Dynamic VAR model-based control charts for batch process monitoring

Filho, Danilo Marcondes; Valk, Márcio

doi:10.1016/j.ejor.2019.12.038

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…The main methodology we adopt in this paper is the vector autoregression (VAR) model, which is an ad hoc dynamic multi-variable model analyzing a number of endogenous variables simultaneously, where each endogenous variable is regressed on its own lags and the lags of all other variables (Sims [ 34 ]; George et al, [ 35 ]; Marcondes and Valk [ 36 ]). The model is also widely applied in studying the relations among different commodity markets (see Alsalman [ 37 ]; Dolatabadi et al, [ 38 ]; Zhang and Lin [ 39 ]).…”

Section: Methodsmentioning

confidence: 99%

Impact persistence of stock market risks in commodity markets: Evidence from China

et al. 2021

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The risk spillover among financial markets has been noticeably investigated in a burgeoning number of literature. Given those doctrines, we scrutinize the impact persistence of volatility spillover and illiquidity spillover of Chinese commodity markets in this paper. Based on the sample from 2010 to 2020, we reveal that there is a cross-market spillover of volatility and illiquidity in China and also, interactions between volatility and illiquidity in different financial markets are pronounced. More importantly, we demonstrate that different commodity markets have different responsiveness to stock market shocks, which embeds their market characteristics. Specifically, we discover that the majority of the traders in gold market might be hedger and therefore gold market is more sensitive to stock market illiquidity shock and thus the shock impact in persistent. On the other hand, agricultural markets like corn and soybean markets might be dominated by investors and thus those markets respond to the stock market volatility shocks and the shock impact in persistent over 10 periods given the first period of risk shock happening. In fact, different Chinese commodity markets’ responsiveness towards Chinese stock market risk shocks indicates the stock market risk impact persistence in Chinese commodity markets. This result can help policymakers to understand the policy propagation effect according to this risk spillover channel and risk impact persistence mechanism in China.

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Section: Methodsmentioning

confidence: 99%

Impact persistence of stock market risks in commodity markets: Evidence from China

et al. 2021

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“…Over the years, and especially in recent times, numerous articles have been published on control charts to optimize process monitoring through various improvements, amendments, and adaptations to current practical needs, see e.g. Teoh et al [40], Mitra et al [31], Filho & Valk [18], Song et al [37,38], Nguyen et al [35], just to name a few relevant works.…”

Section: Introductionmentioning

confidence: 99%

The performance of the hypergeometric np chart with estimated parameter

Johannssen

Chukhrova

Castagliola

2022

European Journal of Operational Research

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“…Woodall and Thomas 39 , Roes and Does 34 , and Park 32 , among others, considered linear models with multiple sources of variation and developed control charts including Shewhart‐type

\bar{X}

charts for the mean. In multivariate applications, Mason and Young 27 , Jin and Guo 20 and Filho and Valk 12 , among others, have also considered process monitoring. These methodologies need to be adapted in view of the more recent knowledge in the literature.…”

Section: Introductionmentioning

confidence: 99%

A phase II EWMA chart for the balanced one‐way random effects model

Yao

Chakraborti

2022

Quality & Reliability Eng

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Statistical process control (SPC) techniques are used in applications where more than one sources of variation are present, for example, in a one‐way random effects (variance components) model. The Shewhart X¯$\bar{X}$ control charts for this situation have been studied by several researchers. However, exponentially weighted moving average (EWMA) charts are generally recommended in Phase II, as they are more sensitive to detecting smaller process mean shifts. We consider EWMA charts for the one‐way random effects model in the balanced case, properly accounting for the effects of parameter estimation while designing and implementing the control charts. In the sequel we derive and calculate the corrected (adjusted) control limits for Phase II applications. Following the recent literature, the limits are formulated under two perspectives, the unconditional and the conditional. Tabulations of the charting constants are provided under both perspectives along with an illustration with some actual data. A simulation study examining in‐control (IC) robustness performance is conducted under both perspectives. Finally, some conclusions are given. An R package is provided to support deployment of the proposed control chart in practice.

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Dynamic VAR model-based control charts for batch process monitoring

Cited by 19 publications

References 14 publications

Impact persistence of stock market risks in commodity markets: Evidence from China

Impact persistence of stock market risks in commodity markets: Evidence from China

The performance of the hypergeometric np chart with estimated parameter

A phase II EWMA chart for the balanced one‐way random effects model

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