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Under Solvency II, corporate governance requirements are a complementary, but nonetheless essential, element to build a sound regulatory framework for insurance undertakings, also to address risks not specifically mitigated by the sole solvency capital requirements. After recalling the provisions of the Second Pillar concerning the system of governance, the paper highlights the emerging regulatory trends in the corporate governance of insurance firms. Among others things, it signals the exceptional extension of the duties and responsibilities assigned to the board of directors, far beyond the traditional role of both monitoring the chief executive officer, and assessing the overall direction and strategy of the business. However, a better risk governance is not necessarily built on narrow rule-based approaches to corporate governance.
Depending on the point of time and location, insurance companies are subject to different forms of solvency regulation. In modern regulation regimes, such as the future standard Solvency II in the EU, insurance pricing is liberalized and risk-based capital requirements will be introduced. In many economies in Asia and Latin America, on the other hand, supervisors require the prior approval of policy conditions and insurance premiums, but do not conduct risk-based capital regulation. This paper compares the outcome of insurance rate regulation and risk-based capital requirements by deriving stock insurers’ best responses. It turns out that binding price floors affect insurers’ optimal capital structures and induce them to choose higher safety levels. Risk-based capital requirements are a more efficient instrument of solvency regulation and allow for lower insurance premiums, but may come at the cost of investment efforts into adequate risk monitoring systems. The paper derives threshold values for regulator’s investments into risk-based capital regulation and provides starting points for designing a welfare-enhancing insurance regulation scheme.
Insurance guarantee schemes aim to protect policyholders from the costs of insurer insolvencies. However, guarantee schemes can also reduce insurers’ incentives to conduct appropriate risk management. We investigate stock insurers’ risk-shifting behavior for insurance guarantee schemes under the two different financing alternatives: a flat-rate premium assessment versus a risk-based premium assessment. We identify which guarantee scheme maximizes policyholders’ welfare, measured by their expected utility. We find that the risk-based insurance guarantee scheme can only mitigate the insurer’s risk-shifting behavior if a substantial premium loading is present. Furthermore, the risk-based guarantee scheme is superior for improving policyholders’ welfare compared to the flat-rate scheme when the mitigating effect occurs.
Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
Central clearing counterparties (CCPs) were established to mitigate default losses resulting from counterparty risk in derivatives markets. In a parsimonious model, we show that clearing benefits are distributed unevenly across market participants. Loss sharing rules determine who wins or loses from clearing. Current rules disproportionately benefit market participants with flat portfolios. Instead, those with directional portfolios are relatively worse off, consistent with their reluctance to voluntarily use central clearing. Alternative loss sharing rules can address cross-sectional disparities in clearing benefits. However, we show that CCPs may favor current rules to maximize fee income, with externalities on clearing participation.
Market risks account for an integral part of life insurers' risk profiles. This paper explores the market risk sensitivities of insurers in two large life insurance markets, namely the U.S. and Europe. Based on panel regression models and daily market data from 2012 to 2018, we analyze the reaction of insurers' stock returns to changes in interest rates and CDS spreads of sovereign counterparties. We find that the influence of interest rate movements on stock returns is more than 50% larger for U.S. than for European life insurers. Falling interest rates reduce stock returns in particular for less solvent firms, insurers with a high share of life insurance reserves and unit-linked insurers. Moreover, life insurers' sensitivity to interest rate changes is seven times larger than their sensitivity towards CDS spreads. Only European insurers significantly suffer from rising CDS spreads, whereas U.S. insurers are immunized against increasing sovereign default probabilities.
Life insurance convexity
(2023)
Life insurers sell savings contracts with surrender options, which allow policyholders to prematurely receive guaranteed surrender values. These surrender options move toward the money when interest rates rise. Hence, higher interest rates raise surrender rates, as we document empirically by exploiting plausibly exogenous variation in monetary policy. Using a calibrated model, we then estimate that surrender options would force insurers to sell up to 2% of their investments during an enduring interest rate rise of 25 bps per year. We show that these fire sales are fueled by surrender value guarantees and insurers’ long-term investments.
Life insurance convexity
(2021)
Life insurers massively sell savings contracts with surrender options which allow policyholders to withdraw a guaranteed amount before maturity. These options move toward the money when interest rates rise. Using data on German life insurers, we estimate that a 1 percentage point increase in interest rates raises surrender rates by 17 basis points. We quantify the resulting liquidity risk in a calibrated model of surrender decisions and insurance cash flows. Simulations predict that surrender options can force insurers to sell up to 3% of their assets, depressing asset prices by 90 basis points. The effect is amplified by the duration of insurers' investments, and its impact on the term structure of interest rates depends on life insurers' investment strategy.
This paper documents that the bond investments of insurance companies transmit shocks from insurance markets to the real economy. Liquidity windfalls from household insurance purchases increase insurers' demand for corporate bonds. Exploiting the fact that insurers persistently invest in a small subset of firms for identification, I show that these increases in bond demand raise bond prices and lower firms' funding costs. In response, firms issue more bonds, especially when their bond underwriters are well connected with investors. Firms use the proceeds to raise investment rather than equity payouts. The results emphasize the significant impact of investor demand on firms' financing and investment activities.
