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The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

Industry 5.0 has shown a new approach to integrating enterprises, particularly fintech firms. It would be interesting to see whether we are ready to implement Industry 5.0 across domains and enterprises while there are still obstacles to overcome, such as cybercrime impacting fintech organizations. Using empirical analysis of the fintech stocks that make up the KBW index, the author uncovered the influence of cybercrime on investor herding behavior in a highly interdependent environment provided by Industry 4.0. The cross-sectional standard deviation has been shown to rise after a cyber attack on a company anywhere on the globe. Furthermore, the author established the long-term equilibrium of the volatilities of gold and bitcoin returns, as well as the volatility of Keefe, Bruyette, and Woods (KBW) returns, in the sample after the firms’ cyber assault using Vector Auto Regressive (VAR) and Vector error correction model (VECM) models. Following the cyber assault, there is a decrease in the volatility of KBW returns while the volatility of bitcoin returns rises, suggesting a volatility transfer from one market to the other. These results show that during times of crisis, investors should be more careful in their approach to investment diversification, and any Industry 5.0 implementations should be done with the constraints in mind.

It is essential to understand how the countries with the highest number of tourist arrivals have managed to recover or not based on the competitiveness of the tourism industry during the pandemic stage. It is necessary to evaluate the policies implemented by each government to maintain the competitive performance of their industries. This chapter proposes a comprehensive review of the policies implemented in the 10 most visited countries according to UNWTO data. Most of these policies are geared toward economic and financial flexibility strategies for companies and individuals in the industry under study. The effectiveness of these policies is evaluated with statistical information extracted from a unified UNWTO database to reduce biases in the effectiveness analysis. Finally, concluding remarks are offered on the effectiveness of the policies and their contribution to the sector's recovery.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

An unexpected Coronavirus (COVID-19) pandemic has negatively affected the tourism and the hospitality industry, including luxury accommodation service providers. While this was not the first virus outbreak to impact the tourism sectors, in this case, its consequences were devastating. In this light, this contribution analyzes the case of an Italian luxury hotel, a winner of numerous awards during the last few years, including the prestigious World Luxury Hotel Award. The researchers compare its pre- and the post-COVID situation. They clarify that the outbreak has resulted in reduced reservations and explain how the upscale hotel responded to the unprecedented crisis by implementing different approaches. The luxury hospitality business decided to defend its brand differentiation and positioning strategy by continue offering improved service quality and by introducing enhanced hygiene and sanitation facilities, in order to deliver customer-centric experiences to their valued guests.