Dr Eric Ofosu-Hene

Abstract

This paper presents the results of a targeted survey conducted among UK pension consultancies, insurers and reinsurers to explore current actuarial practices and challenges in modelling the impact on mortality and morbidity of climate perils. The responses reveal several recurring themes, including varying degrees of climate risk integration, plus significant data and methodological limitations. This is against a background of growing regulatory and stakeholder pressure to incorporate climate considerations into actuarial models. While a small number of organisations have made substantive progress, many are at a preliminary stage, relying primarily on qualitative or scenario-based approaches. Respondents identified a pressing need for standardised methodologies, improved data sources and practical guidance to support consistent and credible modelling. The findings highlight a clear need for profession-led guidance, improved access to climate-health data and practical frameworks to support the consistent integration of climate-related mortality and morbidity risks into actuarial work. Respondents expressed strong support for collaborative development of standardised scenarios, illustrative modelling approaches and case studies to bridge the gap between public-health research and actuarial application. The paper concludes with practical recommendations for practitioners and identifies priority areas for future research and professional guidance.

Abstract

This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

Mechanisms to incentivize divestment strategies, such as divestment schedules, are an important component of carbon reduction strategies. We use dynamic asset allocation methodologies to assess this impact over time on index portfolios (S&P 500 and FTSE 100), and global exchange-traded funds (ETFs). Although return profiles are not affected, the risk profile of S&P 500 divestment portfolios is impacted by rapid divestment strategies as divestment concentration increases. Instantaneous divestment may benefit management structure, while slower divestment provides greater stability in portfolios’ tracking errors and benefits carbon reduction, especially from reinvested capital. Divesting from energy and utilities sectors reduces carbon footprint of up to 7%, while ETFs’ divesting from highly carbon concentrated ETFs offers further carbon footprint reductions. Investing in funds with low carbon footprint results in lower dividend returns and management fees. Although ETFs’ returns are insensitive to divestment strategies and schedules, their risk profiles are affected, proportionally to their carbon intensity, especially for rapid divestment and at the expense of higher tracking errors. Divestment strategies based on ESG rating screening of FTSE 100 portfolios improve diversification and impact risk/return performance. Our study underscores the importance of considering investors’ demographics, such as dividends, management structure, and carbon reduction targets.

Against the backdrop of the climate finance gap, this paper investigates factors that motivate migrant participation in diaspora bonds tied to climate projects. The empirical study is based on a survey of 1265 responses from migrants in Europe. We employ change theory to study the shift in diaspora consumption remittance towards impact investing. The migrant’s association with the home country is positively correlated with participation in the bond. Diaspora bonds that pay frequent coupons, have longer maturity, target high education attainers and high-income earners are more likely to be successfully subscribed. Climate emergency requires investment in impact-driven projects and the result of this study highlights factors diaspora consider important when subscribing to diaspora bonds to close the climate finance gap.

This chapter investigates factors that motivate migrant participation in diaspora bonds issued to finance climate projects. The empirical study is based on a survey of 1265 migrants and interview responses from 17 diaspora investors and academic researchers in Europe. The migrant’s connection to the country of origin is positively correlated with participation in the bonds. Diaspora bonds with longer maturity and frequent coupons are attractive to high-income earners. A climate emergency requires investment in mitigation and adaptation projects. Thus, this study highlights factors diaspora considers important when subscribing to diaspora bonds for climate projects. These findings have major academic and policy implications.

This research has conducted a profound evaluation into the annuity market in Malaysia from both the business and individual perspective. To a great extent, analysis is made on how the annuity insurers could better maintain the solvency level of annuity fund which is exposed under significant economic risks by obtaining an optimal fund allocation to maximise investment returns through the development of Asset-Liability Management (ALM). By adapting the concept from Wilkie model, six fundamental models including inflation rate, real interest rate, KLCI stock return, KLCI stock dividend yield, 10-year MGS return as well as Malaysian property return were formulated accordingly to the Box-Jenkins modelling approach. It concludes that the optimal weightages combination suggests that the annuity insurers should allocate 94% of the single premium such as RM100,000 which collected from the annuitant to bond market, whereas the other 5% of the fund should be invested in stock but only 1% of the premium should be allocated into the property investment.

Mechanisms to develop divestment strategies are an essential component of carbon reduction strategies. The rate at which investors should divest has become a critical aspect of effective divestment, which has shifted from the periphery to a movement of over a thousand major investors, totaling more than $40.43 trillion in committed funds. We use a comprehensive approach and various asset allocation methodologies to address and assess this impact overtime on S&P 500 portfolios and global ETFs. For the S&P 500 portfolios, instantaneous divestment provides an advantage in terms of management structure, and when including ESG reporting considerations, faster divestment rates are preferable however these are shown to come at the expense of higher portfolio tracking errors. Rapid divestment by shorting may induce higher management fees, and also higher tracking error than slow divestment. Furthermore, we find slower divestment provides a greater stability in the portfolio with regard to the tracking error.Once a portfolio is fully divested from fossil fuels, divestment from secondary sectors utilising fossil fuels or those producing significant carbon outputs can further reduce a portfolios carbon footprint. For example, divesting from both energy and utility sectors offers a reduced carbon footprint of up to 7%, while ETFs' divesting offers substantially greater portfolio carbon footprint reductions. However, this comes at a cost since we find that investing in low carbon footprint funds results in lower dividend returns and attracts lower management fees. A study on ETFs with varying concentrations on carbon-intensive assets reveals that even though the return profile of the ETFs is insensitive to divestment strategies, their risk profile is proportionally (to their carbon intensity) affected by divestment strategies. Our study underscores the importance of considering the investors' demographics, such as retirees seeking dividend cash flows, management structure and carbon reduction targets on divestment strategies for ESG targets.

In this paper, we estimate the effect of exchange rate moments on foreign direct investment (FDI). Using data from a developing country (specifically Ghana) during the years 1990–2012 and robust regression and bootstrap method, the study finds that FDI increases as a result of the depreciation of the local currency. Secondly, using standard deviation as a measure of volatility, we find positive relationship between exchange rate volatility and FDI. Skewness did not show any significant relationship with FDI, which is straight to the heart of the mean reverting principle of the exchange rate. We find the political atmosphere as one of the main factors of drawing FDI into Ghana. These results are robust in the presence of all the standard control variables having significant coefficients under the various methodologies.

The objective of the study was in two parts; first, to construct an overall risk index to ascertain risk level of banks listed on Ghana Stock Exchange (GSE), second, to ascertain whether there is a significant relationship between risk managem...

In this paper a new approach to project the time varying index kt for the Lee-Carter (LC) model by using a machine learning technique known as Neural Network (NN) is proposed for forecasting Malaysian male and female mortality rates. To evaluate the forecasting performance of the proposed model, the conventional LC model which uses ARIMA to forecast kt is used as a benchmark. However unlike previous studies were done in Malaysian, we employed 9 different ARIMA models and evaluated the AIC and BIC to obtain the best fit model to forecast kt. The forecasting performance of the two methodologies were then compared using 3 performance indicators Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). In this study, findings showed that the proposed NN model outperformed the conventional ARIMA model for forecasting both Malaysian male and female mortality rates.