Robustly Hedging Variable Annuities with Guarantees Under Jump and Volatility Risks

Accurately quantifying and robustly hedging options embedded in the guarantees of variable annuities is a crucial task for insurance companies in preventing excessive liabilities. Due to sensitivities of the benefits to tails of the account value distribution, a simple Black-Scholes model is inadequate. A model which realistically describes the real world price dynamics over a long time horizon is essential for the risk management of the variable annuities. In this paper, both jump risk and volatility risk are considered for risk management of lookback options embedded in guarantees with a ratchet feature. We evaluate relative performances of delta hedging and dynamic discrete risk minimization hedging strategies. Using the underlying as the hedging instrument, we show that, under a Black-Scholes model, local risk minimization hedging is significantly better than delta hedging. In addition, we compare risk minimization hedging using the underlying with that of using standard options. We demonstrate that, under a Merton's jump diffusion model, hedging using standard options is superior to hedging using the underlying in terms of the risk reduction. Finally we consider a market model for volatility risks in which the at-the-money implied volatility is a state variable. We compute risk minimization hedging by modeling at-the-money Black-Scholes implied volatility explicitly; the hedging effectiveness is evaluated, however, under a joint underlying and implied volatility model which also includes instantaneous volatility risk. Our computational results suggest that, when implied volatility risk is suitably modeled, risk minimization hedging using standard options, compared to hedging using the underlying, can potentially be more effective in risk reduction under both jump and volatility risks

Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

BACKGROUND: It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set. RESULTS: We found that cross-validated performances systematically overestimated performance on the blind set. This was found not to be due to the presence of similar peptides in the cross-validation dataset. Rather, we found that small size and low sequence/affinity diversity of either training or blind datasets were associated with large differences in cross-validated vs. blind prediction performances. We use these findings to derive quantitative rules of how large and diverse datasets need to be to provide generalizable performance estimates. CONCLUSION: It has long been known that cross-validated prediction performance estimates often overestimate performance on independently generated blind set data. We here identify and quantify the specific factors contributing to this effect for MHC-I binding predictions. An increasing number of peptides for which MHC binding affinities are measured experimentally have been selected based on binding predictions and thus are less diverse than historic datasets sampling the entire sequence and affinity space, making them more difficult benchmark data sets. This has to be taken into account when comparing performance metrics between different benchmarks, and when deriving error estimates for predictions based on benchmark performance.Fil: Kim, Yohan. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sidney, John. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Buus, Søren. Universidad de Copenhagen; DinamarcaFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unido

User centric cloud service model in public sectors: Policy implications of cloud services

This study examines the acceptance of cloud computing services in government agencies by focusing on the key characteristics that affect behavioral intent. The study expanded upon the technology acceptance model by incorporating contextual factors such as availability, access, security, and reliability. The research model was empirically verified by investigating the perception of users working in public institutions. Modeling results showed that user intentions and behaviors were largely influenced by the perceived features of cloud services. Also these features were found to be the significant antecedents of cloud computing usefulness and ease of use. The findings should guide governments' promotion of cloud public services to increase user awareness by enhancing usability and appeal and ensuring security

Cell morphology drives spatial patterning in microbial communities

The clearest phenotypic characteristic of microbial cells is their shape, but we do not understand how cell shape affects the dense communities, known as biofilms, where many microbes live. Here, we use individual-based modeling to systematically vary cell shape and study its impact in simulated communities. We compete cells with different cell morphologies under a range of conditions and ask how shape affects the patterning and evolutionary fitness of cells within a community. Our models predict that cell shape will strongly influence the fate of a cell lineage: we describe a mechanism through which coccal (round) cells rise to the upper surface of a community, leading to a strong spatial structuring that can be critical for fitness. We test our predictions experimentally using strains of Escherichia coli that grow at a similar rate but differ in cell shape due to single amino acid changes in the actin homolog MreB. As predicted by our model, cell types strongly sort by shape, with round cells at the top of the colony and rod cells dominating the basal surface and edges. Our work suggests that cell morphology has a strong impact within microbial communities and may offer new ways to engineer the structure of synthetic communities

A socio-technical framework for Internet-of-Things design

This study presents a case application of a socio-technical framework to assess and predict the development of the Internet of Things (IoT) in Korea. Applying a socio-technical system approach to the IoT, this paper seeks a clear understanding of how the IoT will evolve and stabilize in a smart environment. It investigates the complex interaction between social and technical aspects of the IoT, by highlighting the co-evolution, interaction, and interface, which constitute the next generation network environment. It describes the challenges in designing, deploying, and sustaining the diverse components of the IoT, and provides a snapshot of Korea's current approach to meeting this challenge. Finally, the findings of this study provide insights into these challenges and opportunities, by offering a socio-technical analysis of IoT development. The insights help to conceptualize how the IoT can be designed and situated within human-centered contexts

Automated benchmarking of peptide-MHC class I binding predictions

Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto-bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto-bench/mhci/join.Fil: Trolle, Thomas. Technical University of Denmark; DinamarcaFil: Metushi, Imir G.. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Greenbaum, Jason A.. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Kim, Yohan. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sidney, John. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Lund, Ole. Technical University of Denmark; DinamarcaFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentin

Studies on Changes in Ruminal pH and Microbiota, and Epithelial Transcriptomic Adaptation in Weaning Calves

岐阜大学(Gifu University)博士(獣医学)博士論文 (Doctoral dissertation)doctoral thesi

The effect of environmental factors on accounting systems: a comparison between South Korea and Australia

As globalization of the business environment increases, cross-national accounting differences have been the main focus of international accounting research. Specifically, the relationship between accounting and environmental factors has been the subject of many debates over the last decade. In comparative studies of accounting history, culture and practices, researchers have become increasingly aware of the importance of environmental factors in shaping a country's accounting system. This study explores whether environmental factors influence accounting systems by comparing South Korea and Australia. Although both South Korea and Australia were colonies, they had different cultural backgrounds and different legal/judicial systems. Australia's accounting standards-setting process is based on business practices and is relatively open to public opinion, while Korea's accounting standards tend to be enforced by the government and harmonized to tax law. Many prior studies suggest that environmental factors can be a valuable tool in explaining and understanding differences in the way in which accounting operates in countries with different environments. Based on environmental factors, this study found that cultural factors and institutional structures have a significant influence on the development of accounting systems and cause their differences. This study is expected to provide a systematic framework for differences in the development of accounting systems by analyzing the effects of environmental factors on accounting systems

URANS Computations of Cavitating Flow around a 2-D Wedge by Compressible Two-Phase Flow Solver

This paper deals with the computation of unsteady cavitating flow around a twodimensional wedge by using Unsteady Reynolds Averaged Navier-Stokes (URANS) flow solver. Because of accuracy deterioration problem due to excessive numerical dissipations for low Mach number unsteady flow, properly scaled RoeM and AUSMPW+ numerical flux schemes are used to accurately compute unsteady cavitating flow. Fast Fourier Transform (FFT) analysis results of experiments and computations are compared to show similar dominant frequencies of shedding vortices. Shedding pattern and location of vortices are also compared to show similar behavior of each flow result.OAIID:RECH_ACHV_DSTSH_NO:A201606327RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001138CITE_RATE:FILENAME:M2J.1.AS733_1588F1.pdfDEPT_NM:기계항공공학부EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/d3e93894-8ef1-4bce-9dfe-e2c270ced9d8/linkCONFIRM: