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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.
This work proposes to employ the (bursty) GLO model from Bingmer et. al (2011) to model the occurrence of tropical cyclones. We develop a Bayesian framework to estimate the parameters of the model and, particularly, employ a Markov chain Monte Carlo algorithm. This also allows us to develop a forecasting framework for future events.
Moreover, we assess the default probability of an insurance company that is exposed to claims that occur according to a GLO process and show that the model is able to substantially improve actuarial risk management if events occur in oscillatory bursts.
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.
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.
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.
This paper investigates the effects of a rise in interest rate and lapse risk of endowment life insurance policies on the liquidity and solvency of life insurers. We model the book and market value balance sheet of an average German life insurer, subject to both GAAP and Solvency II regulation, featuring an existing back book of policies and an existing asset allocation calibrated by historical data. The balance sheet is then projected forward under stochastic financial markets. Lapse rates are modeled stochastically and depend on the granted guaranteed rate of return and prevailing level of interest rates. Our results suggest that in the case of a sharp increase in interest rates, policyholders sharply increase lapses and the solvency position of the insurer deteriorates in the short-run. This result is particularly driven by the interaction between a reduction in the market value of assets, large guarantees for existing policies, and a very slow adjustment of asset returns to interest rates. A sharp or gradual rise in interest rates is associated with substantial and persistent liquidity needs, that are particularly driven by lapse rates.
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.
Macro-finance theory predicts that financial fragility builds up when volatility is low. This “volatility paradox’” challenges traditional systemic risk measures. I explore a new dimension of systemic risk, spillover persistence, which is the average time horizon at which a firm’s losses increase future risk in the financial system. Using firm-level data covering more than 30 years and 50 countries, I document that persistence declines when fragility builds up: before crises, during stock market booms, and when banks take more risks. In contrast, persistence increases with loss amplification: during crises and fire sales. These findings support key predictions of recent macrofinance models.
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.
Common systemic risk measures focus on the instantaneous occurrence of triggering and systemic events. However, systemic events may also occur with a time-lag to the triggering event. To study this contagion period and the resulting persistence of institutions' systemic risk we develop and employ the Conditional Shortfall Probability (CoSP), which is the likelihood that a systemic market event occurs with a specific time-lag to the triggering event. Based on CoSP we propose two aggregate systemic risk measures, namely the Aggregate Excess CoSP and the CoSP-weighted time-lag, that reflect the systemic risk aggregated over time and average time-lag of an institution's triggering event, respectively. Our empirical results show that 15% of the financial companies in our sample are significantly systemically important with respect to the financial sector, while 27% of the financial companies are significantly systemically important with respect to the American non-financial sector. Still, the aggregate systemic risk of systemically important institutions is larger with respect to the financial market than with respect to non-financial markets. Moreover, the aggregate systemic risk of insurance companies is similar to the systemic risk of banks, while insurers are also exposed to the largest aggregate systemic risk among the financial sector.