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Bending rigidity, k, is classically measured for lipid membranes to characterize their nanoscale mechanical properties as a function of composition. Widely employed as a comparative tool, it helps understanding the relationship between the lipid's molecular structure and the elastic properties of its corresponding bilayer. Widely measured for phospholipid membranes in the shape of giant unilamellar vesicles (GUVs), bending rigidity is determined here for three self-assembled structures formed by a new biobased glucolipid bioamphiphile, rather associated to the family of glycolipid biosurfactants than phospholipids. In its oleyl form, glucolipid G-C18:1 can assemble into vesicles or crystalline fibers, while in its stearyl form, glucolipid G-C18:0 can assemble into lamellar gels. Neutron spin-echo (NSE) is employed in the q-range between 0.3 nm-1 (21 nm) and 1.5 nm-1 (4.1 nm) with a spin-echo time in the range of up to 500 ns to characterize the bending rigidity of three different structures (Vesicle suspension, Lamellar gel, Fiber gel) solely composed of a single glucolipid. The low (k= 0.30 $\pm$ 0.04 kbT) values found for the Vesicle suspension and high values found for the Lamellar (k= 130 $\pm$ 40 kbT) and Fiber gels (k= 900 $\pm$ 500 kbT) are unusual when compared to most phospholipid membranes. By attempting to quantify for the first time the bending rigidity of self-assembled bioamphiphiles, this work not only contributes to the fundamental understanding of these new molecular systems, but it also opens new perspectives in their integration in the field of soft materials.

Models for spin systems known from statistical physics are applied by analogy in econometrics in the form of agent-based models. Researchers suggest that the state variable temperature $T$ corresponds to volatility $\sigma$ in capital market theory problems. To the best of our knowledge, this has not yet been theoretically derived, for example, for an ideal agent system. In the present paper, we derive the exact algebraic relation between $T$ and $\sigma$ for an ideal agent system and discuss implications and limitations., Comment: Theoretical Contribution

Historically, Kennard was the first to choose the standard deviation as a quantitative measure of uncertainty, and neither he nor Heisenberg explicitly explained why this choice should be appropriate from the experimental physical point of view. If a particle is prepared by a single slit of spatial width $\Delta x$, it has been shown that a finite standard deviation $\sigma_p<\infty$ can only be ensured if the wave-function is zero at the edge of $\Delta x$, otherwise it does not exist [8]. Under this circumstances the corresponding sharp inequality is $\sigma_p \Delta x\geq \pi\hbar$. This bound will be reconsidered from the mathematical point of view in terms of a variational problem in Hilbert space and will furthermore be tested in a 4f-single slit diffraction experiment of a laser beam. Our results will be compared with a laser-experiment recently given by Fern\'andez-Guasti (2022) [9]., Comment: 7 pages, 4 figures

Protein dynamics has been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how local fluctuations can lead to global allosteric changes. Here we show that molecule-spanning dynamics on the 100 ns time scale precede larger allosteric changes. We assign global real-space movements to dynamic modes on the 100 ns time scales, which became possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom MD simulations. Additionally, we demonstrate the effect of Sba1, a co-chaperone of Hsp90, on these molecule-spanning dynamics, which implies functional importance of such dynamics. Our integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein and indicates that such dynamics are the molecular basis for allostery and large conformational changes in proteins., Comment: 29 main text pages, 7 main text figures; 27 SI pages, 15 SI figures

This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics., Comment: 33 pages, 3 Figures, 11 Tables JEL Classification: C12, C13, C43, E22

Cryptocurrencies (CCs) become more interesting for institutional investors' strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition., Comment: 16 Pages, 2 Figures, 3 Tables, JEL Classification: C14, C46, C55, E22, G10

In this paper, we investigate whether mixing cryptocurrencies to a German investor portfolio improves portfolio diversification. We analyse this research question by applying a (mean variance) portfolio analysis using a toolbox consisting of (i) the comparison of descriptive statistics, (ii) graphical methods and (iii) econometric spanning tests. In contrast to most of the former studies we use a (broad) customized, Equally-Weighted Cryptocurrency Index (EWCI) to capture the average development of a whole ex ante defined cryptocurrency universe and to mitigate possible survivorship biases in the data. According to Glas/Poddig (2018), this bias could have led to misleading results in some already existing studies. We find that cryptocurrencies can improve portfolio diversification in a few of the analyzed windows from our dataset (consisting of weekly observations from 2014-01-01 to 2019-05-31). However, we cannot confirm this pattern as the normal case. By including cryptocurrencies in their portfolios, investors predominantly cannot reach a significantly higher efficient frontier. These results also hold, if the non-normality of cryptocurrency returns is considered. Moreover, we control for changes of the results, if transaction costs/illiquidities on the cryptocurrency market are additionally considered., Comment: 59 pages, 20 figures. JEL Classification: C12, C13, C32, E22, G11

