Applied Stochastic Models in Business and Industry

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2626

Liu, Haitao; Zou, Jian; Ravishanker, Nalini

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2644

Clustering large financial time series data enables pattern extraction that facilitates risk management. The knowledge gathered from unsupervised learning is useful for improving portfolio optimization and making stock trading recommendations. Most methods available in the literature for clustering financial time series are based on exploiting linear relationships between time series. However, prices of different assets (stocks) may have non‐linear relationships which may be quantified using information based measures such as mutual information (MI). To estimate the empirical mutual information between time series of stock returns, we employ a novel kernel density estimator (KDE) based jackknife mutual information estimation (JMI), and compare it with the widely‐used binning method. We then propose an average distance gradient change algorithm and an algorithm based on the average silhouette criterion that use pairwise and groupwise MI of high‐frequency financial stock returns. Through numerical studies, we provide insights into the impact of the clustering on asset allocation and risk management based on the nonlinear information structure of the US stock market.

Callegaro, Giorgia; Mazzoran, Andrea; Sgarra, Carlo

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2645

We propose and investigate two model classes for forward power price dynamics, based on continuous branching processes with immigration, and on Hawkes processes with exponential kernel, respectively. The models proposed exhibit jumps clustering features. Models of this kind have been already proposed for the spot price dynamics, but the main purpose of the present work is to investigate the performances of such models in describing the forward dynamics. We adopt a Heath–Jarrow–Morton approach in order to capture the whole forward curve evolution. By examining daily data in the French power market, we perform a goodness‐of‐fit test and we present our conclusions about the adequacy of these models in describing the forward prices evolution.

Luo, Qinglin; Luo, Jianwen

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2646

As more and more products are sold, the management of product returns plays a significant role in firms' operational decisions. We consider a capital‐constrained newsvendor model to explore the impact of product returns on firms' operational and financial decisions, in which a capital‐constrained retailer can get financed from either banks or suppliers and refurbish returned products and resell them to customers. Our findings show that offering refurbishment for returned products can increase the retailer's order quantity and improve the capital‐constrained supply chain performance. Moreover, the supplier strategically designs the wholesale price to hinder the retailer refurbishing returned products although the retailer always chooses to provide refurbishment under trade credit. When only trade credit financing is available, there exists a Pareto improvement on both partners' profits under certain cases. In addition, we investigate the financing preferences of the supply chain and find that product returns have a significant influence on the unique financing equilibrium. Finally, we conduct numerical analyses to present the impacts of some key parameters on the performance of both supply chain members.

Herath, Hemantha S. B.; Jahera, John S.

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2647

A primary challenge for forestry businesses is valuing timber harvesting contracts. This article presents an empirical application of determining the optimal harvest volumes under stochastic prices to determine the economic value of timber harvesting contracts and manage business risk using real options theory. We illustrate a dynamic optimization solution procedure and the choice between a single long‐term versus two short‐term timber harvesting contracts from a risk management perspective. A case study of timber harvesting contracts sold in British Columbia is used to demonstrate specification of the many details and adaptations that are required in such valuation problems. Our article offers some interesting results. The highest timber harvesting contract values occur with optimal harvest quantities rather than an equal annual allowable cut (AAC). The difference in values can be substantial (two to three‐fold). Consequently, forestry businesses can benefit by timing their harvest to nonequal quantities in later years of a timber harvesting contract. Depending on the quality of timber, the maximum stumpage value for the second short‐term timber harvesting contract ranges from $12 to $17.8 per cubic meter, resulting in lower profit margins for equal AAC. In contrast, with optimal harvesting, the maximum stumpage value ranges from $2 to $8.5 per cubic meter and higher profit margins. These bounds for stumpage price and profit margins would be useful for forest businesses to better manage business risks when bidding for timber harvesting contracts.

Chu, Amanda M. Y.; Ip, Chun Yin; Lam, Benson S. Y.; So, Mike K. P.

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2650

In this article, we extend the skew‐t data perturbation (STDP) to develop a new statistical disclosure control (SDC) method for data with continuous variables. In this new SDC method, we construct an extended skew‐t (EST) copula to release confidential data for third‐party usage. Using the EST copula for producing perturbed data, we can incorporate rich statistical information in the perturbed data while preserving the marginal distributions of the data. An advancement of this EST‐SDC method is to use a copula distribution, which allows generation of perturbed data from bivariate conditional EST copulas sequentially. We discuss the methodology of EST‐SDC and outline some statistical properties derived from copula theories. Simulations and a real data study are included to demonstrate how the EST‐SDC method can be applied and to compare with the STDP method.

Machado, Marcela A. G.; Lee Ho, Linda; Quinino, Roberto C.; Celano, Giovanni

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2651

Two versions of Phase II attribute+variable (DVMAX) control charts are investigated for monitoring the covariance matrix ∑ of bivariate processes. Monitoring always starts with an attribute chart employing the Max D control chart and, depending on the outcome, a variable control chart named VMAX chart is run at a second stage to check for process stability. In the first version, denoted as the DVMAX1 chart, two independent samples are used at the two stages of the same inspection; with the second version, denoted as the DVMAX2 chart, the same sample is used at both the first and second stage of the same inspection. This approach, based on the implementation of two types of charts, can be designed to be more advantageous than a single variable control chart in terms of detection speed of a shift in the covariance matrix. In general, we conclude that the DVMAX1 control charts not only shows the best statistical performance but also presents a lower average sampling cost. A numerical example illustrates the implementation of the proposed control charts.

Zhou, Congjin; Wang, Guojing; Guo, Jie

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2652

We investigate an optimal refinancing problem for an interest‐only mortgage, where the market mortgage rate is defined as the classical Black‐Scholes geometric Brownian motion with regime switching. We verify that the optimal refinancing strategy only depends on the quotient between the market mortgage rate and the borrowing rate. We show that the optimal refinancing strategy is of threshold type. By reducing the two‐dimensional optimal refinancing problem to a one‐dimensional problem, we obtain the system of equations that the function with respect to quotient satisfies. Analytic solutions are obtained in one‐ and two‐regime cases. Finally, we present some numerical results to illustrate the influence of the model parameters on the optimal refinancing strategy.

Glynn, Chris

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2653

House price indices (HPIs) are statistical measures of real estate price dynamics in defined geographic regions over defined periods of time. HPIs are important metrics that help policymakers, mortgage lenders, real estate investors, and bank regulators monitor market conditions and manage risk. HPIs that are local, reliable, and timely are essential in understanding connections between housing markets and the broader economy. In this article, we examine the algorithmic construction of Zillow's Home Value Index (ZHVI), an HPI built on black box machine learning algorithms. To provide deeper statistical insight into ZHVI than afforded by its black box construction, we develop a Bayesian generative meta‐model that approximates the black box construction of ZHVI series in 100 metropolitan areas (metros). Each ZHVI series is modeled with a global trend, a finite mixture of Gaussian processes, and a local component. We find that there are three shared dynamic patterns across the 100 markets in our analysis, and we utilize this shared latent structure to forecast ZHVI in each metro 12 months ahead. Our clustering strategy has two advantages: (i) it allows us to construct composite HPIs where member metros are learned from the data rather than predetermined; and (ii) it allows us to estimate the relative contributions of cluster‐level and metro‐specific components to a metro's ZHVI, providing a novel statistical attribution of real estate market dynamics.

Romano, Elvira; Giraldo, Ramón; Mateu, Jorge; Diana, Andrea

2022 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2654

The presence of curves that deviate markedly from the core of a set of curves can greatly affect inference and forecasting in a functional regression model. Thus their detection is key to increase the accuracy of the required estimates. This work introduces the concepts of high leverage in general functional regression models with independent and spatially correlated errors. The projection matrix, also known as Hat matrix, plays a crucial role in classical model diagnosis, since it provides a measure of leverage. We propose a generalisation of the projection matrix in both the functional and the spatial functional frameworks under two settings, when the response variable is a scalar, and when it is a function itself, the so‐called total model. Commonly used influence measures are also proposed as functions of the generalised functional leverages and residuals. An application of the proposed procedures for investigating the effect of outliers on the relationship between transformation of the banking industry and the size of cooperative banks in Italy over a period of 14 years is presented.