profit automatically generates. Most leaders use the internal process of emotion in their decision-making, but the usage has not been frequent. The emotional process of decision-making is more involved when the human factor is involved. For instance, one of the participants stated “I did not fire an employee that I had to because he was a needy and poor person.” About the spiritual process of decision-making, although all leaders agreed that they have used spirituality in decision-making, its usage varies. About one-third of the leaders mostly rely on spirituality or on religious teachings during the decision-making process, one-third somehow rely on spirituality or religion, about 50% of the time and one-third rely on spirituality between 25% to 30% of the time.

This study is pilot research as no previous research was carried out on this topic, therefore, it provides a basis of literature on the usage of internal processes on decision-making in Afghanistan. The findings may differ in other economic and national contexts.

This paper focuses on job satisfaction of women employees in the public sector of Afghanistan and identifies factors and challenges influencing women's job satisfaction in their workplace.

The survey data were gathered from 92 employees working at the Ministry of Finance at different position levels.

The findings indicate that women employees highly prioritize salary increment and job promotion as their main job satisfaction factors while mentioning low salary, delay in salary payment and unsuitable workplace as the biggest challenges. Among recommendations, the research suggests to launch workshops for male employees to improve their behavior with women employees in the workplace, to promote employees based on their merit regardless of gender, to standardize salary scales, to develop a chart of female rights highlighting how they should be treated in their workplace and to support the female employees by eliminating gender discrimination and providing a secure work environment free of gender bias.

The findings and recommendations may help public sector organizations as well as the private sector of Afghanistan to improve women employees' job satisfaction.

This article represents an added value for the literature which lacks references about the satisfaction of women working in the public sector of Afghanistan.