Performance Analysis of Equity Index Universal Life Insurance

Equity Index Universal Life (EIUL) is a variation of whole life insurance which carries a death benefit component and a cash value component. The popularity of EIUL has been growing quickly in recent years because of its promised downside protection and upside potential. However, its long-term viability can’t be directly verified since no public data of EIUL is available to check its historical performance. This paper models the stock market return, interest rate and other market variables simultaneously to simulate the potential future returns of EIUL under different scenarios. Research in the literature often only models the stock market return and ignore the other market variables completely or fix their values in simulations. This research believes market variables such as the interest rate and credit spread will play an important part in analyzing the EIUL performance. The viability analysis is then applied to policy holders with different ages, tax brackets and financial goals. With the same initial capital, EIUL performance is compared with combination of stock index investment and a term-life policy

A connection between the stochastic heat equation and fractional Brownian motion, and a simple proof of a result of Talagrand

We give a new representation of fractional Brownian motion with Hurst parameter H<=1/2 using stochastic partial differential equations. This representation allows us to use the Markov property and time reversal, tools which are not usually available for fractional Brownian motion. We then give simple proofs that fractional Brownian motion does not hit points in the critical dimension, and that it does not have double points in the critical dimension. These facts were already known, but our proofs are quite simple and use some ideas of Levy

The Study of Protein Conformation in Solution Via Direct Sampling by Desorption Electrospray Ionization Mass Spectrometry

The direct sampling feature of liquid sample desorption electrospray ionization (DESI) allows the ionization of liquid samples without adding acids/organic solvents (i.e., without sample pretreatment). As a result, it provides a new approach for probing protein conformation in solution. In this study, it has been observed that native protein ions are generated from proteins in water by DESI. Interestingly, the intensities of the resulting protein ions appear to be higher than those generated by ESI of the proteins in water or in ammonium acetate. For protein solutions that already contain acids/organic solvents, DESI can be used to investigate the influences of these denaturants on protein conformations and the obtained results are in good agreement with spectroscopic data. In addition, online monitoring of protein conformational changes by DESI is feasible; for instance, heat-induced unfolding of ubiquitin can be traced with DESI in water without influences of organic solvents/acids. This DESI method provides a new alternative tool for the study of protein conformation in solution

Fixed Index Annuity Return and Risk Analysis with an Enhanced Model

This paper examines the risk and return of fixed index annuity (FIA) with an enhanced model framework which takes into account correlations among market variables and a changing participation rate fluctuating with the market moves. The FIA business model is discussed to explain the participation rate model. Sensitivity analysis of FIA returns is performed for key model assumptions. The risk and return profile of the mix (30/70, 40/60, 50/50, 100/0) of the S&P 500 Index and the 1-year Treasury bills is compared with that of the FIAs. This study focuses on 2 hypothetical contracts: (10-year) annual reset Point-to-Point (PTP) and Monthly-Averaging (MA) contracts with participation rate but no cap or yield spread. PTPs outperformed MAs. They together outperformed the mixes of stock/treasury with comparable risk. Simulated stock index shows higher returns than FIAs most of the time, but FIAs has much less volatilities and much lower tail risk

1988 DWC Membership and Mailing Lists

Membership list of DWC members, 198

Residual-based Language Models are Free Boosters for Biomedical Imaging

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative encoder layer for the direct processing of visual tokens. This strategy represents a significant departure from the standard multi-modal vision-language frameworks, which typically hinge on language-driven prompts and inputs. We found that these LLMs could boost performance across a spectrum of biomedical imaging applications, including both 2D and 3D visual classification tasks, serving as plug-and-play boosters. More interestingly, as a byproduct, we found that the proposed framework achieved superior performance, setting new state-of-the-art results on extensive, standardized datasets in MedMNIST-2D and 3D. Through this work, we aim to open new avenues for employing LLMs in biomedical imaging and enriching the understanding of their potential in this specialized domain