Risk Control Problem Research Articles

Optimal condition‐based parameter learning and mission abort decisions

AbstractUnexpected failures of safety‐critical systems during mission execution are not desirable in that they often result in severe safety hazards and significant financial losses. Prompt mission abort based on real‐time degradation data is an effective means to prevent such failures and enhance system safety. In this study, we focus on safety‐critical systems that experience cumulative shock degradation and fails when the degradation exceeds a failure threshold. Real‐time degradation measurements are obtained via sensor monitoring, which are stochastically related to the hidden degradation parameters that vary across components. We formulate the optimal mission risk control problem as a sequential abort decision‐making problem that integrates adaptive parameter learning, following which a dynamic Bayesian learning approach is exploited to sequentially infer the uncertain degradation parameters by utilizing real‐time sensor data. The problem is constituted as a finite horizon Markov decision process to minimize the expected costs associated with inspections, mission failures and system failures. We derive a series of structural properties of the value function and demonstrate the existence of optimal abort thresholds. In particular, we establish that the optimal policy follows a state‐dependent control limit policy. Additionally, we study the existence and monotonicity of control limits associated with both the number of inspections and degradation severities. We demonstrate the performance of the proposed risk management policy through comparative experiments that show substantial superiorities over risk‐induced loss control.

How Supply Chain Finance Helps SME Financing under Blockchain Technology

Financing difficulties have always been a fatal factor restricting the survival and development of small and medium-sized enterprises. With the continuous development of technology, in recent years, relying on blockchain technology to empower supply chain finance has become one of the effective methods for small and medium-sized enterprises to obtain operating funds. This article takes Shaanxi Sun Simiao High tech Pharmaceutical Co., Ltd. and Jiangxi Ganjiu Liquor Industry Co., Ltd. as examples to explore and analyze the innovation of financing loans based on traditional financing loans and blockchain technology. Through research, it has been found that blockchain technology not only solves the problem of risk control between banks and enterprises, but also expands the financing channels of small and medium-sized enterprises with the characteristics of distributed accounting, effectively improving financing efficiency and promoting business development. This article explores how blockchain technology can serve supply chain finance, and further proposes strategies and suggestions to assist small and medium-sized enterprises in financing.

Optimal risk sharing and dividend strategies under default contagion: A semi-analytical approach

We investigate the risk control and dividend optimization problem of an insurance group in a general setting and propose an innovative semi-analytical approach to the problem. The group consists of multiple subsidiaries and is subject to exogenous default risk. The default intensity is subject to the contagious effect. The contagious effect refers to the increase in default intensities of surviving subsidiaries within the group when a default event occurs. The recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs) is derived together with the verification theorem. We propose a semi-analytical approach that first finds the analytical solution in the continuation region and then the numerical solution in the risk exposure region. We further present a numerical example of a three-subsidiary insurance group to demonstrate the semi-analytical method and illustrate the recursive computation procedures that are extendible to cases with more subsidiaries.

Entrepreneurial borrowing overdue prediction based on stacking model transfer learning

PurposeBy introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.Design/methodology/approachThe authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.FindingsThe effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.Practical implicationsFrom a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.Originality/valueThis paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

Research on Safety Technology of Anti-Sympathatic Detonation for Grenade Assembly

Base on the sympathatic detonation experiment between grenades and accessories, the actual data was obtained. The assembly link with the greatest risk of explosion was summarized by the data analysis results. Base on the obtained experimental data, the process and design requirements of the relevant assembly functional units ware proposed. On the other, the production data collected in the actual production process was comprehensively analyzed and feedback. That is possibility which fundamentally solves the risk control problem of the grenade assembly process.

