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Das Verständnis der kombinierten Auswirkungen physischer und transitorischer Klimarisiken auf Schaden-/Unfallversicherer ist entscheidend für deren Finanzstabilität, da sie von beiden Risikotypen erheblich betroffen sind. Diese Studie untersucht, wie Klimarisiken die Solvenzanforderungen von Schaden-/Unfallversicherern beeinflussen. Physische und transitorische Risiken sowie deren Abhängigkeiten werden mithilfe von Copulas mit Tail-Dependence modelliert. Im Mittelpunkt steht nicht der direkte Einfluss auf die Solvabilitätskapitalanforderungen, sondern auf sogenannte Solvenzlinien, die Mindestanforderungen an Risiko-Rendite-Profile für braune, grüne und andere Anlagetypen beschreiben. Da Kapitalanlagen kurzfristiger als Reaktion auf Klimaschocks angepasst werden können als Verbindlichkeiten, stehen sie im Fokus solvenzorientierter Steuerungsmaßnahmen. Die Analyse verschiedener Klimaszenarien zeigt deren Auswirkungen auf zulässige Anlageportfolios von Versicherern. Je nach Portfoliozusammensetzung und Abhängigkeitsstruktur könnten zusätzliche jährliche Renditen von etwa 6?% auf Jahressicht bzw. 3,2?% über einen Zehnjahreszeitraum erforderlich sein.

The banking sector in Indonesia is facing significant challenges following the COVID-19 pandemic, including declining profitability, bankruptcy risks, and pressures on liquidity and solvency. The lack of research on the moderating role of dividend policy in the relationship between liquidity, solvency, and profitability is a major issue. This study aims to analyze the impact of liquidity (LDR) and solvency (CAR) on the profitability (ROA) of conventional banks listed on the Indonesia Stock Exchange (IDX) for the period 2014-2023, as well as to examine the role of dividend policy (DPR) as a moderating variable. Using a quantitative approach, secondary data from Refinitiv Eikon and financial reports of 22 banks with 175 observations will be analyzed through moderation regression analysis (MRA) using STATA. The results show that liquidity (LDR) has a negative and insignificant effect on ROA, while solvability (CAR) has a positive and significant effect. Dividend policy (DPR) does not moderate the relationship between liquidity or solvability and profitability. These findings emphasize the importance of capital adequacy for profitability, support signaling theory, but indicate the need for an evaluation of liquidity strategies. Dividend policy is not proven to be a moderating mechanism, suggesting that banks should prioritize capital management and regulators should consider contextual factors in dividend policy.

One of the most important risks in the actuarial industry is the longevity risk. The accurate prediction of mortality rates plays a crucial role in the management of the aforementioned risk. Such predictions are performed by modelling the mortality rates using mortality models. Aiming at possible improvements in such forecasts, in this work we examine the effect of data transformation and “linearization†on the quality of time series forecasts of mortality rate data. By the term time series “linearization†is meant the treatment of causes that disrupt the underlying stochastic process measured by a time series. The dataset consists of the time series of the period indices uncovering the mortality trend for England-Wales according to published mortality models. Results indicate a clear improvement in interval forecasts. However, the result on point forecasts is not as clear as is the case of interval forecasts. The documented improvement in interval forecasts can significantly affect the Solvency Capital Requirement, and subsequently the Solvency Ratio for a pension fund. Such an improvement might put some pension providers at a competitive advantage as they have less capital locked in their liabilities. In addition, it was confirmed that the transformed-linearized time series of mortality rates satisfy to a higher extent the need for normality as compared to the original series.

