Spot exchange rate volatility, uncertain policies and export investment decision of firms: a mean-variance decision approach

2021

Decision Sciences

Self Cite

Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well-known Duckworth-Lewis-Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two-round questionnaire survey among 300 purchase managers. The means and variances of the participants' estimates of probabilities of meeting the revised targets within the scheduled time for various model-generated supply scenarios were used to find the participants' risk preferences. In the second-round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches-one, at the group level by estimating the within-and the between-group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk-return trade-off distributions. K E Y W O R D SDuckworth-Lewis-Stern method, mean-variance decision-theoretic model, portfolio of risky options, risk preference structures, supply disruption 1 1 https://shodhganga.inflibnet.ac.in/bitstream/10603/147247/14/14%20leather% 20craftsmanship-sustainable%20livelihood.pdf 2 https://www.automotivelogistics.media/flooding-in-chennai-causes-plant-closuresand-supply-disruption/14135.article (accessed 4 June 2019).

Section: Comparative Staticsmentioning

confidence: 99%

Optimal portfolio choices to split orders during supply disruptions: An application of sport's principle for routine sourcing

Padhi¹,

2021

Decision Sciences

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“…Z can be categorized as the background risk that influences the manufacturer's input choice decision over which the manufacturer has no control, by affecting the total operational cost of input usage. As an example of such risk affecting the (operational) cost of production, one may look at Mukherjee et al (2021). 2 In the present context, this follows from the sourcing decision approach to interconnected risks in a SC system.…”

Section: End-of-period Perfect Competitionmentioning

confidence: 99%

“…Under the chosen MSU, one can easily categorise different kinds of risk preferences (risk aversion, risk neutrality and risk affinity) and various degrees of absolute and relative risk aversions. Therefore, for exemplification purpose, our parametric representation of risk preferences works fine (see Eichner & Wagener, 2009;Broll et al, 2015;Broll & Mukherjee, 2017;Broll et al, 2019;Mukherjee et al, 2021;Padhi & Mukherjee, 2021 for application of this MSU to exemplify the pattern of risk preferences in a nonlinear mean-standard deviations framework). The F.O.C.…”

Section: A Parametric Example To Carry Out Sensitivity Analysis Of Risk-takingmentioning

confidence: 99%

“…According toMukherjee et al (2021), variations in firm size, age and uncertain changes in the industryspecific policies over time (due to changes in management regimes) are going to affect the fixed operational cost of production (in the context ofMukherjee et al, 2021, for exportable) at the firm-level, over which the firm has no control.…”

mentioning

confidence: 99%

“…Given a von-Neumann Morgenstern (vNM) utility index w : R → R, one can write the expected utility emanating from the distribution of y using the mean and the standard deviation of y (see Eichner & Wagener, 2003, 2009Mukherjee et al, 2021):…”

mentioning

confidence: 99%

See 2 more Smart Citations

Sourcing decision under interconnected risks: an application of mean–variance preferences approach

Padhi

2022

Ann Oper Res

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Supply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations.

A risk–return trade‐off or co‐movement? Are food processing firms risk‐averse?

Parhi

et al. 2023

Int. J Fin Econ

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We build a two‐moment decision‐theoretic framework to study how firms in the food‐processing industry negotiate between risk and return while relying on imported inputs for production at an intensive margin. Two possibilities emerge: either a co‐movement or a trade‐off in risk and return under various industry and economic conditions. Building on our theoretical setting, we design a testable empirical framework that considers a panel of 316 firms in the Indian food‐processing industry between 1993 and 2009. We find strong evidence of a decrease in the absolute risk aversion preference, although the magnitude varies measurably across firms.