Self-selection and risk sharing in a modern world of lifelong annuities - Abstract of the London Discussion

This abstract relates to the following paper: Gerrard, R., Hiabu, M., Kyriakou, I. and Nielsen, J. P. (2018) Self-selection and risk sharing in a modern world of lifelong annuities ‐ Abstract of the London Discussion. British Actuarial Journal. Cambridge University Press, 23. doi: 10.1017/S135732171800020X

Self-selection and risk sharing in a modern world of life-long annuities

Communicating a pension product well is as important as optimising the financial value. In a recent study, we showed that up to 80% of the value of a pension lump sum could be lost if customer communication failed. In this paper, we extend the simple customer interaction of the earlier contribution to the more challenging lifetime annuity case. Using a simple mobile phone device, the pension customer can select the life-long optimal investment strategy within minutes. The financial risk trade-off is presented as a trade-off between the pension paid and the number of years the life-long annuity is guaranteed. The pension payment decreases when investment security increases. The necessary underlying mathematical financial hedging theory is included in the stud

Microwave-assisted hydrothermal synthesis of carbon monolith via a soft-template method using resorcinol and formaldehyde as carbon precursor and pluronic F127 as template

A new microwave-assisted hydrothermal synthesis of carbon monolith is reported in this work. The process uses microwave heating at 100 °C under acidic condition by employing a triblock copolymer F127 as the template, and resorcinol–formaldehyde as the carbon precursor. Scanning electron microscopy, Fourier transform infrared spectroscopy, nitrogen sorption measurements, transmission electron microscopy, X-ray studies and thermogravimetic analysis were used to characterize the synthesized material. The carbon monolith is crack-free, mesoporous and has a high surface area of 697 m²/g. The results demonstrate that the microwave-assisted hydrothermal synthesis is a fast and simple approach to obtain carbon monoliths, as it reduces effectively the synthesis time from hours to a few minutes which could be an advantage in the large scale production of the material

Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models

The relationship between the prices of crude oil and its refined products is at the heart of the oil industry. Crude oil and refined products volatilities and correlations have been mod- elled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using fractionally integrated multivariate GARCH models from a fore- casting and a risk management perspective. Several models for the spot returns on three major oil-related markets are compared. In-sample results show significant evidence of long-memory decay and leverage effects in volatilities and of time-varying autocorrelations. The forecasting performance of the models is assessed by means of three approaches: the Superior Predictive Ability test, the Model Confidence Set and the Value-at-Risk. The results indicate that the multivariate models incorporating long-memory outperform the short-memory benchmarks in forecasting the conditional covariance matrix and associated risk magnitudes. The paper makes an innovative contribution to the analysis of the relationship between crude oil and its refined products providing refiners, physical oil traders, non-commercial oil traders and other energy markets agents with significant insights for hedging and risk management operations

Forecasting benchmarks of long-term stock returns via machine learning

Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners

Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case

Long-term return expectations or predictions play an important role in planning purposes and guidance of long-term investors. Five-year stock returns are less volatile around their geometric mean than returns of higher frequency, such as one-year returns. One would, therefore, expect models using the latter to better reduce the noise and beat the simple historical mean than models based on the former. However, this paper shows that the general tendency is surprisingly the opposite: long-term forecasts over five years have a similar or even better predictive power when compared to the one-year case. We consider a long list of economic predictors and benchmarks relevant for the long-term investor. Our predictive approach consists of adopting and implementing a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. We consistently find that long-term forecasting performs well and recommend drawing more attention to it when designing investment strategies for long-term investors. Furthermore, our preferred predictive model did stand the test of Covid-19 providing a relatively optimistic outlook in March 2020 when uncertainty was all around us with lockdown and facing an unknown new pandemic

Site-Dilution in quasi one-dimensional antiferromagnet Sr2(Cu1-xPdx)O3: reduction of Neel Temperature and spatial distribution of ordered moment sizes

