journal article
LitStream Collection
Home
Benth, Fred Espen; Lempa, Jukka
2023 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.2815
This paper is concerned with managing risk exposure to temperature using weather derivatives. We consider hedging temperature risk using so‐called HDD‐ and CDD‐index futures, which are instruments written on temperatures in specific locations over specific time periods. The temperatures are modelled as continuous‐time autoregressive (CARMA) processes and pricing of the hedging instrument is done under an equivalent pricing measure. We develop hedging strategies for locations, cutoff temperatures, and time periods different to the ones in the traded contracts, allowing for more flexibility in the hedging application. The dynamic hedging strategies are expressed explicitly by the term structure of the volatility. We also provide numerical case studies with temperatures following a CAR(3)‐process to illustrate the temporal behaviour of the hedge under different scenarios.
Silveira Netto, Carla Freitas; Brei, Vinicius A.; Hyndman, Rob J.
2023 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.2823
One of the most challenging aspects for managers when building a forecasting system is choosing how to aggregate the data at different levels. This is frequently done without the manager knowing how these choices can compromise the system's accuracy. This article illustrates these compromises by comparing different structures and aggregation criteria. Our article proposes and empirically tests a framework on how to build a coherent and more accurate forecasting system. The framework's first phase compares different time series forecasting methods, including statistical, “standard” machine learning, and deep learning. Results show that one of the statistical methods (autoregressive integrated moving average, or, for short, ARIMA) outperforms machine and deep learning methods. The second phase compares different combinations of aggregation criteria, structures of the forecasting system, and coherent forecast methods (i.e., adjustments to the forecasts at different levels of aggregation). The results show that using different criteria and structures indeed impacts predictions' accuracy. When it is necessary to disaggregate the forecast, our results show that it is best to add more information in a grouped structure, adjusted by a bottom‐up method. This combination provides the best performance, that is, the lowest mean absolute‐scaled error (MASE) in most nodes, compared to the other structures and coherent forecast methods used. The results also suggest that aggregating the time series further by geographical regions is essential to improve accuracy when forecasting products' and channels' sales.
Cha, Ji Hwan; Finkelstein, Maxim
2023 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.2819
Minimally repaired items are considered. In practice, minimal repair can be unsuccessful, and in this case, it should be repeated. The Polya‐Aeppli process, which is a generalization of the Poisson process is used in the article for the corresponding modeling. Some properties, useful for optimal maintenance, are derived. An important generalization to the case when the probability of the unsuccessful attempt is time‐dependent is described. An application of the derived results to obtaining the optimal time of replacement for a system with multiattempt minimal repairs is discussed. The study is illustrated by detailed numerical examples.
Panja, Arindam; Kundu, Pradip; Pradhan, Biswabrata
2023 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.2822
An effective way to increase system reliability is to use redundancies (spares) into the systems either in component level or in system level. In this prospect, it is a significant issue that which set of available spares providing better system reliability in some stochastic sense. In this paper, we derive sufficient conditions under which a coherent system with a set of active redundancy at the component level or the system level provide better system reliability than that of the system with another set of redundancy, with respect some stochastic orders. We have derived the results for the component lifetimes following accelerated life (AL) model. The results obtained help us to design more reliable systems by allocating appropriate redundant components from the set of available options for the same. Various examples satisfying the sufficient conditions of the theoretical results are provided. Some results are illustrated with real‐world data.
2023 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.2814
This article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational losses, exhibiting standard stochastic properties and tail behavior that closely align with actual operational losses. At an aggregate level, the model endeavors to replicate the average frequency and severity of losses observed within the internal fraud—retail banking category. Moreover, we identify macro‐environmental factors that exert influence over the severity and frequency of model‐simulated losses, consistent with findings in the existing literature.