Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.     

传媒的发展和大众文化研究及其前瞻

本文认为,网络时代大众文化的研究趋向重点应该有三个方面:第一,网络赋予了大众文化不同的特征,使它在内涵和技术特征上发生了变化.大众文化研究有必要对这些变化在理论上进行分析和批判;第二,对网络时代大众文化诸多现象的批评;第三,加强网络时代大众文化的引导问题的研究.     

碳市场建立对非控排企业绿色创新的影响研究

以尚未纳入碳交易体系的上市公司为样本,基于溢出效应框架分析碳市场建立对非控排企业环保决策的影响。研究发现,客户被纳入碳市场交易促进了非控排供应商企业的绿色创新。其作用机制在于,碳交易下的控排企业绿色创新具有基于生产网络的压力效应和动力效应,能够激发关联非控排企业的绿色创新意愿,并助推其绿色创新能力提升。这一溢出效应有助于实现非控排企业绿色创新增量提质,且该过程具有方向性,碳市场建立并不存在基于生产网络“自上而下”的治理效果外溢,相关影响在非控排企业客户集中度较高、绿色创新基础较好及环境不确定性较高时更加明显。此外,碳市场建立的绿色创新溢出效应对于推动非控排企业可持续发展具有积极作用。研究结论为高效发挥碳市场绿色治理效果、深化环保改革以促进经济高质量发展提供了管理启示。     

社会资本研究综述

社会资本是近年来学术界研究的热潮,内容涉及微观、中观以及宏观各个层次,取得了一定的成果,但同时也存在一些不足,诸如概念不统一、缺乏有效的测量以及研究中忽视其消极功能等。与此同时,社会资本作为一个外来分析工具和理论模型,面临着研究的本土化问题。     

网络对青少年的影响与对策

在网络逐渐大众化的今天,青少年疯狂上网已经成为所有家长和学校棘手的重要问题。从网络给青少年带来的影响入手,在肯定了网络对青少年积极影响的同时,指出了负面影响的消极作用,从明确责任、强化建设等方面提出解决存在问题、合理利用网络的应对策略。     

中文网络资源指南分类体系比较分析

通过对六种中文搜索引擎网络资源指南一、二级类目的比较分析,总结现有中文网络分类目录类目设置的规律及存在的主要问题,提出制定规范的网络信息分类标准的必要性、可行性与一般原则。     

基于DEA的网络出版流程效率评价研究

文章使用DEA方法,以网络出版流程的完整建设、效率建设和其他建设的投入为输入指标,以对网络出版商的经济效益影响、对网络出版物的影响和社会效益三方面为输出指标,对网络出版流程的效率进行评价。通过评价结果,判断并得知目前网络出版流程的现状,发现存在的问题,寻找出现这种问题的原因,最终寻找解决措施。     

基于计算实验的逆向物流网络优化研究

文章针对逆向物流网络存在的高复杂性与不确定性,运用计算实验方法构建了一个四级的逆向物流网络模型。文章运用该模型研究了逆向物流网络的动态优化过程,并运用实际算例验证了模型的有效性。     

网络环境下公共图书馆读者服务工作初探

21世纪的读者服务观念,必须建立在现代的科学的思想观念基础上,实现“以书为主体”的服务,向“以人为主体”的服务观念的转变,顺利完成从“物的传递”向“知识的传递”的过渡。要站在读者的立场,进行换位思考,创造全新的现代化服务观念。