Search results

This paper aims to analyze thoroughly all of the sources of research used to develop the money laundering (ML) and terrorist financing (TF) low-risk rating, a rating attained by Norway according to the Basel Institute of Governance, and determine the reasons why Norway is one of only two countries in the world according to the 2012 report, with the other being Estonia, to gain an overall low-risk ML and TF rating.

The differences between the USA and Norway which has obtained a low-risk ranking, were compared and contrasted.

Beginning with the Basel Institute Rating index as a legitimate source for use in assessing anti-money-laundering (AML)/TF risk, and the amount of documentation used in the index’s methodology, it has been proven that the low-risk rating Norway has received is well deserved, and that the US rating of medium risk is also deserved for the time the report was published. Achieving a low-risk rating is not as ambiguous as recently thought and neither is its application on a global scale.

The paper identifies practical areas of improvement and concerns in addressing the overall issue of ML and terrorist financing.

The housing market in South Africa has the potential to drive economic growth and attract foreign investment, but it can be affected by various risk factors. This paper aims to conduct an empirical analysis of the effect of country risk components on the housing market in South Africa.

Linear and nonlinear autoregressive distributed lag (ARDL) models were used to evaluate the effects of the economic, financial and political risk factors of country risk on the prices of different segments of houses based on 276 monthly time-series data from January1995 to December 2015.

First, the results established that the three housing indices were more sensitive to political risk in the long run. Second, short run results showed that the three housing indices were largely influenced by their own preceding adjustments in the short run albeit minimal influences from political risk. Third, large housing segments indicated a higher magnitude of the country risk effect in South Africa.

This paper concluded that the response of housing prices to changes in the country risk components differed across the three segments of the housing market in South Africa. Consequently, this study presented the first comparison of the reactions of different housing segments to different components country risk.

The beta coefficient used for the cost of equity calculation is at the heart of the valuation process. This study conducts comparative analyses of the classical capital asset pricing model (CAPM) and downside CAPM risk parameters to gain further insight into which risk parameter leads to better performing risk measures at explaining stock returns.

The study conducts a comparative analysis of 16 risk measures at explaining the stock returns of 4531 companies of 20 developed and 25 emerging market index for 2000–2018. The analyses are conducted using both the global and local indices and both USD and local currency returns. Calculated risk measures are analyzed in a panel data setup using a univariate model. Results are investigated in country-specific and model-specific subsets.

The results show that (1) downside betas are better than CAPM betas at explaining the stock returns, (2) both risk measure groups perform better for emerging markets, (3) global downside beta model performs better than global beta model, implying the existence of the contagion effect, (4) high significance levels of total risk and unsystematic risk measures further support the shortfall of CAPM betas and (5) higher correlation of markets after negative shocks such as pandemics puts global CAPM based downside beta to a more reliable position.

The data are limited to the index securities as beta could be time varying.

Results overall provide insight into the cost of equity calculation and emerging market assets valuation.

The framework and methodology enable us to compare and contrast CAPM and downside-CAPM risk measures at the firm level, at the global/local level and in terms of the level of market development.

This study aims to examine the relationship between the Indonesian Islamic capital market, the country's risk and macroeconomic factors.

This study uses the Johansen cointegration test and the vector error correction model (VECM) on monthly data from January 2003 to March 2016 to examine the variables that influenced the Islamic capital market proxied by the Jakarta Islamic Index (JII).

The findings indicate the existence of short-term and long-term cointegrations between country risk (political, economic and financial risks), macroeconomic variables (industrial production index, inflation and oil price) and JII. In the long run, financial risk positively affects the JII, whereas economic risks and inflation are negatively related. In the short run, only inflation affect negatively the JII.

The study emphasizes the critical role of financial risk in affecting the Islamic capital market. Investors negatively respond to higher financial risk and react positively to more increased economic threats. The variable of financial risk has the highest coefficient, indicating that the investors favour a conducive financial environment in deriving JII.

This study extends the previous literature with an attempt to empirically examine the influence of Indonesia's country risk on the Islamic stock market through VECM.

Climate risk greatly increases the risk exposure of global investments. Both the climate risks of home countries and host countries may affect international investment behaviors. The purpose of this paper is to explore the impact of climate risk and climate risk distance on foreign direct investment (FDI) inflows and outflows. Targeted proposals are provided to promote international economic and trade cooperation and the authors provide suggestions for the FDI strategies of multinational enterprises.