Attempts to allocate capital across a selection of different investments are often hampered by the fact that investors' decisions are made under limited information (no historical return data) and during an extremely limited timeframe. Nevertheless, in some cases, rational investors with a certain level of experience are able to ordinally rank investment alternatives through relative assessments of the probabilities that investments will be successful. However, to apply traditional portfolio optimization models, analysts must use historical (or simulated/expected) return data as the basis for their calculations. This paper develops an alternative portfolio optimization framework that is able to handle this kind of information (given by an ordinal ranking of investment alternatives) and to calculate an optimal capital allocation based on a Cobb-Douglas function, which we call the Sorted Weighted Portfolio (SWP). Considering risk-neutral investors, we show that the results of this portfolio optimization model usually outperform the output generated by the (intuitive) Equally Weighted Portfolio (EWP) of different investment alternatives, which is the result of optimization when one is unable to incorporate additional data (the ordinal ranking of the alternatives). To further extend this work, we show that our model can also address risk-averse investors to capture correlation effects., Comment: 22 pages, 2 Figure, 2 Tables

In risk management, tail risks are of crucial importance. The assessment of risks should be carried out in accordance with the regulatory authority's requirement at high quantiles. In general, the underlying distribution function is unknown, the database is sparse, and therefore special tail models are used. Very often, the generalized Pareto distribution is employed as a basic model, and its parameters are determined with data from the tail area. With the determined tail model, statisticians then calculate the required high quantiles. In this context, we consider the possible accuracy of the calculation of the quantiles and determine the finite sample distribution function of the quantile estimator, depending on the confidence level and the parameters of the tail model, and then calculate the finite sample bias and the finite sample variance of the quantile estimator. Finally, we present an impact analysis on the quantiles of an unknown distribution function., Comment: 3 figures

This paper contributes to answering a question that is of crucial importance in risk management and extreme value theory: How to select the threshold above which one assumes that the tail of a distribution follows a generalized Pareto distribution. This question has gained increasing attention, particularly in finance institutions, as the recent regulative norms require the assessment of risk at high quantiles. Recent methods answer this question by multiple uses of the standard goodness-of-fit tests. These tests are based on a particular choice of symmetric weighting of the mean square error between the empirical and the fitted tail distributions. Assuming an asymmetric weighting, which rates high quantiles more than small ones, we propose new goodness-of-fit tests and automated threshold selection procedures. We consider a parameterized family of asymmetric weight functions and calculate the corresponding mean square error as a loss function. We then explicitly determine the risk function as the finite sample expected value of the loss function. Finally, the risk function can be used to discuss the question of which symmetric or asymmetric weight function and, thus, which goodness-of-fit test should be used in a new method for determining the threshold value., Comment: 1 Figure. arXiv admin note: text overlap with arXiv:1805.10040

In risk management, tail risks are of crucial importance. The quality of a tail model, which is determined by data from an unknown distribution, depends critically on the subset of data used to model the tail. Based on a suitably weighted mean square error, we present a method that can separate the required subset. The selected data are used to determine the parameters of the tail model. Notably, no parameter specifications have to be made to apply the proposed procedure. Standard goodness of fit tests allow us to evaluate the quality of the fitted tail model. We apply the method to standard distributions that are usually considered in the finance and insurance industries. In addition, for the MSCI World Index, we use historical data to identify the tail model and to compute the quantiles required for a risk assessment., Comment: 38 pages, 12 figures

In general, underestimation of risk is something which should be avoided as far as possible. Especially in financial asset management, equity risk is typically characterized by the measure of portfolio variance, or indirectly by quantities which are derived from it. Since there is a linear dependency of the variance and the empirical correlation between asset classes, one is compelled to control or to avoid the possibility of underestimating correlation coefficients. In the present approach, we formalize common practice and classify these approaches by computing their probability of underestimation. In addition, we introduce a new estimator which is characterized by having the advantage of a constant and controllable probability of underestimation. We prove that the new estimator is statistically consistent., Comment: 5 pages, 6 figures, working paper

We consider particles prepared by the von Neumann-L\"uders projection. For those particles the standard deviation of the momentum is discussed. We show that infinite standard deviations are not exceptions but rather typical. A necessary and sufficient condition for finite standard deviations is given. Finally, a new uncertainty relation is derived and it is shown that the latter cannot be improved., Comment: 3 pages, introduction shortened, content unchanged

In the present work we investigate a new type of billiards defined inside n-simplex regions. We determine an invariant ergodic (SRB) measure of the dynamics for any dimension. In using symbolic dynamics the (KS or metric) entropy is computed and we find that the system is chaotic for all cases n > 2., Comment: 7 pages, 1 figures

In the present work we investigate a new type of billiards defined inside of $n$--simplex regions. We determine an invariant ergodic (SRB) measure of the dynamics for any dimension. In using symbolic dynamics, the (KS or metric) entropy is computed and we find that the system is chaotic for all cases $n>2$., Comment: 8 pages, uuencoded compressed postscript file