Volume 17, Issue 1

Whether the stay-at-home order and face mask mandate are effective in slowing down the COVID-19 virus transmission is up for debate. To investigate this matter, we employ a unique angle. A two-wave logistic equation is proposed and then fitted to the cumulative case counts of all 50 states in the U.S. from the onset to early December of 2020 when vaccinating begins at large scale. The data period is confined to isolate the effects of executive orders from that of vaccination. The length of the first wave’s accelerating phase is regressed on variables describing the stay-at-home order and face mask mandate, along with control variables. A state’s lockdown duration is discovered to be negatively related to the time it takes for the virus to transit from accelerating to decelerating rates. This finding provides statistical support to the executive orders and can be useful in guiding risk management of future pandemics.

This study aims to analyse the effects of the General Agreement on Trade in Services (GATS) on the investment activities of the Pakistani insurance sector. First, we analyse the impact of GATS on the overall and company-wise return on investment of the insurance sector of Pakistan. In a panel data setting, the traditional fixed and random effect models are unsuitable for dealing with the possible issues, including endogeneity, unobserved heterogeneity, profit persistency, and the correlation between lagged dependent variables and independent variables. Therefore, we apply the Generalised Method of Moments (GMM) system estimator to the panel data from 1984 to 2018. In the Arellano-Bond framework, the estimated results indicate that the overall investment returns of the Pakistani insurance sector have increased after the GATS membership. However, company-wise analysis reveals mixed evidence. The average returns of IGI Insurance Limited and EFU General Insurance Limited have increased significantly after the GATS membership. These results support one of our empirical conjectures that a rising tide lifts some boats more than it does others. Then, we apply Markowitz’s Portfolio Theory to estimate the portfolio returns and the associated risk using the financial statements data from 1984 to 2018. The estimates of portfolio analysis reveal that the portfolio returns of the Pakistani insurance sector improve by 6.70 percentage points after the membership. However, the associated portfolio risk also increases by 11.13 percentage points, 7.40 times higher than the Pre-GATS risk. Despite the better returns, there is an intensive increase in investment volatility after GATS membership. This study provides a valuable reference for insurance managers and the Ministry of Finance and Securities & Exchange Commission of Pakistan to control and improve the insurance sector’s performance in Pakistan.

The use of risk maps is widespread and also mentioned in risk management standards. These visualizations display sets of risks by plotting each risk along two axes, representing the probability of occurrence and impact. Using an eye-tracking methodology, data on the cognitive processing of information from such risk maps were collected in order to examine why certain decisions are taken and what may influence their comprehension of this information. Data were collected from German and Indian participants. Those two countries are interesting for this study, as they differ greatly in several relevant domains like uncertainty avoidance or individualism. We found that individuals are generally able to perform a visual search task using a risk map but have more difficulty in making comparisons between two risks based on this type of visualization. The findings suggest that performance was related to cognitive reflection and that participants who reflected more on their decisions had a higher share of their fixations on target regions. In line with existing research, there seems to be evidence to support that cultural influences are at play when people work with risk maps, as Indians paid more attention to the context of the risk map’s target region. The influence of familiarity with working with risk maps was unclear, as there were some differences in eye movements visible but not for all variants.

We examine the determinants of credit rating for the debt instruments of Indian firms. The ordered probit model analysis of firms with rating announcements by CARE, CRISIL, and ICRA, the major rating agencies in India, indicates that interest coverage, profitability, financial leverage, market size, stock beta, and volatility have a significant effect on credit ratings. We find evidence that the credit rating levels have become lower after the market regulator’s passage of Transparency and Disclosure Norms in June 2010. The results suggest that regulatory disclosure requirements influence rating agencies to be conservative in their rating standards.

This paper proposes a sparse regularization technique for ratemaking under practical constraints. In tariff analysis of general insurance, rating factors with many categories are often grouped into a smaller number of classes to obtain reliable estimate of expected claim cost and make the tariff simple to reference. However, the number of rating-class segmentation combinations is often very large, making it computationally impossible to compare all the possible segmentations. In such cases, an L1 regularization method called the fused lasso is useful for integrating adjacent classes with similar risk levels in its inference process. Particularly, an extension of the fused lasso, known as the group fused lasso, enables consistent segmentation in estimating expected claim frequency and expected claim severity using generalized linear models. In this study, we enhance the group fused lasso by imposing ordinal constraints between the adjacent classes. Such constraints are often required in practice based on bonus–malus systems and actuarial insight on risk factors. We also propose an inference algorithm that uses the alternating direction method of multipliers. We apply the proposed method to motorcycle insurance claim data, and demonstrate how some adjacent categories are grouped into clusters with approximately homogeneous levels of expected claim frequency and severity.