The Risk Measurement under the Variance-Gamma Process with Drift Switching

The paper discusses an extension of the variance-gamma process with stochastic linear drift coefficient. It is assumed that the linear drift coefficient may switch to a different value at the exponentially distributed time. The size of the drift jump is supposed to have a multinomial distribution. We have obtained the distribution function, the probability density function and the lower partial expectation for the considered process in closed forms. The results are applied to the calculation of the value at risk and the expected shortfall of the investment portfolio in the related multivariate stochastic model.

Cyber-attack and Measuring its Risk

In this short research, cyber-attack and the well-known attacking methods are discussed. Moreover, how many attacks were made in 2021 compared to the attacks in the previous year is found, to determine how fast this malicious activity is growing and the reasons which motivate such cyber-attacks are studied. The risk measurement methods are also discussed in this article based on some previous research. The conclusions are made on the suitable solution for cyber-attack, reviewed based on the point of view of different research.

Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model

PurposeThe purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model.Design/methodology/approachThe joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market.FindingsEmpirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model.Social implicationsThe proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks.Originality/valueA novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.

Methodological approach to risk prediction noncompliance in patients with myocardial infarction

Aim. To develop a methodological approach in order to predict the risk of noncompliance in patients with myocardial infarction.Methods. 416 patients were questioned in the single-centered, prospective, non-randomized study using the original author's method. The patients were treated in specialized cardiological departments of the city of Kemerovo with the diagnosed myocardial infarction. The methodological approach to predicting the risk of non-compliance in patients with myocardial infarction covered 29 factors in 6 main blocks: sociodemographic and socio-economic characteristics, health status, medical and pharmaceutical culture of the patient, awareness of medical and pharmaceutical services, patient adherence to medical recommendations.Results. Patients with myocardial infarction were characterized by insufficient adherence to the therapy, low awareness of the disease, which can negatively affect the longterm disease prognosis. The identification of a large number of subjective factors limiting adherence to the therapy is the reason for the widespread use of noncompliance risk measurement among patients with myocardial infarction, which will allow determining the range of the risk group for each individual patient.Conclusion. The adherence to the treatment of patients with myocardial infarction is revealed as 80% which is indicated as low and requires the prophylactic use of educational and psychological programs that increase medical and social awareness and readiness to comply with the doctor's recommendations, and also justifies the need for complex risk measurement of non-compliance patients for personalized identification and addressing risk factors for poor adherence to therapy.

Price Risk Measurement of China’s Soybean Futures Market Based on the VAR-GJR-GARCH Model

As one of the main forces in the futures market, agricultural product futures occupy an important position in China’s market. As China’s futures market started late and its maturity was low, there are many risks. This study focuses on the Dalian soybean futures market. Dynamic risk measurement models were established to empirically analyze risk measurement problems under different confidence levels. Then, the conditional variance calculated by the volatility model was introduced into the value-at-risk model, and the accuracy of the risk measurement was tested using the failure rate test model. The empirical results show that the risk values calculated by the established models at the 99% and 95% confidence levels are more valuable through the failure rate test, and the risk of China’s soybean futures market can be measured more accurately. The characteristics of “peak thick tail” and “leverage effect” are added to the combination model to calculate the conditional variance more accurately. The failure rate test method is used to test the model, which enriches the research problem of risk measurement.

Research on the Risk Measurement Algorithm of Supply Chain Order Financing Based on Insurance Actuarial

In today’s globalized economy, all the links of supply chain are interlinked. Most of the upstream raw material manufacturers or producers in the said chain are small- and medium-sized enterprises (SMEs) that provide the basis for the efficient operation of the whole supply chain. However, SMEs in China, especially those playing a pivotal role in China’s export-oriented economy at this stage, do not have access to the corresponding financial treatment. Supply chain finance provides a new perspective to solve this contradiction. Henceforth, this paper introduces modern financial engineering risk measurement tools to measure the financial risk in supply chain finance, specifically while evaluating the single financing business. Moreover, the chief objective of this paper will be the analysis of the characteristics and connotations of order financing business model. In addition, the focus will be to analyze the risk of order financing from the perspective of banks and other financial institutions. Additionally, this paper will use the CreditRisk + model based on insurance actuarial principles to manage credit risk in order financing business based on foreign currency settlement, in conjunction with the characteristics of supply chain finance and multinational supply chain. Furthermore, a risk measurement method for the application of order financing in multinational supply chains will be provided. Ultimately, the experiments show that the solution of this paper defines and analyzes the financial risks brought by order financing business to bank financing.

Operation Strategy of Central Air Conditioning Virtual Power Plant Based on Risk Measurement Method

Virtual power plant is an integrated technology and operation mode to realize air-conditioning load participating in power system operation, further benefitting low carbon renewable energy applications. However, the principle of multi-system coupling in central air-conditioning poses a challenge to normal load regulation. Besides, the uncertainties of demand-side resources bring risks to the operation of virtual power plant. In this paper, the regulation characteristics of central air conditioning are obtained by experiment, while the potential of central air conditioning is quantified by a thermodynamic model, further resulting in the central air conditioning could be transformed into a virtual unit model. Then the dynamic capacity optimization strategy is formulated based on the risk measurement theory, while the generation task decomposition strategy is also formulated based on the equal increase rate criterion, thus forming a two-tier operation strategy of virtual power plant. Finally, illustrative case study is constructed to quantitatively analyze the power generation capacity and effectiveness of the virtual power plant. The effectiveness and practicability of the proposed strategy is also verified to benefit low carbon energy applications.