G28 Government Policy and Regulation
Refine
Year of publication
Document Type
- Working Paper (19)
- Article (4)
Language
- English (23)
Has Fulltext
- yes (23)
Is part of the Bibliography
- no (23)
Keywords
- Financial Regulation (2)
- Portfolio optimization (2)
- Risk aggregation (2)
- Solvency II (2)
- Systematic Risk (2)
- Systemic Risk (2)
- Asset Concentration Risk (1)
- Bank Accounting (1)
- Banking Supervision (1)
- Banking Union (1)
- Banks (1)
- Big data (1)
- CECL (1)
- COVID-19 (1)
- COVID-19 Pandemic (1)
- Capital allocation (1)
- Central Clearing (1)
- Central counterparty clearing house (CCP) (1)
- Collateral (1)
- Counterparty Risk (1)
- Credit default swap (CDS) (1)
- Customer data sharing (1)
- Data access (1)
- Data portability (1)
- Derivate (1)
- Derivatives (1)
- Digital footprints (1)
- Dividend Policy (1)
- Economic research (1)
- Eigene Risiko- und Solvenzbewertung (1)
- Enterprise Risk Management (1)
- European Market Infrastructure Regulation (EMIR) (1)
- Expected credit losses (1)
- FinTech (1)
- Financial Stability (1)
- Financial Supervision (1)
- Financial stability (1)
- Fire Sales (1)
- Hauptkomponentenanalyse (1)
- IFRS 9 (1)
- Idiosyncratic Risk (1)
- Impairments (1)
- Institutional Investors’ Ownership (1)
- Insurance Activities (1)
- Insurance Companies (1)
- Insurance companies (1)
- Interest Rates (1)
- Kapitalallokation (1)
- Kontrahentenrisiko (1)
- Lebensversicherung (1)
- Lending (1)
- Life Insurance (1)
- Liquidity Risk (1)
- Liquidity risk (1)
- Liquiditätsrisiko (1)
- Loans (1)
- Loss Sharing (1)
- Margin (1)
- Mark-to-market accounting (1)
- Marketplace lending (1)
- Microprudential Insurance Regulation (1)
- Mikroprudenzielle Versicherungsregulierung (1)
- Model-based regulation (1)
- Monetary policy transmission (1)
- Mutual Funds (1)
- NAV-price-spread (1)
- Notverkäufe (1)
- OTC markets (1)
- OTC-Märkte (1)
- Open banking (1)
- Open-end real estate funds (1)
- Own Risk and Solvency Assessment (1)
- P2P lending (1)
- Persistence (1)
- Political Economy (1)
- Portfoliooptimierung (1)
- Principal Component Analysis (1)
- QE (1)
- Risikoaggregation (1)
- Risikobegrenzung (1)
- Risikokapitalallokation (1)
- Risikokommunikation (1)
- Risikomessung (1)
- Risk capital allocation (1)
- Risk communication (1)
- Risk limiting (1)
- Risk measurement (1)
- Scenario analysis (1)
- Secondary market (1)
- Sectoral Asset Diversification (1)
- Sicherheitenmarge (1)
- Sovereign CDS (1)
- Supervision (1)
- Systematisches Risiko (1)
- Systemic risk (1)
- Systemically Important Financial Institutions (1)
- Szenarioanalyse (1)
- Tail correlation (1)
- Textual Analysis (1)
- Trading restrictions (1)
- Verlustbeteiligung (1)
- Vermögenskonzentrationsrisiko (1)
- Zentrales Clearing (1)
- Zinssätze (1)
- bond market liquidity (1)
- capital regulation (1)
- capital requirements (1)
- career concerns (1)
- catastrophe risk transfer (1)
- central bank (1)
- discretionary decisions (1)
- idiosynkratisches Risiko (1)
- market-making (1)
- pandemic insurance (1)
- private–public partnerships (1)
- quantitative easing (1)
- sektorale Vermögensdiversifizierung (1)
- systematisches Risiko (1)
- systemisches Risiko (1)
Different insurance activities exhibit different levels of persistence of shocks and volatility. For example, life insurance is typically more persistent but less volatile than non-life insurance. We examine how diversification among life, non-life insurance, and active reinsurance business affects an insurer's contribution and exposure to the risk of other companies. Our model shows that a counterparty's credit risk exposure to an insurance group substantially depends on the relative proportion of the insurance group's life and non-life business. The empirical analysis confirms this finding with respect to several measures for spillover risk. The optimal proportion of life business that minimizes spillover risk decreases with leverage of the insurance group, and increases with active reinsurance business.
European insurers are allowed to make discretionary decisions in the calculation of Solvency II capital requirements. These choices include the design of risk models (ranging from a standard formula to a full internal model) and the use of long-term guarantees measures. This article examines the impact and the drivers of discretionary decisions with respect to capital requirements for market risks. In a first step of our analysis, we assess the risk profiles of 49 stock insurers using daily market data. In a second step, we exploit hand-collected Solvency II data for the years 2016 to 2020. We find that long-term guarantees measures substantially influence the reported solvency ratios. The measures are chosen particularly by less solvent insurers and firms with high interest rate and credit spread sensitivities. Internal models are used more frequently by large insurers and especially for risks for which the firms have already found adequate immunization strategies.
