Option Implied Risk Aversion under Transaction Costs: An Empirical Study

We empirically estimate the option implied coefficient of risk aversion of the market maker for European S&P 500 index options (SPX), involving asset allocation and option market making problems in the presence of proportional transaction costs in trading the underlying asset. We assume that the market maker has constant relative risk aversion utility and holds a two-asset portfolio consisting of the underlying and the riskless asset for a fixed, finite investment horizon which exceeds the option maturity, and she enters a position in the option market with an optimized portfolio. We follow the discrete time approach of Czerwonko and Perrakis (2016a, 2016b) to derive the market maker’s simple investment policy and value functions, and apply a value matching condition to find option upper and lower bounds. Data on the S&P 500 index and the SPX options is collected over the period 1996–2016, 244 months in total, and the major variable, volatility, is re-estimated under the physical distribution. By matching observed SPX prices with numerically derived reservation prices, we estimate the level of implied risk aversion. Results show that in general, the market maker has lower risk aversion compared to investors who she trades with in order to accomplish a trade. A pattern that high risk aversion precedes rare market events is also exhibited, suggesting that a market maker may adopt a waiting policy if market events can be anticipated due to the information asymmetry

A Dual Cox Model Theory And Its Applications In Oncology

Given the prominence of targeted therapy and immunotherapy in cancer treatment, it becomes imperative to consider heterogeneity in patients' responses to treatments, which contributes greatly to the widely used proportional hazard assumption invalidated as in several clinical trials. To address the challenge, we develop a Dual Cox model theory including a Dual Cox model and a fitting algorithm. As one of the finite mixture models, the proposed Dual Cox model consists of two independent Cox models based on patients' responses to one designated treatment (usually the experimental one) in the clinical trial. Responses of patients in the designated treatment arm can be observed and hence those patients are known responders or non-responders. From the perspective of subgroup classification, such a phenomenon renders the proposed model as a semi-supervised problem, compared to the typical finite mixture model where the subgroup classification is usually unsupervised. A specialized expectation-maximization algorithm is utilized for model fitting, where the initial parameter values are estimated from the patients in the designated treatment arm and then the iteratively reweighted least squares (IRLS) is applied. Under mild assumptions, the consistency and asymptotic normality of its estimators of effect parameters in each Cox model are established. In addition to strong theoretical properties, simulations demonstrate that our theory can provide a good approximation to a wide variety of survival models, is relatively robust to the change of censoring rate and response rate, and has a high prediction accuracy and stability in subgroup classification while it has a fast convergence rate. Finally, we apply our theory to two clinical trials with cross-overed KM plots and identify the subgroups where the subjects benefit from the treatment or not

Stabilization and current-induced motion of antiskyrmion in the presence of anisotropic Dzyaloshinskii-Moriya interaction

Topological defects in magnetism have attracted great attention due to fundamental research interests and potential novel spintronics applications. Rich examples of topological defects can be found in nanoscale non-uniform spin textures, such as monopoles, domain walls, vortices, and skyrmions. Recently, skyrmions stabilized by the Dzyaloshinskii-Moriya interaction have been studied extensively. However, the stabilization of antiskyrmions is less straightforward. Here, using numerical simulations we demonstrate that antiskyrmions can be a stable spin configuration in the presence of anisotropic Dzyaloshinskii-Moriya interaction. We find current-driven antiskyrmion motion that has a transverse component, namely antiskyrmion Hall effect. The antiskyrmion gyroconstant is opposite to that for skyrmion, which allows the current-driven propagation of coupled skyrmion-antiskyrmion pairs without apparent skyrmion Hall effect. The antiskyrmion Hall angle strongly depends on the current direction, and a zero antiskyrmion Hall angle can be achieved at a critic current direction. These results open up possibilities to tailor the spin topology in nanoscale magnetism, which may be useful in the emerging field of skyrmionics.Comment: 31 pages, 6 figures, to appear in Physical Review

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration

Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are capable of interpreting and generating complex natural language, while domain models are efficient in learning task-specific information. In this paper, we propose the precedent-enhanced LJP framework (PLJP), a system that leverages the strength of both LLM and domain models in the context of precedents. Specifically, the domain models are designed to provide candidate labels and find the proper precedents efficiently, and the large models will make the final prediction with an in-context precedents comprehension. Experiments on the real-world dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a promising direction for LLM and domain-model collaboration that can be generalized to other vertical domains

Inhibition of SIRT2 by Targeting GSK3β-Mediated Phosphorylation Alleviates SIRT2 Toxicity in SH-SY5Y Cells