An Asymptotic Method for Predicting Risks in Problems of Stochastic Monitoring and Control

To ensure the stabilization of the equilibrium state in a nonlinear system in the presence of noise, it is not enough to solve the local stabilization problem, it is also necessary to ensure continuous monitoring of a possible transition to a critical state leading to system failure. To organize such monitoring, we use the large deviations principle applied to dynamical systems with small perturbations. For the purposes of monitoring, the optimal path that we call the A-profile is important. We use the A-profile to build a situational forecast in the risk control problem for a multi-agent system. In addition to the nonlinear mechanism of internal stabilization of the level h for each of the agents, there are forces of mean field interaction between the agents. The weak limit in this model with the number of agents tending to infinity is described by the FokerPlanck-Kolmogorov equation, but the use of approximation up to O(h2) leads to a finite-dimensional Wentzel-Freidlin scheme. According to the scheme, we obtain an explicit A-profile as a solution of the degenerate Abel equation of the second kind. At the same time, the approximation in h makes it possible to develop a method of successive approximations for the A-profile. In this paper, the A-profile is synthesized as a solution of the optimal control problem, where the state-dependent Riccati equation method and the method of the approximating sequence of Riccati equations are used. In the article, these methods are applied and compared within the framework of the risk control problem.

Monitoring-data mechanism-driven dynamic evaluation method for slope safety

Many high and steep slopes are formed in the construction of hydropower, highway, and railway projects. The long-term safety risk management and control problems are prominent. Slope deformation monitoring is one of the means to directly characterize the safety status of a slope, so it is of great significance to use slope deformation monitoring information to accomplish the dynamic evaluation of slope safety for safe engineering operations. In this study, the aging component of the slope displacement was separated using the empirical mode decomposition method. Combined with the response surface equation and the genetic optimization algorithm, a parameter back analysis method based on the aging displacement increment of the slope was proposed, and the determined calculation parameters can better reflect the creep characteristics of rock slope. Based on the results of the back analysis parameters, the most dangerous slip surface of the slope was determined using the strength reduction method, and the time series of the safety factor of the slope was obtained. Combining the dynamic coupling relationship between the slope displacement monitoring quantity and the slope stability, a surrogate model between the safety factor of the slope and the displacement aging component was constructed for the first time. A novel monitoring-data mechanism-driven dynamic evaluation method for slope safety was proposed considering the creep characteristics of the slope. The method was applied to an engineering slope example. The relative errors of the fitting and the prediction results of the dynamic evaluation model were less than 1%. The dynamic quantitative evaluation of the slope safety based on the displacement monitoring data was achieved. This provided an effective guarantee for slope safety control.

Centralized systemic risk control in the interbank system: Weak formulation and Gamma-convergence

This paper studies a systemic risk control problem by the central bank, which dynamically plans monetary supply to stabilize the interbank system with borrowing and lending activities. Facing both heterogeneity among banks and the common noise, the central bank aims to find an optimal strategy to minimize the average distance between log-monetary reserves of all banks and the benchmark of some target steady levels. A weak formulation is adopted, and an optimal randomized control can be obtained in the system with finite banks by applying Ekeland’s variational principle. As the number of banks grows large, we prove the convergence of optimal strategies using the Gamma-convergence argument, which yields an optimal weak control in the mean field model. It is shown that this mean field optimal control is associated to the solution of a stochastic Fokker–Planck–Kolmogorov (FPK) equation, for which the uniqueness of the solution is established under some mild conditions.

Using a Duffing control approach to control the single risk factor in complex social-technical systems

An approach based on forced Duffing Equation with cubic term is proposed to solve the single risk factor control problem in complex social-technical systems from the micro perspective. The single risk control problem is transformed into a system vibration control problem. Firstly, the relationships among Duffing Equation, system state sudden change and single risk factor control are analyzed. System control point crosses the bifurcation set is an important condition for the system state sudden change, the Duffing Equation can be used to establish the vibration equation of the complex social-technical system, and the risk control method of the complex social-technical system can be deduced based on the vibration equation. Secondly, the interference of the external environment in Duffing Equation is defined as the energy change of risk pulse. Next, we establish the system risk control equation and derive its bifurcation response equation with stable solutions. Under three safety protection measures conditions, including strengthening internal damping of system, reducing external excitation influence, strengthening internal damping of the system while reducing external excitation influence, three system risk control strengthening equations are established and their corresponding bifurcation response equations with stable solutions are derived, respectively. Finally, a real-world case study is conducted.

