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Advances in distributed ledger technology are leading to a growing decentralisation of financial services (“decentralised finance”) that can be offered largely without intermediation by financial institutions. An important driver for this development is the ongoing tokenisation of assets, payments and rights, which enables the digital encryption of “crypto assets” on distributed ledgers. This article elaborates the foundations and fields of application of decentralised financial services with crypto assets that could challenge the established business models of financial institutions. This trend not only affects payment systems based on controversial crypto currencies such as Bitcoin, but also exchange platforms, capital markets solutions and corporate financing. A rapidly growing ecosystem of start-ups, tech companies and financial institutions is emerging, yet this ecosystem lacks a consistent regulatory framework. The European initiative MiCA (Markets in Crypto Assets) points in the right direction but needs to be adopted soon to ensure the future competitiveness of the European financial sector.
We extend the canonical income process with persistent and transitory risk to cyclical shock distributions with left-skewness and excess kurtosis. We estimate our income process by GMM for US household data. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The tax and transfer system reduces dispersion and left-skewness. We then show that in a standard incomplete-markets life-cycle model, first, higherorder risk has sizable welfare implications, which depend on risk attitudes; second, it matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, it has non-trivial implications for self-insurance against shocks.
We consider an additively time-separable life-cycle model for the family of power period utility functions u such that u0(c) = c−θ for resistance to inter-temporal substitution of θ > 0. The utility maximization problem over life-time consumption is dynamically inconsistent for almost all specifications of effective discount factors. Pollak (1968) shows that the savings behavior of a sophisticated agent and her naive counterpart is always identical for a logarithmic utility function (i.e., for θ = 1). As an extension of Pollak’s result we show that the sophisticated agent saves a greater (smaller) fraction of her wealth in every period than her naive counterpart whenever θ > 1 (θ < 1) irrespective of the specification of discount factors. We further show that this finding extends to an environment with risky returns and dynamically inconsistent Epstein-Zin-Weil preferences.
Using a structural life-cycle model and data on school visits from Safegraph and school closures from Burbio, we quantify the heterogeneous impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. Our data suggests that secondary schools were closed for in-person learning for longer periods than elementary schools (implying that younger children experienced less school closures than older children), and that private schools experienced shorter closures than public schools, and schools in poorer U.S. counties experienced shorter school closures. We then extend the structural life cycle model of private and public schooling investments studied in Fuchs-Schündeln, Krueger, Ludwig, and Popova (2021) to include the choice of parents whether to send their children to private schools, empirically discipline it with data on parental investments from the PSID, and then feed into the model the school closure measures from our empirical analysis to quantify the long-run consequences of the Covid-19 school closures on the cohorts of children currently in school. Future earnings- and welfare losses are largest for children that started public secondary schools at the onset of the Covid-19 crisis. Comparing children from the topto children from the bottom quartile of the income distribution, welfare losses are ca. 0.8 percentage points larger for the poorer children if school closures were unrelated to income. Accounting for the longer school closures in richer counties reduces this gap by about 1/3. A policy intervention that extends schools by 3 months (6 weeks in the next two summers) generates significant welfare gains for the children and raises future tax revenues approximately sufficient to pay for the cost of this schooling expansion.
Using a structural life-cycle model, we quantify the heterogeneous impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. In the model, public investment through schooling is combined with parental time and resource investments in the production of child human capital at different stages in the children’s development process. We quantitatively characterize the long-term consequences from a Covid-19 induced loss of schooling, and find average losses in the present discounted value of lifetime earnings of the affected children of close to 1%, as well as welfare losses equivalent to about 0.6% of permanent consumption. Due to self-productivity in the human capital production function, skill attainment at a younger stage of the life cycle raises skill attainment at later stages, and thus younger children are hurt more by the school closures than older children. We find that parental reactions reduce the negative impact of the school closures, but do not fully offset it. The negative impact of the crisis on children’s welfare is especially severe for those with parents with low educational attainment and low assets. The school closures themselves are primarily responsible for the negative impact of the Covid-19 shock on the long-run welfare of the children, with the pandemic-induced income shock to parents playing a secondary role.
We characterize the optimal linear tax on capital in an Overlapping Generations model with two period lived households facing uninsurable idiosyncratic labor income risk. The Ramsey government internalizes the general equilibrium effects of private precautionary saving on factor prices and taxes capital unless the weight on future generations in the social welfare function is sufficiently high. For logarithmic utility a complete analytical solution of the Ramsey problem exhibits an optimal aggregate saving rate that is independent of income risk, whereas the optimal time-invariant tax on capital implementing this saving rate is increasing in income risk. The optimal saving rate is constant along the transition and its sign depends on the magnitude of risk and on the Pareto weight of future generations. If the Ramsey tax rate that maximizes steady state utility is positive, then implementing this tax rate permanently induces a Pareto-improving transition even if the initial equilibrium capital stock is below the golden rule.
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
(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.