G32 Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure
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Socially responsible investing (SRI) continues to gain momentum in the financial market space for various reasons, starting with the looming effect of climate change and the drive toward a net-zero economy. Existing SRI approaches have included environmental, social, and governance (ESG) criteria as a further dimension to portfolio selection, but these approaches focus on classical investors and do not account for specific aspects of insurance companies. In this paper, we consider the stock selection problem of life insurance companies. In addition to stock risk, our model set-up includes other important market risk categories of insurers, namely interest rate risk and credit risk. In line with common standards in insurance solvency regulation, such as Solvency II, we measure risk using the solvency ratio, i.e. the ratio of the insurer’s market-based equity capital to the Value-at-Risk of all modeled risk categories. As a consequence, we employ a modification of Markowitz’s Portfolio Selection Theory by choosing the “solvency ratio” as a downside risk measure to obtain a feasible set of optimal portfolios in a three-dimensional (risk, return, and ESG) capital allocation plane. We find that for a given solvency ratio, stock portfolios with a moderate ESG level can lead to a higher expected return than those with a low ESG level. A highly ambitious ESG level, however, reduces the expected return. Because of the specific nature of a life insurer’s business model, the impact of the ESG level on the expected return of life insurers can substantially differ from the corresponding impact for classical investors.
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.
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.
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.
In crisis times, insurance companies might feel the pressure to present an investment portfolio performance that is superior to the market, since investment portfolios back the claims of policyholders and serve as a signal for the claims’ safety. I investigate how a stock market crisis as experienced over the course of the Covid-19 pandemic influences insurance firms’ decisions on the allocation of their corporate bond portfolio. I find that insurers shift their portfolio holdings towards lower credit risk assets as financial market conditions tighten. This tendency seems to be restricted by the liquidity risk of high-yield assets, and the credit risk of lower-rated investment grade assets. Both effects lead to an increase in the fraction of less liquid assets during the crash and the recovery.
In times of crisis, insurance companies may invest into riskier assets to benefit from expected price recoveries. Using daily stock market data for 34 European insurers, I investigate how a stock market contraction, as experienced during the Covid-19 pandemic, affects insurers’ decision on the allocation of their corporate bond portfolio. I find that insurers shift their portfolio holdings pro-cyclically towards lower credit risk assets in the first month of the market contraction. As the crisis progresses, I find evidence for counter-cyclical investment behavior by insurers, which can neither be explained by credit rating downgrades of held bonds nor by hedging with CDS derivatives. The observed counter-cyclical investment behavior of insurers could be beneficial for the financial system in attenuating price declines, but excessive risk-taking by insurance companies over longer periods can also reinforce stress in the system.
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.