Applied Stochastic Models in Business and Industry最新文献

In this article, we aim to provide a detailed econometric analysis of the realized volatility in international stock markets of Brazil, China, Europe, India, the United Kingdom, and the United States, which represent a mix of large developing, and developed markets. For our purpose, we use the functional data analysis (FDA) framework, whence discrete volatility data were first transformed into continuous functions, and thereafter, derivatives of the continuous functions were investigated, and kinetic and potential energy associated is the volatility system were extracted. Results revealed that COVID-19 indeed had a significant effect on international financial market volatility for all the countries, with the exception of China. The realized volatility of the international financial markets did return to their pre-COVID levels in May 2020, and this recovery time was significantly faster than the 2008 financial crisis recovery period. Within the FDA framework, we further investigated the role of uncertainty on the realized volatility, specifically from an outbreak of an infectious disease (such as COVID-19) and a daily newspaper-based infectious disease index as the predictor. The regression analysis showed that the volatility of financial markets can be accurately modeled by this infectious disease index, but only for periods experiencing an epidemic or pandemic.

Accelerated degradation tests (ADTs) are widely used for assessing the reliability of long-life products. During an ADT, accelerated stresses not only expedite the degradation of test products but also increase the likelihood of encountering traumatic shocks. Moreover, it is important to acknowledge that measurement errors can be inevitable during the observation process of an ADT. Unfortunately, these errors are often overlooked in the optimal design of the ADT, especially when multiple competing failure modes are present. In this article, we propose a new approach to design ADTs when measurement errors exist and test products suffer from degradation failures and random shock failures. We utilize the Wiener process to model the degradation path, incorporating normally distributed measurement errors, and an exponential distribution to fit the time between random shock failures. Given the number of test products and the termination time, we optimize the ADT plans under three common design criteria. The equivalence theorem is used to verify the optimality of the optimal ADT plans. A real-life example and sensitivity analysis are provided to illustrate our proposed method. The results demonstrate that when competing failure modes are present, the optimal ADT plans, which account for measurement errors, differ significantly from those that do not consider such errors.

Value at risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this article is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities—corn and soybean—enabling a comprehensive comparison of the VaR values in the presence and absence of jumps.

Longevity crucially affects demand for pensions, insurance products and annuities. Consistent empirical evidence shows that women have historically experienced lower mortality rates than men. In this article, we study a measure of the gender gap in mortality rates, we call “Gender Gap Ratio”, across a wide range of ages and for four countries: France, Italy, Sweden, and USA. We show the stylized facts that characterize the trend of the Gender Gap Ratio, both in its historical evolution and future projection. Focusing on an example temporary life annuity contract, we give a monetary consistency to the Gender Gap Ratio. We show evidence that a Gender Gap Ratio that ranges between 1.5 and 2.5, depending on age, translates into a significant reduction of up to 23% in the benefits from a temporary life annuity contract for women with respect to men, against the same amount invested in the life annuity. The empirical evidence discussed in this article documents the crucial importance of working toward a more widespread demographic literacy, for example, a range of tools and strategies to raise longevity consciousness among individuals and policy-makers, in the framework of gender equality policies.

Interest rate derivative pricing is a critical aspect of fixed-income markets, where efficient methods are essential. This study introduces a novel approach to pricing path-dependent interest rate derivatives within a broad class of affine jumps. The study's particular setting is the Fourier-cosine series (COS) method adaptation, which offers an accurate and computationally efficient method for pricing interest rate derivatives. The Fourier-cosine series approach can be used to compute probability density functions and option pricing with a linear computing complexity and exponential convergence rate. The lack of a quick and precise pricing technique for Asian interest rate options in diverse fixed-income market scenarios is a research gap that is being addressed. This approach closes this gap by providing quasi-closed and closed-form equations for a range of density and characteristic functions, resulting in precise pricing. The results demonstrate the versatility of the COS method in interest rate markets. Similar to what has been previously reported for stock options, the numerical findings demonstrate the extreme precision and computing speed of the pricing and hedging estimations provided here. This method is an innovative approach to interest rate derivative pricing, offering researchers and practitioners a powerful tool for efficiently calculating prices and calibrating options across strikes and maturities.

Random weighted $��k�$-out-of-$��n�$ systems are very useful in modeling the lifetimes of systems, wherein the success or failure of a system depends not only on its current operational status, but also on the contributions made by its components. In this paper, we consider random weighted $��k�$-out-of-$��n�$ systems with redundant components drawn randomly from a mixed population consisting of $��m�$ different subpopulations/substocks. We study different optimal allocation policies of active redundancies and minimal repair components in a random weighted $��k�$-out-of-$��n�$ system. Moreover, we investigate how the heterogeneity of subpopulations of items impacts the lifetime of a random weighted $��k�$-out-of-$��n�$ system. We also present some simulational results and a real data analysis for illustrative purpose.

Online transactions have become the dominant and most popular form of online payment in today's digital economy. Due to the growing popularity of e-commerce and the convenience it offers, both consumers and businesses are rapidly adopting online transactions. Notably, credit cards have become one of the most popular and standard online payment methods. However, it should be noted that credit card transactions are not without challenges. In particular, detecting and preventing fraudulent transactions is a major concern of the online payment system. It is difficult to find an effective detection model that can detect the new patterns created by fraudsters, due to the constant evolution of their methods to exploit the vulnerability of current security protocols. These fraud patterns are evolving and may not correspond to existing documented models, leading to a reduction in their identification. In addition, the customer's behavior can affect the model detection as it is susceptible to change based on factors such as economic conditions, trends, and individual circumstances. When consumers deviate from their typical behavior, the model may generate false alerts, thereby reducing its ability to differentiate between legitimate and fraudulent transactions. This article presents a new supervised detection model, called K-Fuse, which introduces an unsupervised phase in order to detect fraud patterns that may correspond to innovative models introduced by fraudsters. K-Fuse is a supervised classification method that fuses three steps consisting of (i)unsupervised clustering to identify hidden patterns of transactions in a dataset, (ii) a novel feature selection criterion based on the unsupervised results, and (iii) supervised classification to exploit the results of clustering and feature selection to predict new transactions as fraudulent or legitimate.

In this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location-scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value-at-risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location-scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real-world applications.