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Introduction: Studies on the insurance sector/companies have, in recent years, taken their place in literature at an increasing rate. Especially after the 2008 global financial crisis, the need for people to ensure their assets has structurally changed both the transaction volume and the yield structure of insurance sector. The increase in demand for insurance has also increased the appetite of investors to make an investment on this sector. The transaction volume of the insurance sector has increased year by year coupled with the number of insurance companies traded on the stock exchanges has started to increase in the same direction.

Aim: This chapter aims to determine the return structure of the Borsa Istanbul Insurance Index (XSGRT) based on daily closing values.

Method: Markedly with similar studies in the literature review, the authors determined that the Markov Regime Switching (MRS) model is the best-suited model for the current research. It was applied for the data set of XSGRT Index from 1997 to 2020.

Results: The result shows that XSGRT has three regimes named as expansion regime, normal regime and recession regime. Subsequently, it has been determined that the index generally attends to transition from the recession regime to the expansion regime and normal regime. This outcome is statistically significant at a 5% significance level and confirmed by backtesting results. Likewise, the duration of the recession regime is longer than the normal and expansion regime.

Conclusion: Despite the fact that the XSGRT has not yet completed its development compared to other main and sectoral indices, it is one of the indices that offer attractive earnings for investors. To put it differently, the desire of insurance companies to stay longer totally in the normal and expansion period and their immediate exit from the recession period provides them with a significant competitive advantage in contrast to other indices.

Originality/Value: This research contributes to the literature by providing additional evidence for existing studies using the longer duration of data set and applying the MRS model for Insurance Index. Best of our knowledge, it is the first study that examines the return structure of XSGRT based on its daily closing values from 1997 to 2020. In essence, investors can use the result of this study and compare it with other stock indices to make the accurate investment decision to maximise their welfare and return on their equity investments. The authors suggest that not only the return but also the regime structures of the invested shares (indices) should be taken into account for investment decisions.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

Using a market model of international equity returns, which fully incorporates the regime switching and heteroskedasticity effects, we conduct an empirical study on the asymmetric behavior of 31 emerging equity markets across the different regimes of both the global and the local markets. Asymmetric correlation is found to be much weaker than that among developed markets as documented in the recent studies. There is little evidence of performance enhancement by possessing information on asymmetric correlation in international asset allocation strategies involving emerging markets.

Real estate investment has been generally accepted as a value-adding proposition for a portfolio investor. Such an impression is not only shared by investment professionals and financial advisors but also appears to be supported by an overwhelming amount of research in the academic literature. The benefits of adding real estate as an asset class to a well-diversified portfolio are usually attributed to the respectable risk-return profile of real estate investment together with the relatively low correlation between its returns and the returns of other financial assets. By using the regime-switching technique on an extensive historical dataset, we attempt to look for the statistical evidence for such a claim. Unfortunately, the empirical support for the claim is neither strong nor universal. We find that any statistically significant improvement in risk-adjusted return is very much limited to the bullish environment of the real estate market. In general, the diversification benefit is not found to be statistically significant unless investors are relatively risk averse. We also document a regime-switching behavior of real estate returns similar to those found in other financial assets. There are two distinct states of the real estate market. The low-return (high-return) state is characterized by its high (low) volatility and its high (low) correlations with the stock market returns. We find this kind of dynamic risk characteristics to play a crucial role in dictating the diversification benefit from real estate investment.

The establishment of a currency union is a topical issue in the West African Monetary Zone (WAMZ). The subject of currency union formation needs to be reassessed in light of the recent efforts towards the economic integration of west African countries. This study employs the Markov Switching Model (MSM) to determine whether a currency union in WAMZ is feasible. The study analyzes the regime switching behavior in WAMZ countries’ foreign exchange markets before and after the formation of the union. The contribution of this study is two-fold. First, the study accounts for the success or otherwise of the latest efforts to integrate the fiscal and monetary strategies in the zone. Secondly, the study contributes to the literature on the currency union literature in WAMZ by using Markov Switching Model (MSM) to generate novel results. The results of the study revealed that prior to the WAMZ formation, the real exchange rates of member states were more divergent. In contrast, a growing but marginal, convergence was observed after the formation of the zone amongst four (Nigeria, Sierra Leone, Gambia, and Liberia) of the six countries. The authors conclude that while WAMZ is on course for establishing a currency union, their monetary authorities must work together, particularly with Ghana and Liberia, to synchronize their policy efforts, and policy makers must implement policies to strengthen harmonious trade interactions.

Many multinational firms attempt to cope with trade policy uncertainties by developing the option of manufacturing their goods in multiple production facilities in different countries. In this chapter, we explore how such “production switching” options affect the vulnerability of a country’s exports to foreign protectionism. We present a theoretical model of such behavior and show that production switching increases the elasticity of a country’s export with respect to tariffs. The magnitude of the elasticity depends on a country’s position in the value chain. We use the model’s predictions to provide new insights into the vulnerability of China’s exports during the current Sino–US trade war.

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.

This chapter introduces a conceptual framework which links consumers' demographic characteristics with their attitudes toward major shopping area attributes (the push/pull factors), as well as their motivations toward cross-border shopping. It is built on the extant literature of outshopping, cross-border shopping, and consumer switching behavior. It has been tested with data collected from 485 Hong Kong residents. A nonparametric approach will be used to analyze the data. Findings of this study show that “age” and “education” characteristics are good indicators for most of the macrofactors (shopping area attributes). As for microfactors (motivational factors), “age” and “gender” are the best indicators. Results of this study also confirm previous findings that demographic characteristics of consumers affect their cross-border shopping behavior. Low prices on products and good services are the most important pull-factor attracting cross-border shopping. It further reveals that a higher percentage of cross-border shoppers are from lower income families, having only secondary education level, and in the age category of 30–49. Implications for retailers, governments, and tourism-related institutions are discussed.