Comparison of binary classifier and outlier detection in different equilibrium

In the fields of financial risk control and mechanical production, the data sets with abnormal problems are always extremely unbalanced, because the most of abnormal problems occur hardly. Using this unbalanced data set to train the binary classifier, the result is often not ideal. Although there are Ensemble Learning and Grid Search methods to improve the F1 and accuracy of the classifier, in order to simplify the model, it is better to regard this financial risk control problem as an outlier detection problem than a binary classification problem. This paper uses the public data set on Kaggle, and compares the performance of the commonly used binary classification algorithms including Bayesian, Decision Tree, Random Forest, Logistic Regression Classifier, K-Nearest Neighbor (KNN), AdaBoost, One-Class SVM, Isolation Forest and Local Outlier Factor on balanced and unbalanced data sets respectively. According to the experimental results, this paper find that Bayesian is more suitable when the data set is small. Random Forest is more suitable for balanced data. For medium and large data sets with extremely unbalanced data, the effect of using One-Class SVM is better and more stable, and the effect of stable model is more important than that of unstable one.

A Traceable Online Insurance Claims System Based on Blockchain and Smart Contract Technology

In the current medical insurance claims process, there are problems of low efficiency and complex services. When a patient applies for medical insurance claims, he/she must go to the hospital to apply for a diagnosis certificate and receipt and then send the relevant application documents to the insurance company. The patient will not receive compensation until the company completes the verification with the patient’s hospital. However, we can improve the current dilemma through blockchain technology. Blockchain technology can effectively open up the information channels of the insurance industry and medical institutions, promote industry integration, and enhance the ability of insurance companies to obtain information. In this research, we used blockchain and smart contract technology to make the following contributions to the development of Internet insurance. First, blockchain and smart contract technology can effectively solve the problem of online underwriting. Second, it is conducive to improving supervision. Third, it is conducive to solving risk control problems. Fourth, it is conducive to effective anti-money laundering. The proposed scheme fulfills the following security requirements: mutual authentication of identities, non-repudiation between each of two roles, and other major blockchain-based security requirements. In the event of a dispute, we also proposed an arbitration mechanism to divide responsibilities.

Mean–variance investment and risk control strategies — A time-consistent approach via a forward auxiliary process

We consider an optimal investment and risk control problem for an insurer under the mean–variance (MV) criterion. By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form. We compare our formulation and optimal strategy to those under the precommitment and game-theoretic framework. Numerical studies show that, when the financial market is negatively correlated with the risk process, optimal investment may involve short selling the risky asset and, if that happens, a less risk averse insurer short sells more risky asset.

Research on the Impact of Fiscal Transparency on Local Government Debt Risks：Based on the Perspective of Intergovernmental Relations

Under the general work guideline of making progress while maintaining stability, the importance and concern of local government debt risk issues are increasing. In order to alleviate the local government debt crisis, the central government has issued a number of important documents. Due to the active and continuous borrowing motives of local governments and the tendency to conceal debt behavior, the central government’s top-down” macroeconomic control measures have little effect. Fiscal transparency is a broad bottom-up” public monitoring tool that can effectively promote public supervision and restraint on government behavior. It has a positive and effective effect on controlling corruption, improving government credibility, and reducing debt risks.Firstly, this paper constructs a theoretical model based on game theory. The theoretical model uses the motive of local governments as the entry point to analyze how fiscal transparency will restrict local governments’ illegal debt-raising behavior. The results of the theoretical model show that: （1）With a single local government as the main player of the game, the increase in fiscal transparency will increase the expected cost of local governments’ illegal borrowing, thereby reducing the amount of illegal debt.（2）Taking two representative local governments with competitive relationship as the main players, the increase of fiscal transparency has a controlling effect on the illegal borrowing caused by inter-governmental competition. As the degree of fiscal transparency increases, the competing government will abandon the illegal way of borrowing and instead choose the legal way of borrowing.（3）With the central and local governments in the context of Paternalism” as the main players, the improvement of fiscal transparency can strengthen the central government’s commitment to not help local governments, and encourage local governments to actively converge on illegal debt-raising in order to avoid the debt crisis.Secondly, this paper constructs an empirical model based on provincial panel data to analyze the external performance” of the impact. The results of the study show that: （1）Improving fiscal transparency can effectively reduce local governments’ illegal borrowing impulses.（2） Political tournaments” and soft budget constraints” will exacerbate local governments’ debt impulses. In this context, improving fiscal transparency can effectively curb local governments’ debt impulses.（3）Improving fiscal transparency can encourage local governments to optimize their debt structure and optimize the bond issuance process.In the context of the current wide-ranging concern of local government debt problems, this paper aims to study the problem of local government debt risk control from the perspective of improving fiscal transparency. This paper provides new ideas for controlling local government debt risks, and also provides useful enlightenment for effectively promoting China’s fiscal transparency.