Korean immigrants have migrated to New Zealand over the past three decades in search of a happier and more balanced life. While they anticipated that their children would be integrated into New Zealand society, they have primarily settled in Korean ethnic enclaves. In this context, younger Korean New Zealanders have been exposed to and influenced by New Zealand’s national and Korean ethnic cultures. This study examined success beliefs and well-being among Korean youth in New Zealand with a Third Culture Kid background (TCK K-NZ) in comparison to Korean youth in Korea (K-Korean) and European New Zealand youth (Pākehā). Results indicated that TCK K-NZ youth endorsed extrinsic success similarly to K-Korean youth, but that valuing extrinsic success predicted lowered well-being only for K-Korean youth. Conversely, valuing intrinsic success predicted higher well-being across the three groups. Results also revealed that TCK K-NZ youth's well-being levels were between those of K-Korean and Pākehā youth, potentially influenced by different structural relations between success beliefs and well-being, as well as their position as “third culture kids” in New Zealand. This study contributes to understanding cultures' roles in formulating success beliefs and the relationship between success beliefs and well-being for Korean New Zealander youth.
Background: Patients with cancer have an increased risk of VTE. We compared VTE rates and bleeding complications in 1) cancer patients receiving LMWH or UFH and 2) patients with or without cancer.
Patients with cancer have an increased risk of VTE. We compared VTE rates and bleeding complications in 1) cancer patients receiving LMWH or UFH and 2) patients with or without cancer.
Methods: Acutely-ill, non-surgical patients ≥70 years with (n = 274) or without cancer (n = 2,965) received certoparin 3,000 UaXa o.d. or UFH 5,000 IU t.i.d. for 8-20 days.
Results: 1) Thromboembolic events in cancer patients (proximal DVT, symptomatic non-fatal PE and VTE-related death) occurred at 4.50% with certoparin and 6.03% with UFH (OR 0.73; 95% CI 0.23-2.39). Major bleeding was comparable and minor bleedings (0.75 vs. 5.67%) were nominally less frequent. 7.5% of certoparin and 12.8% of UFH treated patients experienced serious adverse events. 2) Thromboembolic event rates were comparable in patients with or without cancer (5.29 vs. 4.13%) as were bleeding complications. All cause death was increased in cancer (OR 2.68; 95%CI 1.22-5.86). 10.2% of patients with and 5.81% of those without cancer experienced serious adverse events (OR 1.85; 95% CI 1.21-2.81).
Conclusions: Certoparin 3,000 UaXa o.d. and 5,000 IU UFH t.i.d. were equally effective and safe with respect to bleeding complications in patients with cancer. There were no statistically significant differences in the risk of thromboembolic events in patients with or without cancer receiving adequate anticoagulation.
Trial Registration: clinicaltrials.gov, NCT00451412
Die Befundung individueller Fallkonstellationen bei geeigneten Parameterkonstellationen und Fragestellungen ist ein zentraler Bestandteil der medizinischen Aufgabenstellung des Fachgebietes Laboratoriumsmedizin.
Um den labormedizinischen Anteil der medizinischen Diagnostik umfassend zu unterstützen, sollte unabhängig vom Einsatz wissensbasierter Systeme die labormedizinische Spezialbefundung generell bei entsprechenden Fragestellungen und Kenngrößenkonstellationen sowie bei Verfügbarkeit der jeweils geeigneten Methodik, bei Vorhandensein der entsprechenden Krankheitsprävalenzen und der entsprechenden labormedizinischen Kenntnisse durchgeführt werden. Dieser Notwendigkeit wird aber oft wegen des Aufwandes der individuellen fallbezogenen Befunderstellung nicht im erforderlichen Umfang entsprochen.
Bei richtigem Einsatz wissensbasierter Systeme kann die labormedizinische Spezialbefundung effizient unterstützt und auf hohem Niveau optimiert und, soweit sinnvoll, standardisiert werden. Dies ist eine der wesentlichen Zielsetzungen der Pro.M.D.-Entwicklung (Prologsystem zur Unterstützung Medizinischer Diagnostik). Weitere zum Teil ebenfalls bereits zu einem großen Teil erreichte Ziele bei der Pro.M.D.-Entwicklung sind die Schaffung einer gemeinsamen Notationsebene für das bei der labormedizinischen Spezialbefundung formalisierbare Wissen und die dadurch erreichbare Verbesserung des fallbezogenen Erfahrungsaustausches.
Highlights
• Protocol for extracting and analyzing pollen grains from fossil insects
• Individual fossil grains can be analyzed using a combined approach
• Simple and fast TEM embedding and sectioning protocol
• Protocol enables a taxonomic assignment of pollen
Summary
This protocol explains how to extract pollen from fossil insects with subsequent descriptions of pollen treatment. We also describe how to document morphological and ultrastructural features with light-microscopy and electron microscopy. It enables a taxonomic assignment of pollen that can be used to interpret flower-insect interactions, foraging and feeding behavior of insects, and the paleoenvironment. The protocol is limited by the state of the fossil, the presence/absence of pollen on fossil specimens, and the availability of extant pollen for comparison.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
(2022)
Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.