Applied Stochastic Models in Business and Industry最新文献

Monroe (1978) demonstrates that any local semimartingale can be represented as a time-changed Brownian Motion (BM). A natural question arises: does this representation theorem hold when the BM and the time-change are independent? We prove that a local semimartingale is not equivalent to a BM with a time-change that is independent from the BM. Our result is obtained utilizing a class of additive processes: the additive normal tempered stable (ATS). This class of processes exhibits an exceptional ability to calibrate the equity volatility surface accurately. We notice that the sub-class of additive processes that can be obtained with an independent additive subordination is incompatible with market data and shows significantly worse calibration performances than the ATS, especially on short time maturities. These results have been observed every business day in a semester on a dataset of S&P 500 and EURO STOXX 50 options.

In a real options framework, we analyze the behavior of a large energy producer who can invest in a portfolio of Renewable Energy Source (RES) and dirty energy source. Competitive fuel prices challenge the investments in RES. Given a budget constraint, the agent allocates the optimal capacities of both energy instalments and selects the optimal investment time. We use the model to compare the effectiveness of classical support schemes such as Feed-in Tariffs or Green Certificate with respect to forms of taxation of dirty technology such as Carbon Taxes or Carbon Permits. This paper proposes a conceptual framework and qualitative analysis to understand which support system enhances the attractiveness of renewable energy investments.

A method for constructing exact simulated confidence intervals is presented, valid for situations with both discrete and continuous observations. The idea of the method is to invert a data generating function, which needs not be monotone, and where special attention is taken when the data generating function contains jumps. The method is applied to obtain exact confidence intervals for certain types of censored data from exponential distributions. The censoring schemes under study are earlier treated in the literature, and a comparison to these approaches is considered. The connection to fiducial inference is discussed, and a difference in the paradigm of obtaining intervals for the parameter is studied.

The digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision-making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. This real-world application demonstrates the effectiveness of our approach in identifying and prioritizing Digital Twin applications in a complex industrial context. This research contributes to the growing body of knowledge surrounding Digital Twins, providing organizations with a structured approach to leverage the potential of this transformative technology.

Numerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel-based methods, such as the Hilbert-Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization-based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models.