This paper describes the peak, fat tail, and skewness characteristics of asset price via a Lévy process. It applies asymmetric GARCH model to depict asset price's random volatility characteristics and builds a GARCH-Lévy option pricing model with random jump characteristics. It also uses circular maximum likelihood estimation technology to improve the stability of model parameter estimation. In order to test the model's pricing results, we use Hong Kong Hang Seng Index (HSI) price data and its option data to carry out empirical studies. Results prove that the pricing bias of EGARCH-Lévy model is lower than that of standard HestonNandi (HN) model in the financial industry. For short-term, middle-term, and long-term European-style options, the pricing error of EGARCH-Lévy model is the lowest.

The convergence of physical stores and e-commerce has led to the emergence of a new retail business mode in the retail industry. In today’s world, new retail supply chains face the potential risks of disruption caused by natural and man-made disasters, and epidemics. In this paper, we simulate a three-stage new retail supply chain consisting of suppliers, manufacturers, and a retailer with online and offline channels in the AnyLogistix simulation and optimization software. We develop a simulation model to analyze the effects of various supply chain node disruptions on new retail supply chain performance and service level with consideration of four scenarios: disruption-free; manufacturer disruption; warehouse center disruption; offline store disruption. The main results show that supply chain node disruptions have negative impacts on the performance and service level. Besides, the warehouse center disruption has the most devastating effect on this new retail supply chain. Overall, this paper provides insights for decision-makers to consider disruption issues when designing resilient new retail supply chains.

The COVID-19 pandemic has caused severe consequences such as long-term disruptions and ripple effects on regional and global supply chains. In this paper, firstly, we design simulation models using AnyLogistix to investigate and predict the pandemic’s short-term and long-term disruptions on a medical mask supply chain. Then, the Green Field Analysis experiments are used to locate the backup facilities and optimize their inventory levels. Finally, risk analysis experiments are carried out to verify the resilience of the redesigned mask supply chain. Our major research findings include the following. First, when the pandemic spreads to the downstream of the supply chain, the duration of the downstream facilities disruption plays a critical role in the supply chain operation and performance. Second, adding backup facilities and optimizing their inventory levels are effective in responding to the pandemic. Overall, this paper provides insights for predicting the impacts of the pandemic on the medical mask supply chain. The results of this study can be used to redesign a medical mask supply chain to be more resilient and flexible.