Certificate pricing using Discrete Event Simulations and System Dynamics theory

The study proposes an innovative application of Discrete Event Simulations (DES) and System Dynamics (SD) theory to the pricing of a certain kind of certificates very popular among private investors and, more generally, in the context of wealth management. The paper shows how numerical simulation software mainly used in traditional engineering, such as industrial and mechanical engineering, can be successfully adapted to the risk analysis of structured financial products. The article can be divided into three macro-sections: in the first part a synthetic overview of the most widespread option pricing models in the quantitative finance branch is given to the readers together with the fundamental technical-instrumental background of the implemented DES and SD simulator. After dealing with some of the most popular models adopted for Equity and Equity index options, which are the most common underlying assets for the certificates structuring, we move, in the second part, to describe how the mathematical models can be integrated into a general simulation environment able to provide both DES and SD extensively used in the engineering field. The core stochastic differential equation (SDE) will therefore be translated, together with all its input parameters, into a visual block model which allows an immediate quantitative analysis of how market parameters and the other model variables can change over time. The possibility for the structurer to observe how the variables evolve day-by-day gives a strong sensitivity to evaluate how the price and the associated risk measures can be directly affected. The third part of the study compares the results obtained from the simulator designed by the authors with the more traditional pricing approaches, which consist in programming Matlab® codes for the numerical integration of the core stochastic dynamics through a Euler-Maruyama scheme. The comparison includes a price check using the Bloomberg® DLIB pricing module and a check directly against the valuation provided by the counterparty. In this section, real market cases will therefore be examined with a complete quantitative analysis of two of the most widespread categories of certificates in wealth management: Multi-asset Barrier Reverse Convertible with Issuer Callability and Multi-asset Express Certificate with conditional memory fixed coupon.

Staffing and Capacity Planning for SARS-CoV-2 Monoclonal Antibody Infusion Facilities: A Performance Estimation Calculator based on Discrete-Event Simulations

Objective: The COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and Methods: Using real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results: 162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥ 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. Discussion: Physical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. Conclusion: Simulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

Local Virtual Times Analysis in PCS Model

Efficient scalability and process synchronization are critical for achieving high performance in distributed computing environments. Analysis of the scalability is usually done using intensive case studies, which give an answer only for the particular set of model parameters. We found an efficient way to analyze the time evolution in models simulated with the Parallel Discrete Event Simulations (PDES) approach. The essential feature of PDES is the concept of local virtual time (LVT) associated with the evolution of each process of the model. The LVT of processes evaluates in simulations and forms a complicated profile.These profiles remind the profiles of the surface growth in the physical devices. In physics, researchers use the concept of universality, which helps to divide the different regimes of the class's surface growth—each class is described by some universal laws and does not depend on the details of the model. We demonstrate the applicability of this concept and present a model of LVT profile evolution in Personal Communication Service (PCS) model. The PCS network consists of a square grid of radio ports that serve users in their zone (cell). We build the LVT-PCS model, which describes the evolution of the LVT profile associated with the PCS model. We simulate the PCS model using the ROSS simulator (optimistic PDES) and compare results with those simulated by our LVT-PCS model. We found the profile demonstrates property, which is known in physics as roughening transition. We estimate the values of ``critical’’ exponents for two models, which seem to belong to the same universality class. We believe that the similarity we found can be helpful for the preliminary analysis of the model scalability, process desynchronization, and possible deadlocks.

Local Virtual Times Analysis in PCS Model

Efficient scalability and process synchronization are critical for achieving high performance in distributed computing environments. Analysis of the scalability is usually done using intensive case studies, which give an answer only for the particular set of model parameters. We found an efficient way to analyze the time evolution in models simulated with the Parallel Discrete Event Simulations (PDES) approach. The essential feature of PDES is the concept of local virtual time (LVT) associated with the evolution of each process of the model. The LVT of processes evaluates in simulations and forms a complicated profile.These profiles remind the profiles of the surface growth in the physical devices. In physics, researchers use the concept of universality, which helps to divide the different regimes of the class's surface growth—each class is described by some universal laws and does not depend on the details of the model. We demonstrate the applicability of this concept and present a model of LVT profile evolution in Personal Communication Service (PCS) model. The PCS network consists of a square grid of radio ports that serve users in their zone (cell). We build the LVT-PCS model, which describes the evolution of the LVT profile associated with the PCS model. We simulate the PCS model using the ROSS simulator (optimistic PDES) and compare results with those simulated by our LVT-PCS model. We found the profile demonstrates property, which is known in physics as roughening transition. We estimate the values of ``critical’’ exponents for two models, which seem to belong to the same universality class. We believe that the similarity we found can be helpful for the preliminary analysis of the model scalability, process desynchronization, and possible deadlocks.

Probabilistic Model for the Lifetime Prediction of IoT Devices

The development of new electronics and communications technologies allows the Internet of Things (IoT) concepts to be put into practice. A large amount of information is expected to travel over the internet from intelligent objects to many different kinds of software and cloud computing applications. However, there are still several challenges to be overcome. Among them, management of devices energy consumption is intriguing. Therefore, a model to estimate the range of probabilities of the energy spent by IoT equipment is presented in this work. The results include the formulation of probability distributions and discrete event simulations. The proposed method can be applied to design and evaluate IoT applications.