Contingent convertible bonds with the default risk premium

Contingent convertible bonds (CoCos) are hybrid instruments characterized by both debt and equity. CoCos are automatically converted into equity or written down when a predefined trigger event occurs. The present study quantifies the issuing bank's default risk that only manifests in the post-conversion period for pricing CoCos depending on a loss-absorbing method. This work aims to reflect the distinct features of equity-conversion CoCos - in contrast to a write-down CoCos - in a valuation framework. Accordingly, we propose a model to compute the ratio of common equity Tier 1 (CET1), which is composed of core capital and risky assets, by employing a geometric Brownian motion and a random variable. Then, we formulate the post-conversion risk premium by measuring the probability with which the bank's CET1 ratio breaches a regulatory default threshold after conversion. Finally, we empirically examine a positive value of the post-conversion risk premium embedded in the market prices of equity-conversion CoCos

Optimal Investment, Heterogeneous Consumption and Best Time for Retirement

This paper studies an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The utility for luxury goods is not necessarily a concave function. The optimal heterogeneous consumption strategies for a class of non-homothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all in luxury goods when the wealth is large. The optimal retirement policy is shown to be both universal, in the sense that all individuals should retire at the same level of marginal utility that is determined only by income, labor cost, discount factor as well as market parameters, and not universal, in the sense that all individuals can achieve the same marginal utility with different utility and wealth. It is also shown that individuals prefer to retire as time goes by if the marginal labor cost increases faster than that of income. The main tools used in analyzing the problem are from PDE and stochastic control theory including variational inequality and dual transformation. We finally conduct the simulation analysis for the featured model parameters to investigate practical and economic implications by providing their figures

PPM1A Controls Diabetic Gene Programming through Directly Dephosphorylating PPAR?? at Ser273

Peroxisome proliferator-activated receptor gamma (PPAR gamma) is a master regulator of adipose tissue biology. In obesity, phosphorylation of PPAR gamma at Ser273 (pSer273) by cyclin-dependent kinase 5 (CDK5)/extracellular signal-regulated kinase (ERK) orchestrates diabetic gene reprogramming via dysregulation of specific gene expression. Although many recent studies have focused on the development of non-classical agonist drugs that inhibit the phosphorylation of PPAR gamma at Ser273, the molecular mechanism of PPAR gamma dephosphorylation at Ser273 is not well characterized. Here, we report that protein phosphatase Mg2+/Mn2+-dependent 1A (PPM1A) is a novel PPAR gamma phosphatase that directly dephosphorylates Ser273 and restores diabetic gene expression which is dysregulated by pSer273. The expression of PPM1A significantly decreases in two models of insulin resistance: diet-induced obese (DIO) mice and db/db mice, in which it negatively correlates with pSer273. Transcriptomic analysis using microarray and genotype-tissue expression (GTEx) data in humans shows positive correlations between PPM1A and most of the genes that are dysregulated by pSer273. These findings suggest that PPM1A dephosphorylates PPAR gamma at Ser273 and represents a potential target for the treatment of obesity-linked metabolic disorders

Between Neoliberalism and Democracy : The Transformation of the Developmental State in South Korea

This study addresses the transformation of the South Korean developmental state since the early 1990s in relation to globalization and neoliberal restructuring. First, several key analytical-concepts are discussed for the study. Next, we examine two recent civilian- governments' major policies that have accelerated the transformation. Then, we spell out the changes of three major institutional actors in the developmental-state framework, i.e., the state, banks, and chaebols, which have resulted from the aforementioned conditions and policies. In conclusion, we argue that an alternative path should be followed instead of the current path of neoliberal transformation in South Korea to achieve a form of substantively-democratic development

Development and Validation of a Personality Assessment Instrument for Traditional Korean Medicine: Sasang Personality Questionnaire

Objective. Sasang typology is a traditional Korean medicine based on the biopsychosocial perspectives of Neo-Confucianism and utilizes medical herbs and acupuncture for type-specific treatment. This study was designed to develop and validate the Sasang Personality Questionnaire (SPQ) for future use in the assessment of personality based on Sasang typology. Design and Methods. We selected questionnaire items using internal consistency analysis and examined construct validity with explorative factor analysis using 245 healthy participants. Test-retest reliability as well as convergent validity were examined. Results. The 14-item SPQ showed acceptable internal consistency (Cronbach's alpha = .817) and test-retest reliability (r = .837). Three extracted subscales, SPQ-behavior, SPQ-emotionality, and SPQ-cognition, were found, explaining 55.77% of the total variance. The SPQ significantly correlated with Temperament and Character Inventory novelty seeking (r = .462), harm avoidance (r = −.390), and NEO Personality Inventory extraversion (r = .629). The SPQ score of the So-Eum (24.43 ± 4.93), Tae-Eum (27.33 ± 5.88), and So-Yang (30.90 ± 5.23) types were significantly different from each other (P < .01). Conclusion. Current results demonstrated the reliability and validity of the SPQ and its subscales that can be utilized as an objective instrument for conducting personalized medicine research incorporating the biopsychosocial perspective

A NUMERICAL STUDY ON THE OPEN WATER PERFORMANCE OF A PROPELLER WITH SINUSOIDAL PITCH MOTION

When a ship operates in waves, the ship moves with 6 degrees-of-freedom, and a propeller at the stern of the ship cannot avoid moving due to the ship motion. Therefore, it is important to analyse the propulsion performance while considering the ship motion in waves for efficient ship operation. The pitch motion of the ship has a dominant effect on the variation of the propeller performance and results in sinusoidal pitch motion of the propeller. In this study, a numerical analysis was done using a KP458 model propeller with a diameter of 10 cm, which was designed for the KLVCC2 body plan. The propeller performance was calculated using computational fluid dynamics (CFD) at several constant tilt angles. Numerical simulations were then conducted with sinusoidal pitch motion in several conditions of varying pitch angle. The variations of the thrust and torque of the propeller in sinusoidal pitch motion were compared with the results obtained in constant tilt angles

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

Simple Summary The cervix region segmentation significantly affects the accuracy of diagnosis when analyzing colposcopy. Detecting the cervix region requires manual, intensive, and time-consuming labor from a trained gynecologist. In this paper, we propose a deep learning-based automatic cervix region segmentation method that enables the extraction of the region of interest from colposcopy images in an unsupervised manner. The segmentation performance with a Dice coefficient improved from 0.612 to 0.710 by applying the proposed loss function and encoder-weighted learning scheme and showed the best performance among all the compared methods. The automatically detected cervix region can improve the performance of image-based interpretation and diagnosis by suggesting where the clinicians should focus during colposcopy analysis. Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy