Abstract

Climate change has led to a rise in both the frequency and intensity of extreme weather events, presenting substantial challenges to the global property insurance sector. This study examines the concurrent crises faced by insurance companies in maintaining profitability and by property owners in affording coverage, thereby necessitating a reevaluation of current insurance paradigms. We present an innovative approach that utilizes a back-propagation (BP) neural network to forecast insurance losses and guide strategic underwriting decisions in regions prone to perils. Our advanced model combines crucial factors such as the likelihood of extreme meteorological phenomena, insurance risk premiums, and capital adequacy ratios to predict impending losses and inform insurance policy formulation. Through an extensive analysis of nearly two decades of catastrophe data from the United States and New Zealand, we determine the model’s effectiveness in anticipating insurance-related losses and providing strategic guidance to insurance entities. The study concludes with recommendations for insurers to enhance risk assessment, innovate products, collaborate with governments, invest in green projects, and establish dedicated climate risk management teams. The study contributes to the discourse on climate change adaptation in the insurance industry and offers practical solutions for navigating the complexities of extreme weather risk.

Abstract

The implications of insurance risk margins for insurers are extensive, impacting their competitive position, financial stability, and overall business strategy. Inadequate risk margins can lead to significant financial losses and regulatory sanctions, endangering their reputation and long-term viability. This study examines the insurance claims for general accident and subsidized agriculture business classes of the Nigerian Agricultural Insurance Corporation (NAIC) from 2007 to 2019. The chain ladder technique is employed to estimate ultimate claims. By incorporating claims variability using the Mack model, we estimate a more appropriate risk margin for outstanding claims. Additionally, we frame the allocation of outstanding claims as an optimization problem and compute the optimal reserves for each accident year, constrained by 90% of the insurer’s overall budget, assuming log-normally distributed claims. Our findings indicate that although the total ultimate reserves for the general accident class is much lower than that of the subsidized agriculture, the former’s aggregated coefficient of variation for all accident years combined is much higher, with a value greater than one. This suggests that NAIC’s general accident class of business is highly susceptible to systemic risk contagion. Moreover, a significant underestimation of the risk margin is observed when variability analysis is not considered. Consequently, Nigerian insurance regulators are urged to mandate variability analysis in the claims reserve estimation within annual financial reports.

Abstract

This study uses spatial-gravity models to examine intra-industry trade flow based on technological intensity for a cluster of the main EU-advanced economies (Spain, France, Germany, Italy, and Greece) and MEDA-transitional economies (Morocco, Algeria, Tunisia, Libya, Egypt, Israel, Jordan, Lebanon, Syria, and Turkey) from 1990 to 2020. We have inserted into the models in an original way the variables of cultural affinity to grasp the effects of the liability of foreignness. We also consider the upheavals began with the “global economic crisis” (2007–2008), including the “sovereign debt crisis” for EU countries (2009–2011), and the “Arab Springs” for MEDA-transitional economies (2011–012). Results show the SAR-AR specification used to estimate our models is correct, and variables to catch the liability of foreignness make for more suitable regression. We have found significant trade flows with one order of lags, as a proxy for a persistence effect in trade flows, as well as other explanatory variables such as industry middle productivity, openness, stock market capitalization, and the exchange rate. While tariff barriers, remittance in- and outflows, and dummy variables that capture the effect of partnership agreements and the upheavals are less significant, nonetheless, these two effects have been found to be significant in the low-tech and service industries. We also found that improvements in the governance climate in EU-advanced economies have a negative effect on intra-industry trade flow, whereas improvements in the governance climate in MEDA-transitional economies have a positive effect, but only for high-tech production and services. We conclude that it is important for the European Union to have traced new guidelines to Euro-Mediterranean (Euro-Med) common action through cooperation in order to avoid the deterioration of political and economic relations between the countries in the Euro-Med region.

Abstract

This study assesses the performance of Small Finance Banks in India, focusing on their efficiency in the post-COVID-19 period from 2019 to 2023. It explores the influence of bank-specific and macroeconomic factors on the efficiency of Small Finance Banks, with the aim of understanding their role in promoting financial inclusion. The research employs a two-stage Data Envelopment Analysis (DEA) framework to evaluate the efficiency of 10 selected Small Finance Banks. It incorporates both bank-specific variables (such as capital adequacy ratio, credit-deposit ratio, and liquidity ratio) and macroeconomic factors (GDP and inflation) in a Tobit regression model to analyze their influence on efficiency. The study reveals that the majority of Small Finance Banks remained resilient during the pandemic, consistently achieving high efficiency scores, except for a slight dip in 2020–2021 due to lockdown measures. Bank-specific factors indicate a converse association between capital adequacy ratio and efficiency, while liquidity ratio and credit-deposit ratio positively correlate with efficiency. Macro factors, including GDP and inflation, have minimal impact on Small Finance Banks efficiency. This study provides a comprehensive assessment of Small Finance Banks’ performance post-COVID-19, shedding light on the factors influencing their efficiency. It offers valuable insights for policymakers and Small Finance Banks to strengthen their role in advancing financial inclusion in India, contributing to a more inclusive and dynamic financial landscape. The findings suggest that Small Finance Banks should focus on expanding their influence in niche segments by increasing assets, deposits, and revenue streams while managing operating expenses and liquidity risks. Listing on the stock exchange and active policy support for research initiatives can enhance the Small Finance Banks ecosystem and drive financial inclusion.