This paper sheds light on the life insurance sector’s liquidity risk exposure. Life insurers are important long-term investors on financial markets. Due to their long-term investment horizon they cannot quickly adapt to changes in macroeconomic conditions. Rising interest rates in particular can expose life insurers to run-like situations, since a slow interest rate passthrough incentivizes policyholders to terminate insurance policies and invest the proceeds at relatively high market interest rates. We develop and empirically calibrate a granular model of policyholder behavior and life insurance cash flows to quantify insurers’ liquidity risk exposure stemming from policy terminations. Our model predicts that a sharp interest rate rise by 4.5pp within two years would force life insurers to liquidate 12% of their initial assets. While the associated fire sale costs are small under reasonable assumptions, policy terminations plausibly erase 30% of life insurers’ capital due to mark-to-market accounting. Our analysis reveals a mechanism by which monetary policy tightening increases liquidity risk exposure of non-bank financial intermediaries with long-term assets.
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.
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.
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that traditional tail-correlation matrices—which are conventionally assumed to have ones on the diagonal—can lead to substantial biases of the aggregate risk measurement’s sensitivities with respect to risk exposures. Due to these biases, decision-makers receive an odd view of the effects of portfolio changes and may be unable to identify the optimal portfolio from a risk-return perspective. To overcome these issues, we introduce the “sensitivity-implied tail-correlation matrix”. The proposed tail-correlation matrix allows for a simple deterministic risk aggregation approach which reasonably approximates the true aggregate risk measurement according to the complete multivariate risk distribution. Numerical examples demonstrate that our approach is a better basis for portfolio optimization than the Value-at-Risk implied tail-correlation matrix, especially if the calibration portfolio (or current portfolio) deviates from the optimal portfolio.
Historical evidence like the global financial crisis from 2007-09 highlights that sector concentration risk can play an important role for the solvency of insurers. However, current microprudential frameworks like the US RBC framework and Solvency II consider only name concentration risk explicitly in their solvency capital requirements for asset concentration risk and neglect sector concentration risk. We show by means of US insurers’ asset holdings from 2009 to 2018 that substantial sectoral asset concentrations exist in the financial, public and real estate sector, and find indicative evidence for a sectoral search for yield behavior. Based on a theoretical solvency capital allocation scheme, we demonstrate that the current regulatory approaches can lead to inappropriate and biased levels of solvency capital for asset concentration risk, and should be revised. Our findings have also important implications on the ongoing discussion of asset concentration risk in the context of macroprudential insurance regulation.
Gradient capital allocation, also known as Euler allocation, is a technique used to redistribute diversified capital requirements among different segments of a portfolio. The method is commonly employed to identify dominant risks, assessing the risk-adjusted profitability of segments, and installing limit systems. However, capital allocation can be misleading in all these applications because it only accounts for the current portfolio composition and ignores how diversification effects may change with a portfolio restructuring. This paper proposes enhancing the gradient capital allocation by adding “orthogonal convexity scenarios” (OCS). OCS identify risk concentrations that potentially drive portfolio risk and become relevant after restructuring. OCS have strong ties with principal component analysis (PCA), but they are a more general concept and compatible with common empirical patterns of risk drivers being fat-tailed and increasingly dependent in market downturns. We illustrate possible applications of OCS in terms of risk communication and risk limits.
Most insurers in the European Union determine their regulatory capital requirements based on the standard formula of Solvency II. However, there is evidence that the standard formula inaccurately reflects insurers’ risk situation and may provide misleading steering incentives. In the second pillar, Solvency II requires insurers to perform a so-called “Own Risk and Solvency Assessment” (ORSA). In their ORSA, insurers must establish their own risk measurement approaches, including those based on scenarios, in order to derive suitable risk assessments and address shortcomings of the standard formula. The idea of this paper is to identify scenarios in such a way that the standard formula in connection with the ORSA provides a reliable basis for risk management decisions. Using an innovative method for scenario identification, our approach allows for a simple but relatively precise assessment of marginal and even non-marginal portfolio changes. We numerically evaluate the proposed approach in the context of market risk employing an internal model from the academic literature and the Solvency Capital Requirement (SCR) calculation under Solvency II.
This paper investigates systemic risk in the insurance industry. We first analyze the systemic contribution of the insurance industry vis-à-vis other industries by applying 3 measures, namely the linear Granger causality test, conditional value at risk and marginal expected shortfall, on 3 groups, namely banks, insurers and non-financial companies listed in Europe over the last 14 years. We then analyze the determinants of the systemic risk contribution within the insurance industry by using balance sheet level data in a broader sample. Our evidence suggests that i) the insurance industry shows a persistent systemic relevance over time and plays a subordinate role in causing systemic risk compared to banks, and that ii) within the industry, those insurers which engage more in non-insurance-related activities tend to pose more systemic risk. In addition, we are among the first to provide empirical evidence on the role of diversification as potential determinant of systemic risk in the insurance industry. Finally, we confirm that size is also a significant driver of systemic risk, whereas price-to-book ratio and leverage display counterintuitive results.