Sirtuin 2 (SIRT2) is thought to be important in the pathogenesis of Parkinson’s disease (PD), and the inhibition of SIRT2 rescues α-synuclein toxicity in a cellular model of PD. Recent studies have focused on identifying inhibitors of SIRT2, but little is known about the processes that directly regulate its function. GSK3β is a serine/threonine protein kinase that affects a wide range of biological functions, and it is localized in Lewy bodies (LBs). Therefore, we investigated whether SIRT2 is regulated by GSK3β and enhances cell death in PD. In the present study, Western blot showed that total SIRT2 levels did not change noticeably in a cellular model of PD but that SIRT2 phosphorylation was increased, and GSK3β activity was elevated. In addition, mass spectrometry (MS) studies indicated that SIRT2 was phosphorylated by GSK3β at three specific sites. Phospho- or dephospho-mimicking studies demonstrated that this postmodification (phosphorylation) increased SIRT2 toxicity in SH-SY5Y cells. Collectively, our findings identify a posttranslational mechanism that controls SIRT2 function in PD and provide evidence for a novel regulatory pathway involving GSK3β, SIRT2, and α-synuclein

Liposomal Curcumin Targeting Endometrial Cancer Through the NF-κB Pathway

Background/Aims: Emerging evidence suggests that curcumin possesses chemopreventive properties against various cancers. However, its poor bioavailability limits its clinical application. In this study, we aimed to utilize encapsulation in liposomes (Lipo) as a strategy for the clinical administration of curcumin for endometrial carcinoma (EC). Methods: Curcumin was encapsulated in a liposomal delivery system to prepare a formulation of liposomal curcumin (LC). EC cell lines Ishikawa and HEC-1 were treated with the compound and cell proliferation was measured using MTT assay. Hoechst 33258 staining assay and flow cytometry were used to detect apoptosis of the cells. Wound healing and cell invasion assays were employed to monitor cell motility. Underlying target signaling, such as NF-κB, caspases, and MMPs, were further studied via qRT-PCR and western blot. Thereafter, a zebrafish model was used to assess the toxicity of LC. Finally, a zebrafish transplantation tumor model of EC was grown and treated with LC. Tumors were monitored and harvested to study the expression of NF-κB. Results: The formation of LC was successfully developed with excellent purity and physical properties. In vitro, LC resulted in dose-dependent inhibition of proliferation, induction of apoptosis, and suppression of Ishikawa and HEC-1 cell motility. LC treatment also suppressed the activation and/or expression of NF-κB, caspase-3, and MMP-9. No demonstrable toxicity was found in the zebrafish model and tumors were suppressed after treatment with LC. PCR analysis also showed down-regulated expression of NF-κB. Conclusions: LC was successfully prepared and played biological roles against EC probably through negative regulation of the NF-κB pathway in vitro and in vivo, which demonstrates its potential therapeutic effects in EC

Development of hybrid application for supply and demand

HTML5 is the latest version of the HyperText Markup Language. It helps describe the document's structure and allows documents to be cross-linked. Today the language has grown, with an Alexa research stating that 34% of sites were using HTML5 alone (discounting other versions of HTML such as HTML 4.0 or XHTML). Further, HTML has gained two other partner systems that are closely associated with and support it. These are CSS (Cascading Style Sheets – latest iteration CSS3) helps HTML improve the document’s user interface by describing how it should look. JavaScript then helps to build interactivity. The purpose of this project is to develop an application for interactive teaching and learningBachelor of Engineerin

Public Investment, Environmental Regulation, and the Sustainable Development of Agriculture in China Based on the Decomposition of Green Total Factor Productivity

This study aims to accurately assess the growth of agricultural total factor productivity and its driving components under the constraints of resources and environment, and provides reliable information for agricultural policy formulation and agricultural development practices. According to the input and output panel data of provincial agricultural planting in China and employing the Global Malmquist–Luenberger (GML) index method and the Bootstrap method, this paper measures China’s agricultural green total factor productivity (GTFP), technical efficiency change (EC), and technical best-practice gap change (BPC). In addition, the Tobit model is applied to analyze the impact of public investment and environmental regulation variables on China’s agricultural GTFP and its components. The results show that (1) China’s agricultural GTFP has steadily improved, and technological promotion is the main contributor; (2) agricultural GTFP and its components present significant spatial differences, which are overall manifested as agricultural priority development zone > agricultural moderate development zone > agricultural protection development zone; and (3) financing support of technical innovation and the intensity of environmental regulation have a significant positive impact on agricultural GTFP and its components. The combination of positive technical innovation support and appropriate environmental regulation helps to improve agricultural GTFP and achieve the sustainable development of China’s agriculture