Abstract

This paper examined the impact of ownership concentration on cash-holding levels, including 4832 Chinese-listed companies. This study employed the Fixed Effects Model and the Generalized Method of Moments for quantitative analysis. This study shows a positive relationship between ownership concentration and cash holdings. Furthermore, ESG can mitigate the direct correlation between ownership concentration and corporate cash holdings. Finally, the impact described above is particularly noticeable for non-state-owned enterprises. In summary, the empirical findings offer a new analytical perspective on the cash-holding decisions of corporations in the Chinese capital market. Furthermore, this study illustrates the importance of ESG in corporate development to mitigate ownership concentration and excess cash holdings. As a result, the findings show that non-financial reporting, such as ESG disclosure, can reduce agency issues, making more accurate assessments of enterprise performance.

Abstract

This paper introduces a novel application of principal component analysis (PCA) in constructing equity indices. While PCA is well-established in other fields, its use in financial index design remains underexplored. The proposed method addresses entropy concerns in nonlinear return time series. PCA is employed to determine equity weights, using factor loadings to guide its construction. This results in a factor model index (FMI) that identifies sub-sectors and assigns data-driven weights. The FMI framework is flexible, allowing adaptation to different asset sub-groups and facilitating synthetic replication of risk factors.

Abstract

The role of social financing as a catalyst for financial growth, with potential to stimulate economic expansion, is a subject requiring further scrutiny and exploration. This study employs quarterly data from 2014 to 2023, encompassing social financing and economic growth patterns in both China and its Fujian Province. Through the construction of four OLS models and a VAR model, it has been determined that the extent of social financing within Fujian has a notable impact in enhancing regional economic growth, though the direct financing percentage proves to be insignificant. The research suggests that middle-income economies have the potential to increase their social financing scale. Relevant governmental bodies can facilitate economic progression by broadening enterprise financing avenues and altering the ratio of direct financing in social financing as per the actual requirements.

Abstract Interest rates influence the calculation of premiums, reserves and benefits in the long-term. Theoretically, calculation of such actuarial values is based on the assumption of constant interest rates although interest rates constantly move over time. To obtain a more realistic assessment in valuation, it is beneficial if stochastic interest rates are used. Accurate calculation of reserves ensures that the insurance company can pay claims. A Reserve is a sum of money held by a financial institution such as a life office or a pension fund to cover for the difference between present value of future liabilities including expenses and present value of future premiums. A term insurance contract is an insurance policy that pays the sum assured to the beneficiaries if the policyholder dies within the duration of the policy. The purpose of this study was to compare the reserves that would be needed for a term insurance policy using the Vasicek and the Cox-Ingersol-Ross models. The models were chosen and are widely used because they are tractable and their ease of implementation. The stages of this research activity started by estimating the parameters for the models using Maximum Likelihood Estimation Method. Interest rates were then simulated for the models under study. Next, the reserve value of term insurance policy was determined using the simulated interest rates for the models using the Prospective Method for four randomly generated people of different ages. At the final stage, the results of the reserve values for the models were interpreted and compared. Kenya’s Life Table 2001–2003 was used as the reference in determination of mortality assumptions in reserve calculation. The goodness of fit of the models were done using Likelihood Ratio Test and CIR model was a better fit for the data. Interest rates were highly volatile, a feature replicated better by CIR model. Reserve values were also high for CIR model. Reserve values were higher for male insureds due to a higher mortality rate for men than women while the benefit reserves for the younger age were lower as compared to the older ages.

Abstract In Nigeria’s electricity sector, there are numerous energy generation firms (GenCos). These companies are responsible for the generation of electricity for millions of clients around the country. These companies often face significant inventory management issues despite the high electricity demand. These inefficiencies can result in both financial losses and operational disruptions, which negatively impact these businesses’ overall profitability. Additionally, this study will look into how inventory management affects revenue generation, operational costs, and overall financial sustainability. Three generation firms were selected to represent Nigeria’s energy industry in the dataset. Mainstream Energy Solutions Limited, Transcorp Power Limited, and Egbin Power PLC are some of the companies. Important elements needed to assess inventory management practices and their impact on profitability in Nigeria’s oil industry are contained in the dataset. The following variables are used: year, city, location, cash flow, revenue, capital cost, and operating cost. Regression analysis, sensitivity analysis, and Data Envelope Analysis (DEA) were used to examine inventory management and its impact on profitability in the Nigerian energy sector. According to the analysis, there is a lot of pressure on businesses in Nigeria’s energy sector to efficiently control costs to stay profitable.