Sensor radiation interception risk control in target tracking

This paper is mainly on the problem of radiation interception risk control in sensor network for target tracking. Firstly, the sensor radiation interception risk is defined as the product of the interception probability and the cost caused by the interception. Secondly, the radiation interception probability model and cost model are established, based on which the calculation method of interception risk can be obtained. Thirdly, a sensor scheduling model of radiation risk control is established, taking the minimum interception risk as the objective function. Then the Hungarian algorithm is proposed to obtain sensor scheduling scheme. Finally, simulation experiments are mad to prove the effectiveness of the methods proposed in this paper, which shows that compared with the sensor radiation interception probability control method, the interception risk control method can keep the sensor scheduling scheme in low risk as well as protect sensors of importance in the sensor network.

The optimal multi-period hedging model of currency futures and options with exponential utility

In this paper, we consider the optimal cross-hedging and risk control problem of a competitive firm facing exchange risk exposure in a multi-period setting. We present the optimal positions of currency futures and options to maximize the exponential utility of the terminal wealth by dynamic programming approach. To evaluate the potential effectiveness of cross-hedging with currency futures and options, we compare terminal wealth, utility based on terminal wealth and variance of the wealth accumulation path in three cases: hedging with futures and options, net futures hedging and no hedging. An empirical study of two cross-exchange rates of USD/EUR and CNY/USD is performed. The results show that cross-exchange rate futures and options always offer the firm an improved ability to hedge against the foreign exchange rate risks. Relative to no hedging, cross-hedging with currency futures and options or net currency futures hedging achieves positive returns. As the risk aversion coefficient increases, the firm’s profit of futures hedging increases, while the profit of options hedging decreases. We suggest the firm with smaller risk aversion coefficient choose options hedging, and the firm with higher risk aversion coefficient choose futures hedging. Furthermore, when using currency derivatives for hedging, the wealth volatility also decreases significantly. Thus, for the multinational firms, cross-currency hedging is an efficient risk management technique to reduce their foreign exchange risk exposure.

STUDY ON RISK CONTROL OF WATER INRUSH IN TUNNEL CONSTRUCTION PERIOD CONSIDERING UNCERTAINTY

Water inrush risk is a bottleneck problem affecting the safety and smooth construction of tunnel engineering works, so the risk control of water inrush is important, however, geological uncertainty and artificial uncertainty always accompany tunnel construction. Uncertainty will not only affect the accuracy of water inrush risk assessment results, but also affect the reliability of water inrush risk decision-making results. How to control the influence of uncertainty on water inrush risk is key to solving the problem of water inrush risk control. Based on the definition of improved risk, a risk analysis model of water inrush based on a fuzzy Bayesian network is constructed. The main factors affecting the risk of water inrush are determined by sensitivity analysis, and possible schemes in risk control of water inrush are proposed. Based on the characteristics of risk control of water inrush in a tunnel, a multi-attribute group decision-making model is constructed to determine the optimal water inrush risk control scheme, so that the optimal scheme for reducing uncertainty in risk control of water inrush is determined. Finally, this system is applied to Shiziyuan Tunnel. The results show that the proposed risk control system for reducing uncertainty of water inrush is efficacious.

