Calibrating rough volatility models: a convolutional neural network approach

In this paper, we use convolutional neural networks to find the Hölder exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application.     

基于智能信息处理的供应链绩效评价方法

从软计算的角度出发,比较分析相关供应链绩效评价模型,结果显示不同处理方法的联合将在供应链绩效评价中保持较高的准确度。结合某供应链绩效案例,进行基于智能信息处理的供应链绩效评价方法仿真,结果表明,通过粗糙集约简和模糊综合评估的融合,可显著缩小数据处理规模,降低评估模型的计算复杂度,同时克服模糊评估过度依赖专家知识的缺点。通过粗糙集约简和BP神经网络的融合,可降低BP网络的设计复杂度,克服神经网络训练时间长、知识解释性较差的缺点。     

基于粗糙集的电子废弃物逆向物流的目标优先级排序

逆向物流的战略地位逐渐凸显,电子废弃物因其特殊的产品属性,加之目前我国尚未建立完善的回收体系,亟待规范的“灰色地带”需要实施有效的逆向物流规划,科学合理的目标优先级的确定是其得以实现的理论基点。本文针对电子废弃物的特性,考虑逆向物流不同的实施阶段,分析了影响目标优先级的主要因素,提出了一种基于粗糙集一熵权理论的目标优先级确定算法,改善了传统的主观权重赋值法,结果表明该算法是精确且有效的。     

基于粗糙集的在线拍卖网站灰色评价方法研究

随着在线拍卖的不断发展,在线拍卖网站投资者和参与拍卖商品的竞拍者都十分关注在线拍卖网站的运营状况,因此,客观合理的在线拍卖网站的综合评价方法对在线拍卖网站的建设与发展具有重要的意义,在一定程度上促进了人们在线拍卖活动的意愿。本文通过对在线拍卖网站进行分析,建立了在线拍卖网站评价指标体系,提出了一种基于粗糙集的在线拍卖网站灰色评价方法。该方法先基于粗糙集并结合专家的意见,建立信息系统,进而确定各层指标的权重,然后根据灰色统计的方法建立评价权矩阵,最后基于指标权重信息和评价权矩阵,利用模糊方法进行综合评价。以14家在线拍卖网站进行了实证计算分析,结果表明,该方法具有较好的客观性和实用性,解决了传统在线拍卖网站评价中存在的问题,提高了评价结论的正确性和有效性。     

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts’ opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data: (1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.     

格兰杰因果性检验评述

格兰杰因果性检验是计量经济学中最常用的因果性检验方法，但是因为种种原因，目前很多实证研究存在着对该检验方法的模糊认识和不正确运用。本文回顾了格兰杰因果性检验的发展脉络，对国内目前在实际运用格兰杰因果性时存在的一些问题进行了讨论。     

国际竞争力因素分析的Rough集方法

本文通过WFF和IMD的国际竞争力评价体系以及 2 0 0 0年的评估年鉴的数据 ,利用Rough集方法给出了国际化程度、金融体系、资本流动等方面与国际竞争力的关系知识 ,为提高国际竞争力的决策提供了依据。     

Multiscaling and rough volatility: An empirical investigation

Pricing derivatives goes back to the acclaimed Black and Scholes model. However, such a modelling approach is known not to be able to reproduce some of the financial stylised facts, including the dynamics of volatility. In the mathematical finance community, it has therefore emerged a new paradigm, named rough volatility modelling, that represents the volatility dynamics of financial assets as a fractional Brownian motion with Hurst exponent very small, which indeed produces rough paths. At the same time, prices’ time series have been shown to be multiscaling, characterised by different Hurst scaling exponents. This paper assesses the interplay, if present, between price multiscaling and volatility roughness, defined as the (low) Hurst exponent of the volatility process. In particular, we perform extensive simulation experiments by using one of the leading rough volatility models present in the literature, the rough Bergomi model. A real data analysis is also conducted to test if the rough volatility model reproduces the same relationship. We find that the model can reproduce multiscaling features of the prices’ time series when a low value of the Hurst exponent is used, but it fails to reproduce what the real data says. Indeed, we find that the dependency between prices’ multiscaling and the Hurst exponent of the volatility process is diametrically opposite to what we find in real data, namely a negative interplay between the two.     

Self-contained regime and the meaning of lex specialis rule on the example of Draft Articles on Responsibility of States of UN International Law Commission and the Dispute Settlement Body (DSB) within the law of World Trade Organization (WTO)-selected issues

The aim of this article is a presentation of the selected issues concerning the conception and contemporary perception of the legal order dealing with self-contained regime and the significance of the ...     

上市公司股权制衡与并购绩效——基于夏普利(Shapley)指数与粗糙集的实证研究

本文利用1998～2009年中国上市公司的数据,以夏普利(Shapley)指数构建公司股权制衡指标,运用粗糙集理论分析了中国上市公司中股权制衡与并购绩效的关系。并购绩效是并购决策的最终体现,公司的并购决策会因为大股东之间的相互制衡而发生变化,进而影响并购后的公司绩效。因此,在评估上市公司的并购绩效时,应该对公司的股权制衡情况给予重点关注。在分析股权制衡与并购绩效的关系时,综合应用粗糙集法与回归分析法,能够得到一个更加稳健和全面的结论,即股权制衡是影响并购绩效的重要正面因素。