Simulation-based automatic generation of risk scenarios

A methodology for automatically generating risk scenarios is presented.Its main idea is to let the system model "express itself" through simulation.This is achieved by having the simulation model driven by an elaborated simulation engine,which:(i) manipulates the generation of branch points,i.e.event occurrence times;(ii) employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point.In addition,a backtracking technique,as an extension,is implemented to recover some missed risk scenarios.A widely discussed dynamic reliability example(a holdup tank) is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.     

Ruin probabilities with pairwise quasi-asymptotically independent and dominatedly-varying tailed claims

This paper considers the nonstandard renewal risk model in which a part of surplus is invested into a Black-Scholes market whose price process is modelled by a geometric Brownian motion, claim sizes form a sequence of not necessarily identically distributed and pairwise quasi-asymptotically independent random variables with dominatedly-varying tails.The authors obtain a weakly asymptotic formula for the finite-time and infinite-time ruin probabilities.In particular,if the claims are identically distributed and consistently-varying tailed,then an asymptotic formula is presented.     

The compound binomial model with a constant dividend barrier and periodically paid dividends

Consider the compound binomial risk model with interest on the surplus under a constant dividend barrier and periodically paying dividends.A system of integral equations for the arbitrary moments of the sum of the discounted dividend payments until ruin is derived.Moreover,under a very relaxed condition,the solutions for arbitrary moments are obtained by setting up iteration processes because of a special property of the system of integral equations.     

Simulation and uniform design-based automatic generation of risk scenarios

This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics(e.g.,the order,timing and magnitude of events,the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system.The main idea of the methodology is:(i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine;(ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics;(iii) for each selected design point,employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point.A highly dynamic example adapted from the literature(a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.     

Government policies and corporate financing decisions in China: Theory and evidence

This paper examines the effect of government policies on the financing decisions of firms in China. A real options model is developed to understand how fiscal and monetary policies affect corporate leverage.The model predictions will be tested with a comprehensive panel data set spanning from 2002 to 2011.This work documents robust evidence that show the positive association of both tax and risk-free rate with firm leverage:increase in tax rate and risk free rate by one standard deviation results in the increase in corporate leverages by 0.61 to 1.06 percent and 2.54 to 3.68 percent,respectively.In addition,the productions of the firms are not affected by the tax rate in the short run,and the firms are operating in their optimal market leverage.The implied tax rate and risk free rate are solved by assuming that the firms achieve their optimal leverages.The implied tax rate declines with the size, whereas the opposite goes for implied risk-free rate.     

Physical Examination Data Based Cataract Risk Analysis

Cataract is a very common eye disease and the most significant cause of blindness.In consideration of its burden on society,the focus was put on testing the risk factors of cataract and building robust machine learning models in which these factors can be utilized to predict the risk of cataract.The data used herein was collected by a Chinese physical examination center located in Shanghai.It contains more than 120,000 examinees and about 500 physical examination metrics.Firstly,association rules were adopted to filter 39 abnormalities which are more likely to incur the risk of cataract,and the significance of these abnormalities was tested with univariate analysis and multivariate analysis.The test results indicate that age,diabetes,refractive error,retinal arteriosclerosis,thyroid nodules,and incomplete mammary gland degeneration significantly increase the possibility of cataract.Various machine learning models were compared in terms of their performance in predicting the risk of cataract based on these six factors,among which the logistic regression model and the decision-tree based ensemble methods outperform others.The test set A U C of these models can reach 0.84.     

Personal Credit Risk .Scoring Model Based on Rough Set and Neural Network

In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy     

Impact of the RMB Joining in the SDR Basket on Its Internationalization from the Perspective of Risk Spillover

For evaluating the influence of the Chinese renminbi(RMB) joining in the special drawing right(SDR) basket on RMB's internationalization, the authors systemically study the risk spillover networks and examine the dynamic relationship of exchange rates among the SDR currencies including the US dollar(USD), European Union euro(EUR), Japanese yen(JPY) and British pound(GBP).The empirical results demonstrate that the USD takes a dominant position and holds the biggest risk spillover to other currencies, and the RMB's inclusion to the SDR basket makes the risk spillover to get average, giving rise to the SDR currency system more stable to a certain degree. The inclusion of the RMB in the SDR not only can reduce the systematic risk of the SDR, but also has a certain impact on the international exchange rate markets. Nowadays, in front of the growing trade friction, more such researches could help to effectively deal with the currency disputes.     

A RISK-AVERSE NEWSVENDOR MODEL WITH LIMITED CAPACITY AND OUTSOURCING UNDER THE CVAR CRITERION

For seasonal products,the retailers usually launch various marketing efforts,like advertising and promotion,to promote them in a selling season.While facing large demand from the customers,one should take the capacity constraint and outsourcing into consideration.Considering the shorten life cycles of most products,in this paper we adopt the traditional newsvendor model to investigate the optimal marketing effort along with optimal order quantity.We address the risk aversion issue and characterize the influence of the sellers’ risk propensity with CVaR criterion,and we develop an effective algorithm to obtain the optimal strategy.The impact of sellers’ risk propensity on the performance of the system is illustrated via numerical examples.The innovation of this paper is threefold.First,the optimal joint strategy of the marketing effort and order quantity is investigated and an efficient algorithm to find the optimal strategy is developed.Second,the capacity constraint option and an outsourcing strategy are studied jointly for excess products.Finally,the risk propensity of the seller and its influence are investigated by using the CVaR criterion,through which we obtain some new managerial insights.     