We investigate the Neel temperature of Sr2CuO3 as a function of the site dilution at the Cu (S=1/2) sites with Pd (S=0), utilizing the muon spin relaxation (muSR) technique. The Neel temperature, which is Tn=5.4K for the undoped system, becomes significantly reduced for less than one percent of doping Pd, giving a support for the previous proposal for the good one-dimensionality. The Pd concentration dependence of the Neel temperature is compared with a recent theoretical study (S. Eggert, I. Affleck and M.D.P. Horton, Phys. Rev. Lett. 89, 47202 (2002)) of weakly coupled one-dimensional antiferromagnetic chains of S=1/2 spins, and a quantitative agreement is found. The inhomogeneity of the ordered moment sizes is characterized by the muSR time spectra. We propose a model that the ordered moment size recovers away from the dopant S=0 sites with a recovery length of \xi = 150-200 sites. The origin of the finite recovery length \xi for the gapless S=1/2 antiferromagnetic chain is compared to the estimate based on the effective staggered magnetic field from the neighboring chains.Comment: 10 pages, 9 figures, submitted to PR

Muon Spin Relaxation and Susceptibility Studies of Pure and Doped Spin 1/2 Kagom\'{e}-like system (Cux_xZn1−x_{1-x})3_{3}V2_{2}O7_7(OH)2_{2} 2H2_2O

Muon spin relaxation ($\mu$SR) and magnetic susceptibility measurements have been performed on the pure and diluted spin 1/2 kagom\'{e} system (Cu$_x$Zn$_{1-x}$)$_{3}$V$_{2}$O$_7$(OH)$_{2}$ 2H$_2$O. In the pure $x=1$ system we found a slowing down of Cu spin fluctuations with decreasing temperature towards $T \sim 1$ K, followed by slow and nearly temperature-independent spin fluctuations persisting down to $T$ = 50 mK, indicative of quantum fluctuations. No indication of static spin freezing was detected in either of the pure ($x$=1.0) or diluted samples. The observed magnitude of fluctuating fields indicates that the slow spin fluctuations represent an intrinsic property of kagom\'e network rather than impurity spins.Comment: 4 pges, 4 color figures, Phys. Rev. Lett. in pres

Superconductivity and Field-Induced Magnetism in Pr2−x_{2-x}Cex_xCuO4_4 Single Crystals

We report muon-spin rotation/relaxation (muSR) measurements on single crystals of the electron-doped high-T_c superconductor Pr$_{2-x}$Ce$_x$CuO$_4$. In zero external magnetic field, superconductivity is found to coexist with Cu spins that are static on the muSR time scale. In an applied field, we observe a Knight shift that is primarily due to the magnetic moment induced on the Pr ions. Below the superconducting transition temperature T_c, an additional source of static magnetic order appears throughout the sample. This finding is consistent with antiferromagnetic ordering of the Cu spins in the presence of vortices. We also find that the temperature dependence of the in-plane magnetic penetration depth in the vortex state resembles that of the hole-doped cuprates at temperatures above ~ 0.2 T_c.Comment: 4 pages, 5 figure

Enhancing the Electrocatalytic Activity of Redox Stable Perovskite Fuel Electrodes in Solid Oxide Cells by Atomic Layer-Deposited Pt Nanoparticles

The carbon dioxide and steam co-electrolysis in solid oxide cells offers an efficient way to store the intermittent renewable electricity in the form of syngas (CO + H2), which constitutes a key intermediate for the chemical industry. The co-electrolysis process, however, is challenging in terms of materials selection. The cell composites, and particularly the fuel electrode, are required to exhibit adequate stability in redox environments and coking that rules out the conventional Ni cermets. La0.75Sr0.25Cr0.5Mn0.5O3 (LSCrM) perovskite oxides represent a promising alternative solution, but with electrocatalytic activity inferior to the conventional Ni-based cermets. Here, we report on how the electrochemical properties of a state-of-the-art LSCrM electrode can be significantly enhanced by introducing uniformly distributed Pt nanoparticles (18 nm) on its surface via the atomic layer deposition (ALD). At 850 °C, Pt nanoparticle deposition resulted in a ∼62% increase of the syngas production rate during electrolysis mode (at 1.5 V), whereas the power output was improved by ∼84% at fuel cell mode. Our results exemplify how the powerful ALD approach can be employed to uniformly disperse small amounts (∼50 μg·cm–2) of highly active metals to boost the limited electrocatalytic properties of redox stable perovskite fuel electrodes with efficient material utilization.</p