Risks Analysis of Discarding Water in Power Generation Process of Hydropower Station

Hydropower, as a kind of clean energy, has the characteristics of rapid response to load changes, and is often used to undertake peak regulation and frequency regulation tasks. However, due to the uncertainties of reservoir inflow and runoff, the relative lag of power grid outgoing channels, and the interconnection of intermittent new energy, such as wind power and solar energy, the hydropower system is facing enormous peak regulation pressure and the continuous prominent problem of discarding water. In the study, aiming at the problem of risk control of discarding water in power generation process of hydropower station, a practical calculation method of risk of discarding water in power generation is proposed based on the analysis of frequency of runoff. Taking the Ertan hydropower station as an example, the corresponding water level processes of different discarding water risks under the control of pre-flood falling water level, flood limit water level and storage water level after flood season are deduced, and in the routine dispatching process, the water level operation control guidance corresponding to different discarding risk is introduced. The simulation results show that the proposed discarding water risk control method is practical and effective, and can provide guidance for discarding water risk control in the actual dispatching process of hydropower station.

Optimal investment and risk control problems with delay for an insurer in defaultable market

This paper addresses a investment and risk control problem with a delay for an insurer in the defaultable market. Suppose that an insurer can invest in a risk-free bank account, a risky stock and a defaultable bond. Taking into account the history of the insurer's wealth performance, the controlled wealth process is governed by a stochastic delay differential equation. The insurer's goal is to maximize the expected exponential utility of the combination of terminal wealth and average performance wealth. We decompose the original optimization problem into two subproblems: a pre-default case and a post-default case. The explicit solutions in a finite dimensional space are derived for a illustrative situation, and numerical illustrations and sensitivity analysis for our results are provided.

A pontryaghin maximum principle approach for the optimization of dividends/consumption of spectrally negative markov processes, until a generalized draw-down time

ABSTRACTThe first motivation of our paper is to explore further the idea that, in risk control problems, it may be profitable to base decisions both on the position of the underlying process and on its supremum . Strongly connected to Azema-Yor/generalized draw-down/trailing stop time this framework provides a natural unification of draw-down and classic first passage times. We illustrate here the potential of this unified framework by solving a variation of the De Finetti problem of maximizing expected discounted cumulative dividends/consumption gained under a barrier policy, until an optimally chosen Azema-Yor time, with a general spectrally negative Markov model. While previously studied cases of this problem assumed either Lévy or diffusion models, and the draw-down function to be fixed, we describe, for a general spectrally negative Markov model, not only the optimal barrier but also the optimal draw-down function. This is achieved by solving a variational problem tackled by Pontryaghin's maximum principle. As a by-product we show that in the Lévy case the classic first passage solution is indeed optimal; in the diffusion case, we obtain the optimality equations, but the behavior of associated solutions for further explicit models and the question of whether they do better than the classic solution is left for future work. Instead, we illustrate the novelty by a toy example, with a conveniently chosen scale-like function.

Optimal Insurance Strategy in the Individual Risk Model under a Stochastic Constraint on the Value of the Final Capital

We solve a problem of optimal risk control in the static model by choosing an admissible insurance policy, where the objective functional is the so-called Markowitz utility functional, i.e., a functional that depends only on the mean value and standard deviation of the insurer’s final capital after an insurance transaction. Interests of the insurer are taken into account by introducing probabilistic or, more precisely, quantile constraints (value at risk constraint) on the final capital of the insurer, using a normal distribution to model the distribution of total damage. Additionally, we impose a restriction with probability one on the risk taken from an individual policy holder. Optimal from the point of view of the insurer is the so-called stop-loss insurance. We find explicit forms of conditions for refusing an insurance transaction. We give an example that illustrates the proven results in case of an exponential distribution of claim size.