The Portfolio Decision Under the VaR Constraint

The VaR, a new appearing financial risk-manage tool, have been applied widely. Many financial setups have accustomed to measure the risk of a portfolio with the VaR. So it is very necessary to discuss the portfolio choice problem under the VaR constraint. In this paper, by setting and solving the portfolio choice model under the VaR constraint, we illustrate that the use of the VaR constraint reduces the array of choice to a more manageable range. The probability of traget VaR, therefore, can be thought of as a risk tolerance assessment tool (when coupled with another measure of risk).     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

Coping with the Bullwhip Effect Using Inventory or Cash: An Environment-Behavior-Performance Paradigm

The bullwhip effect is w idely found in business and exerts adverse effects on business activities.To investigate the influence of the bullwhip effect on firm s'performance and their responses,this study proposed an environment-behavior-performance analysis fram ework and offered a new perspective for studying the bullwhip effect.Using data collected from 1,734 listed manufacturers in China from 2002 to 2017,we adopted regression models to test the proposed m odel and conducted a series of robustness tests.We find that the bullwhip effect is positively related to operating risk,inventory,and cash holdings,and the moderate levels of inventory and cash are negatively associated with operating risk.Specifically,inventory and cash play different roles and work together to alleviate operating risk induced by the bullwhip effect.However,excess resource holdings are positively associated with operating risk.Therefore,firm swith different levels of resources should hold suitable levels of inventory,cash,or both as contingent responses to the bullwhip effect.     

Optimal operational decisions when a manufacturer offers trade credit to his retailer

In this paper,we develop models to determine operational and financial decisions of a supply chain under the condition that the retailer faces a financial constraint and the manufacturer can offer trade credit to assist the retailer.We first study the case where the retailer is risk-neutral,and derive the optimal ordering and financial decisions.Then,the case where the retailer is risk-averse(downside risk) is studied and -the effects of the risk on the retailer and manufacturer’s operational and financial decisions are discussed.Finally,numerical examples are provided to conduct managerial analysis.     

How to Construct a Lower Risk FOF Based on Correlation Network? The Method of Principal Component Risk Parity Asset Allocation

In order to build a low-risk Fund of Funds(FOF), from the perspective of correlation, the principal component factor is used to improve the traditional risk parity model. Principal component analysis is used to decompose the underlying assets and generate unrelated principal component factors,and then the authors can construct a principal component risk parity portfolio. The proposed empirical results based on China’s mutual fund market show that the performance of principal component risk parit...     

Convergence of Self-Tuning Regulators Under Conditional Heteroscedastic Noises with Unknown High-Frequency Gain

In the classical theory of self-tuning regulators, it always requires that the conditional variances of the systems noises are bounded. However, such a requirement may not be satisfied when modeling many practical systems, and one significant example is the well-known ARCH(autoregressive conditional heteroscedasticity) model in econometrics. The aim of this paper is to consider self-tuning regulators of linear stochastic systems with both unknown parameters and conditional heteroscedastic noises, where the adaptive controller will be designed based on both the weighted least-squares algorithm and the certainty equivalence principle. The authors will show that under some natural conditions on the system structure and the noises with unbounded conditional variances, the closed-loop adaptive control system will be globally stable and the tracking error will be asymptotically optimal.Thus, this paper provides a significant extension of the classical theory on self-tuning regulators with expanded applicability.     

Reliability and Numerical Analysis of a Robot Safety System

This paper discusses a model for a repairable robot safety system composed of a safety component and two redundant robots according to a semi-group approach. The development of the model is divided into three phases:(Ⅰ) The use of pure analysis to prove the uniqueness of a classical solution to the model;(Ⅱ) The verification of this uniqueness by providing a non-negative solution to the model; and(Ⅲ) the formulation of reliability indices for the model, along with numerical examples to explain the results. The existence, uniqueness, and exponential stability of the solution of the repairable robot safety system is studied using Volterra-type integral-differential-equation theory, and the spectral distribution of the ACP operator is discussed. Moreover, the authors propose a method to investigate the steady-state indicator of the robot safety system with the safety component working, and with or without the component revolving, to make use of the correspondence of the eigenvector to the eigenvalue zero. The authors show that the robot's operation, with or without the safety component revolving and with fractional motion, is superior to that with the safety component working. The novelty of the approach lies in its examination of a unique solution to the system and its exponential stability based on C_0-semi-group theory, the co-final relative theory, and the functional analysis method.     

The Optimal Dividend Barrier in the Perturbed Compound Poisson Risk Model with Randomized Observation Time

This paper considers the dividend problems in the perturbed compound Poisson risk model.Assume that dividends can only be paid at the observation time when the surplus exceeds the barrier level and the excess is paid as dividend.In this paper,integro-differential equations for the expected discounted dividends until ruin and the Laplace transform of ruin time are firstly derived.When the claim is exponentially distributed,explicit expressions for the expected discounted dividends until ruin and the Laplace transform of ruin time are also obtained.Finally,the optimal dividend barrier which maximizes the expected discounted dividends until ruin is given.     

Application reliability for communication networks and its analysis method

The network reliability is difficult to be evaluated because of the complex relationship among the network components.It can be quite different for different users running different applications on the same network.This paper proposes a new concept and a model of application reliability.Different from the existing models that ignores the effects of applications,the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible.This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties.Finally,a case study is presented to illustrate the proposed network reliability model and the analysis method.     

Optimal investment with multiple risky assets under short-selling prohibition in a periodic environment

In this paper,the surplus process is assumed to be a periodic risk model and the insurer is allowed to invest in multiple risky assets described by the Black-Scholes market model.Under short-selling prohibition,the authors consider the optimal investment from an insurer’s point of view by maximizing the adjustment coefficient and the expected exponential utility of wealth at one period, respectively.It is shown that the optimal strategies of both of optimization problems are to invest a fixed amount of money in each risky asset.