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The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

Climate change and the COVID-19 pandemic are complex and have multifaceted effects on countries in an unpredictable and unprecedented manner. While both COVID-19 and the climate crisis share similarities, they also have some notable differences. Being both systemic in nature with knock-on and cascading effects that propagate due to high connectedness of countries, COVID-19, however, presents imminent and directly visible dangers, while the risks from climate change are gradual, cumulative and often distributed dangers. Climate change has more significant medium and long-term impacts which are likely to worsen over time. There is no vaccine for climate change compared to COVID-19. In addition, those most affected by extreme climatic conditions have usually contributed the least to the root causes of the crisis. This is in fact the case of island economies. The chapter thus investigates into the vulnerability and resilience of 38 Small Islands Developing States (SIDs) to both shocks. Adopting a comprehensive conceptual framework and data on various indices from the literature and global databases, we assess the COVID-19 and climate change vulnerabilities of SIDs on multiple fronts. The results first reveal a higher vulnerability across all dimensions for the Pacific islands compared to the other islands in the sample. There is also evidence of a weak correlation between climate change risk and the COVID-19 pandemic confirming our premise that there are marked differences between these two shocks and their impacts on island communities.

Conceptualizing development in terms of risk management has become a prominent feature of mainstream development discourse. This has led to a convergence between the rubrics of financial inclusion and risk management whereby improved access for poor households to private sector credit, insurance and savings products is represented as a necessary step toward building “resilience.” This convergence, however, is notable for a shallow understanding of the production and distribution of risks. By naturalizing risk as an inevitable product of complex systems, the approach fails to interrogate how risk is produced and displaced unevenly between social groups. Ignoring the structural and relational dimensions of risk production leads to an overly technical approach to risk management that is willfully blind to the intersection of risk and social power. A case study of the promotion of index-based livestock insurance in Mongolia – held as a model for innovative risk management via financial inclusion – is used to indicate the tensions and contradictions of this projected synthesis of development and risk management.

Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

This chapter reviews and compares Southeast Asia country practices on global, regional, and local practices for disaster risk assessment (DRA). DRA research and practices include and create a disaster risk management (DRM) solution. There are 11 countries in Southeast Asia, but only 10 countries are members of the Association of Southeast Asian Nations (ASEAN), except Timor-Leste. The key objective of ASEAN’s formation is cooperation in economic growth, social, regional peace and cultural development, disaster management cooperation, and humanitarian assistance at the regional level. The DRM system practiced in ASEAN member countries is discussed in this chapter. Furthermore, the system and findings of DRAs are also addressed. Globally, two DRA structures are discussed and compared, namely Index of Risk Management (INFORM) and World Risk Index (WRI). In addition, regional vulnerability assessment guidelines for regional and national levels are discussed. However, several selected studies and practices such as the Indonesian Risk Index (InaRISK) are being discussed at the local level. Overall, there is space for improvement of coordination in terms of data and technology sharing for DRM, especially for assessment. The finding of this review highlighted the complexity of DRA at the global and regional levels and encouraging community DRA among the ASEAN members.

Introduction: Uncertainty plays an important role on economic stability and macroeconomic variables. Economic agents postpone decisions about investment and consumption in periods in which uncertainty is high. This situation affects economic growth negatively. Recently, uncertainty has focused on policy uncertainty. At this point, economic policy uncertainty (EPU) comes to the forefront. EPU is defined as conception that economic agents do not forecast consequences of economic policies adopted by policy makers and future economic policies. In terms of developing countries, statements presented by policy makers in the United States especially may appear as a source of uncertainty in developing economies.

Aim: Therefore, the aim of this study is to analyze the effects of US EPU on macroeconomic variables for Turkey and Brazil, Russia, India, China and South Africa for periods in which global risk perception is low and high.

Method: The authors used monthly data from January 1998 to December 2018. For this purpose, the authors used Threshold VAR. VIX index takes in consideration as global risk perception. The authors used US EPU index proposed by Baker’s vd. (2016) in order to measure EPU in the United States. Besides, the authors used macroeconomic variables such as industrial production index, inflation and exchange rate.

Findings: As is seen from the results of the analysis, for Turkey’s economy the macroeconomic variables significantly and strongly respond to the changes in the EPU index during the periods in which global risk perception is low; nonetheless, the so-called responses weaken due to the adopted policy of “wait and watch” by investors during the periods in which global risk perception is high.

Based on the aforementioned data, the risk index (RI) identifies the percentage of all students of a given racial/ethnic group in a given disability category. The RI is calculated by dividing the number of students in a given racial/ethnic group served in a given disability category (e.g. LD) by the total enrollment for that racial/ethnic group in the school population. The 1998 OCR data revealed risk indices for all racial/ethnic groups that were higher for LD than those found for MR. The NRC (2002) report stated that, “Asian/Pacific Islander have placement rates of 2.23%. Rates for all other racial/ethnic groups exceed 6%, and for American Indian/Alaskan Natives, the rate reached 7.45%” (p. 47). The second index, odds ratio, provides a comparative index of risk and is calculated by dividing the risk index on one racial/ethnic group by the risk index of another racial/ethnic group. In the OCR and OSEP databases, the odds ratios are reported relative to White students. If the risk index is identical for a particular minority group and White students, the odds ratio will equal 1.0. Odds ratios greater than 1.0 indicate that minority group students are at a greater risk of identification, while odds ratios of less than 1.0 indicate that they are less at risk. Using the 1998 OCR placement rates, the LD odds ratio for American Indian/Alaskan Natives is 1.24, showing that they have a 24% greater likelihood of being assigned to the LD category than White students. Odds ratios for Asian/Pacific Islander are low (0.37). For both Black and Hispanic students, the odds ratios are close to 1.0. The third index, composition index (CI), shows the proportion of all children served under a given disability category who are members of a given racial/ethnic group and is calculated by dividing the number of students of a given racial or ethnic group enrolled in a particular disability category. Two underlying assumptions of the CI are that the sum of composition indices for the five racial/ethnic groups will total 100%, and baseline enrollment of a given racial/ethnic group is not controlled. More specifically, the CI may be calculated using the percent of 6- through 21-year old population with the racial/ethnic composition of IDEA and U.S. census population statistics. For example, if 64% of the U.S. population is White, 15% is Black, 16% is Hispanic, 4% is Asian, and 1% is American Indian these data not interpretable without knowing the percentage of the racial/ethnic composition with IDEA. Hypothetically, IDEA data may show that of the 6–21 year olds served under IDEA, 63% are White, 20% are Black, 14% are Hispanic, 2% are Asian, and 1% is American Indian. To calculate disproportionality, a benchmark (e.g. 10%) against which to measure the difference between these percentages must be used. If the difference between the two percentages and the difference represented as a proportion of the group’s percent of population exceeds +10, then the racial/ethnic group is overrepresented. Conversely, if the difference between the two percentages and the difference represented as a proportion of the group’s percent of the population is larger than −10, then, the racial/ethnic group is underrepresented.