The authors define “climate risk distance” as the difference in climate risks between two countries. This paper uses both a theoretical model and a generalized least squares test to investigate the impact of climate risk distance on FDI from the perspectives of FDI inflows and outflows. In addition, the authors subdivide the samples according to the sign of climate risk distance and rank the FDI share from home country to host country into four groups according to the host country’s climate risk index. Finally, the authors undertake empirical tests with outward foreign direct investment (OFDI) data to support the empirical results.

Investors from countries with low climate risks have the upper hand due to their competitive advantages, like their skills, trademarks and patent rights, which they can transfer abroad to offset the disadvantage of being non-native. This is generally defined as ownership advantage. The impact of climate risk distance on FDI depends on the sign of climate risk distance. Specifically, host countries with higher climate risks compared with the climate risk levels of home countries may experience insignificant reductions in FDI inflows. For investors from home countries with higher climate risks, they are less likely to invest in host countries with lower climate risks. The results for samples from emerging market economies are shown to be more significant.

This study advances the O (ownership advantage) part of the ownership, location and internationalization (OLI) paradigm by incorporating the climate risk distance between the home country and the host country into the influencing factors of FDI. Both the O part and the L (location advantage, the advantage that host countries offers to make internationalization worthwhile to undertake FDI) part of the OLI paradigm concerning climate risks are validated with FDI and OFDI data.

With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon.

This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company.

This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action.

This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.

This study offers a political risk prediction model that replicates a known political risk index on the basis of economic and political variables. The results show that the political risk is affected by the level of the United Nations human development index, the gross domestic savings as a percentage of gross domestic product, the labor force as a percentage of total population, the total expenditures on health and education as a percentage of gross domestic product and the level of the terms of trade.

The purpose of this paper is to contribute to a better understanding on relations between Chinese Outward Foreign Direct Investment (OFDI) and host country political risk. To contribute to a better understanding of whether traditional wisdom on foreign direct investment (FDI) is sufficient to explain the internationalization of Chinese multinational enterprises, the author collected 15 proxy variables from the PRS Group and Heritage Foundation and applied principal component analysis (PCA) to construct a new political risk index (PRI) that measures multiple facets of political risk for 139 countries.

Using this new PRI as a criterion, the author investigated changes in the political risk distribution (PRD) of Chinese outward FDI (OFDI) regarding investment destinations, large projects, annual investment outflows and sectorial distributions from 2006–2017.

The author found that the vast majority of Chinese OFDI during this period is concentrated in moderate- and low-risk countries, even at the sectorial level. This paper also shows that the continuing reform of Chinese OFDI policy and strong government support have led to an unprecedented increase in Chinese OFDI, while the PRD of Chinese OFDI has maintained a gradual decline over the past decade.

This research provides a new measurement that covers multiple facets of political risk.

The Renewable Energy Directive 2009/28/EC of the European Union provides another element to cross-border cooperation by allowing Member States to fulfill their 2020 renewable energy sources (RES) targets by implementing joint projects in third countries through the cooperation mechanisms. The purpose of this paper is to assess the country risk, to support bilateral cooperation for RES electricity generation projects.

A multicriteria decision support methodology has been developed taking into account three evaluation parameters, namely, the investment framework, the social conditions and the energy and technological status. An additive value model has been constructed, and the UTilitès Additives (UTA) – UTA* (UTASTAR) disaggregation method has been implemented to infer the criteria weights. The obtained ranking of alternatives has been subjected to robustness analysis, and finally the proposed methodology has been applied to five North Africa countries, so as to draw key results.

The pilot application of the methodological approach proposed and the model developed was fully compatible with the decision maker’s ranking on a set of fictitious countries and facilitated the assessment of a country’s current situation with regards to its investment, social conditions and energy and technological status. The results regarding the five North African countries examined, indicated the country’s investment framework as the most important factor, from foreign investors’ perspective, affecting a country’s suitability for the implementation of RES projects through a cooperation mechanism and Morocco, as well as Tunisia as the countries with the most suitable conditions for a successful implementation of such projects.

To the best of authors’ knowledge, there are only very few studies trying to assess opportunities and risks emerging from the implementation of joint projects between European and third countries in the field of electricity generation from RES. There are even less studies using (UTASTAR) method on real-world decision-making problems, and almost none are dedicated to energy sector-related problems.