This is a summary of the main topics and findings from the Swiss Risk and Insurance Forum 2015. That event gathered experts from academia, insurance industry, regulatory bodies, and consulting companies to discuss the past and current developments and necessary next steps for dealing with old-age provision. Topics include the pension funding gap, demographic and societal challenges, the valuation of pension liabilities, economic and regulatory capital models, and the role of financial markets.
We provide novel evidence on the role of income taxes for housing rents and spatial sorting. Drawing on comprehensive micro-level data, we estimate the responsiveness of households to tax differentials across municipal boundaries. Correcting for unobservable location characteristics and isolating the residential sorting component, we identify an income tax elasticity of rents of about −0.27 to −0.35. In line with non-homothetic preferences, we find that the marginal willingness to pay for lower taxes increases with income. Counterfactual calculations show how an homogenisation of taxes across jurisdictions and an increase in variation of taxes affect rents and income stratification across space.
We show in this paper that after proper scalings, the characteristic polynomial of a random unitary matrix converges to a random analytic function whose zeros, which are on the real line, form a determinantal point process with sine kernel. Our scaling is performed at the so-called “microscopic” level, that is we consider the characteristic polynomial at points whose distance to $1$ has order $1 / n$. We prove that the rescaled characteristic polynomial does not even have a moment of order one, hence making the classical techniques of random matrix theory difficult to apply. In order to deal with this issue, we couple all the dimensions $n$ on a single probability space, in such a way that almost sure convergence occurs when $n$ goes to infinity. The strong convergence results in this setup provide us with a new approach to ratios: we are able to solve open problems about the limiting distribution of ratios of characteristic polynomials evaluated at points of the form exp($2i\pi\alpha/n$) and related objects (such as the logarithmic derivative). We also explicitly describe the dependence relation for the logarithm of the characteristic polynomial evaluated at several points on the microscopic scale. On the number theory side, inspired by the work by Keating and Snaith, we conjecture some new limit theorems for the value distribution of the Riemann zeta function on the critical line at the level of stochastic processes.
Die Autorinnen schreiben den Akteuren der Mikrofinanzidustrie eine besonder Verantwortung für uhre Kunden, die ärmeren Bevölkerungsschichten, zu. Anders als in der konventionellen Finanzindustrie sind Mikrofinanzkunden in besonderem Masse verwundbar, vulnerabel. Diese Besonderheit rechtfertigt es, hohe Anforderungen an den Kundenschutz zu stellen. Ein solcher scheint zusätzlich geboten aufgrund der "dualen Zielsetzung", die Finanzintermediäre meist explizit verfolgen: eine positive finanzielle und gesellschaftliche Wirkung.
Die hohe Verantwortung von Investoren und Finanzintermediären sollte in der Praxis institutionell abgestützt sein. Freiwillige Selbstverpflichtungen gehören ebenso dazu wie ein hohes Mass an Transparenz, die Wirkungsmessung in der Zielerreichung ebenso wie die verbindliche Umsetzung von Kundenschutzprinzipien und eine standardisierte Berichterstattung.
Die Mikrofinanzindustrie hat sich bisher als lernfähig erwiesen; dieser Lernprozess kommt den vulnerable Kunden zugute und einer verantwortbaren Zukunft der Mikrofinanzindustrie als Ganzes.
We propose a numerical optimization approach that can be used to solve portfolio selection problems including several assets and involving objective functions from cumulative prospect theory (CPT). Implementing the suggested algorithm, we compare asset allocations that are derived for CPT based on two different methods: maximizing CPT along the mean–variance efficient frontier so that simple mean–variance algorithms can be used, and maximizing CPT without this restriction. According to the theoretical literature, with normally distributed returns and unlimited short sales, these two approaches lead to the same optimal solutions. We find that for empirical finite discrete distributions obtained via sampling and subsequent clustering from a normal distribution, the difference between the two approaches remains negligible even if short sales are restricted. However, if standard asset allocation data for pension funds is considered, the difference is considerable. Moreover, for certain types of derivatives, such as call options, the restriction of asset allocations to the mean–variance efficient frontier produces sizable losses in various respects, including decreases in expected returns and expected utility. We are able to explain these differences by CPT’s preference for positive skewness, which is not accounted for by optimizing CPT along the mean–variance efficient frontier.
