Is regulating the solvency of banks counter-productive?

This paper contains a critique of solvency regulation such as imposed on banks by Basel I and II. It argues that banks seeking to maximize rate of return on risk-adjusted capital (RORAC) aim at an optimal level of solvency because on the one hand, solvency S lowers the cost of refinancing; on the other, it ties costly capital. In period 1, exogenous changes in mean returns dµ and in volatility occur, causing optimal adjustments dS * / dµ and dS * / ds in period 2. Since banks reallocate their assets with certain µ and s values in response to the changed solvency level, an endogenous trade-off with slope dµ / ds results in period 3. Both Basel I and II are shown to modify this slope, inducing at least some banks to opt for a higher value of s in certain situations. Therefore, this type of solvency regulation can prove counter-productive

Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework

The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks—a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay. In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR®, a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies’ data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a self-optimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal

Some Comments on QIS3.

In this paper we discuss some issues proposed in the Solvency II - QIS3 report. In particular we comment and discuss the \AISAM-ACME study on non-life long tail liabilities; reserve risk and risk margin assessment under Solvency II". In the latter study the reserve risk calculation of non-life long tail insurers is investigated, based on a sample of 45 supervised European insurance companies. We start by giving an overview of risk measures used in a solvency environment. Next, we show that the Value-at-Risk measure is the solution of a general optimization problem. In a way this supports the current regulatory regime for banking supervision established by the Basel Capital Accord and the Solvency II regulatory regime under construction. Indeed, both have put forward a Value-at-Risk-based capital requirement approach. In the following Section, we confirm the findings of the AISAM-ACME study that, with respect to reserve risk, a loading for solvency amounting to 15% of the reserves, as put forward in QIS3, might be too high. Next, we discuss the concept of one-year volatility, which is crucial in the context of Solvency II. Also, the relation between a long term VaR and the corresponding short term VaR is explored. To conclude, we illustrate the fact that a long tail business should in many cases lead to a lower solvency capital requirement compared to a short tail business with a comparable amount of liabilities.Solvency II; reserve risk;

A Comparative Analysis Of The Effectiveness Of Three Solvency Management Models

The introduction of the Altman’s Z-score model in 1983 and much recently the Enyi’s Relative Solvency Ratio model in 2005 has divergently provided financial analysts with alternative methods of analyzing corporate solvency which hitherto was exclusively done using the traditional historical record based ratio analysis, with particular reference to the current ratio. To test the relevance and effectiveness of the three models, real life performance data were extracted from the annual reports of 7 quoted companies, analyzed using the three models and the results compared to show the strengths and weaknesses of each. The result revealed that the current ratio and the Z-score models suffer from many limitations including imprecision while the Relative Solvency Ratio combines the capability of an effective indicator with the precision required of a true predictor

Do higher solvency ratios reduce the costs of bailing out insured banks?

The relationship between solvency constraints and bank behaviour in the presence of fixed rate deposit insurance is investigated. A rise in the minimum solvency ratio does not necessarily reduce the adverse consequences of moral hazard: bank efficiency may fall and expected bailout costs may rise. Such outcomes are possible even if credit risk is purely systemic. Similar results obtain in respect of level increases in bank capital, tangible or intangible, although in this case purely systemic risk excludes perverse outcomes

Maximum Market Price of Longevity Risk under Solvency Regimes: The Case of Solvency II.

Longevity risk constitutes an important risk factor for life insurance companies, and it can be managed through longevity-linked securities. The market of longevity-linked securities is at present far from being complete and does not allow finding a unique pricing measure. We propose a method to estimate the maximum market price of longevity risk depending on the risk margin implicit within the calculation of the technical provisions as defined by Solvency II. The maximum price of longevity risk is determined for a survivor forward (S-forward), an agreement between two counterparties to exchange at maturity a fixed survival-dependent payment for a payment depending on the realized survival of a given cohort of individuals. The maximum prices determined for the S-forwards can be used to price other longevity-linked securities, such as q-forwards. The Cairns–Blake–Dowd model is used to represent the evolution of mortality over time that combined with the information on the risk margin, enables us to calculate upper limits for the risk-adjusted survival probabilities, the market price of longevity risk and the S-forward prices. Numerical results can be extended for the pricing of other longevity-linked securities

Robustness analysis and convergence of empirical finite-time ruin probabilities and estimation risk solvency margin.

We consider the classical risk model and carry out a sensitivity and robustness analysis of finite-time ruin probabilities. We provide algorithms to compute the related influence functions. We also prove the weak convergence of a sequence of empirical finite-time ruin probabilities starting from zero initial reserve toward a Gaussian random variable. We define the concepts of reliable finite-time ruin probability as a Value-at-Risk of the estimator of the finite-time ruin probability. To control this robust risk measure, an additional initial reserve is needed and called Estimation Risk Solvency Margin (ERSM). We apply our results to show how portfolio experience could be rewarded by cut-offs in solvency capital requirements. An application to catastrophe contamination and numerical examples are also developed.Finite-time ruin probability; robustness; Solvency II; reliable ruin probability; asymptotic Normality; influence function; Estimation Risk Solvency Margin (ERSM)