Risk preferences estimation of exporting firms under exchange rate uncertainty

2021

Decision Sciences

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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).

“…One can see Broll and Mukherjee (2017), Broll et al. (2020), and Mukherjee et al. (2020) in this context.…”

Section: Econometric Estimationmentioning

confidence: 83%

“…In this comparative static analysis, we have derived the propositions using the implicit partial differentiation technique following the works of Broll and Mukherjee (2017), Broll et al. (2020), Eichner and Wagener (2009, 2011, 2012), and Mukherjee et al. (2020).…”

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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“…Therefore, this paper focuses on modelling the attitude of a risk averse manufacturer to obtain optimal supply decision under tightly versus loosely interconnected risks utilising the two-moment (i.e., mean-standard deviation) decision theoretic approach. This modelling approach has also been applied by Eichner & Wagener, 2003, 2009Alghalith et al, 2017;Broll & Mukherjee, 2017;Broll et al, 2019;Huang & Jiang, 2020;Padhi & Mukherjee, 2021; among a few of the many. In such decision-theoretic modelling approach, the preferences over random distributions of the objective function are represented by the utility function, which is defined over only the mean and standard deviation of the objective function.…”

Section: Interconnected Sc Risksmentioning

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%

“…To answer the three research questions mentioned above, we have used a two-moment (or equivalently, a mean-standard deviation) decision-theoretic approach (see, for example, Eichner & Wagener, 2003, 2009Alghalith et al, 2017;Broll & Mukherjee, 2017;Broll et al, 2019;Padhi & Mukherjee, 2021). In such decision-theoretic modelling approach, the preferences over random distributions of the objective function are represented by the utility function, which is defined over only the mean and the variance (or standard deviation) of the objective function.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.