Throughout Europe, the introduction of Solvency II is forcing companies in the life assurance and pensions provision markets to change how they estimate their liabilities. Historically, each solvency assessment required that the estimation of liabilities was performed once, using actuaries' views of economic and demographic trends. Solvency II requires that each assessment of solvency implies a 1-in-200 chance of not being able to meet the liabilities. The underlying stochastic nature of these requirements has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. Currently, practitioners within UK pension provision companies consider the calculations required to meet new regulations to be outside the realms of anything which is achievable. This project brings the calculations within reach: this thesis shows that it is possible to perform the required calculations in manageable time scales, using entirely reasonable quantities of hardware. This is achieved through the use of several techniques: firstly, a new algorithm has been developed which reduces the computational complexity of the reserving algorithm from O(T2) to O(T) for T projection steps, and is sufficiently general to be applicable to a wide range of non unit-linked policies; secondly, efficient ab-initio code, which may be tuned to optimise its performance on many current architectures, has been written; thirdly, approximations which do not change the result by a significant amount have been introduced; and, finally, high performance computers have been used to run the code. This project demonstrates that the calculations can be completed in under three minutes when using 12,000 cores of a supercomputer, or in under eight hours when using 80 cores of a moderately sized cluster.

Solvency II Directive 2009/138/EC requires an insurance and reinsurance undertakings assessment of a Solvency Capital Requirement by means of the so-called “Standard Formula” or by means of partial or full internal models. Focusing on the first approach, the bottom-up aggregation formula proposed by the regulator allows for a capital reduction due to the diversification effect, according to the typical subadditivity property of risk measures. However, once the overall capital has been assessed no specific allocation formula is provided or required in order to evaluate the contribution of each risk source on the overall Solvency Capital Requirement. The aim of this paper is twofold. First, we provide a closed formula for capital allocation fully compliant with the Solvency II Capital Requirement assessed by means of the Standard Formula. The solution enables a top-down approach to assess the allocated Solvency Capital Requirement among the risks considered in the Solvency II multilevel aggregation scheme; we demonstrate that the allocation formula adopted is consistent with the Euler allocation principle. Second, a solution is found as a result of an optimum capital allocation problem based on a Return On Risk Adjusted Capital measure; we establish the equivalence between the Return On Risk Adjusted Capital optimization, when the risk adjusted capital is calculated according to the Standard Formula, and the Markowitz mean-variance optimization.

The present research work aimed to examine the vital factors that affect the solvency of the Indian construction sector. The two different parameters of solvency, namely, debt to total assets (DTA) and cash flow to total liabilities (CFTL), were used in the present study. These two solvency indicators were categorized using zero and one numerical values. One indicates financially sound companies, and zero indicates weak companies with poor solvency ratios. The different financial ratios, namely, profitability, liquidity, leverages, and turnovers, were used as predictors or explanatory variables of insolvency of Indian construction companies. The study employs multivariate discriminant analysis (MDA) and binary logistic regression to predict the factors accountable for the insolvency of the Indian construction sector. The empirical findings of MDA and logistic regression show significant discrimination in the solvency position of construction companies according to their different financial performance parameters, namely, profitability, liquidity, and leverage. The empirical findings suggest that in the first case, the critical indicators predicting solvency are turnover, liquidity, and leverage ratios. In the second measure of solvency (CFTL), profitability significantly discriminates solvency of companies. Overall, the findings of MDA and logistic regression are consistent with each other. The outcomes of the study will be helpful to policymakers' different stakeholders.

We examine the information content of disclosures of solvency and earnings information of European insurance companies under the Solvency I and Solvency II regulatory regimes. Using an event-study research design, we investigate a sample of 571 announcements of 46 insurance firms during the 2012-2018 period. We find that under the Solvency I directive, investors find unexpected earnings to be informative but not the unexpected solvency ratio. However, under the Solvency II directive, both unexpected earnings and the unexpected solvency ratio are relevant to investors. Based on a decomposition of explained variance, we find that under the Solvency II directive, investors' attention has partly shifted away from earnings information toward solvency information. Our results indicate that the disclosed solvency ratios contain value relevant information under the risk-based Solvency II framework and that the requirements under this framework shift investor attention toward solvency information and away from earnings of European insurance companies.