Model risk in real option valuation

Alexander, Carol; Chen, Xi

doi:10.1007/s10479-019-03273-4

Cited by 16 publications

(4 citation statements)

References 71 publications

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“…The topic of model risk, and model uncertainty more broadly, has commanded considerable attention across a diverse range of operations research and quantitative finance contexts. For example, healthcare network design (Denoyel et al 2017); logistics and transportation (Koks et al 2015); hazardous waste processors (Spear et al 1994;Cooke 2009;Piegorsch 2014); environmental risk assessors, health and safety, and engineering (Alexander and Sarabia 2012); climate change modelling (Reis and Shortridge 2020); and financial services and insurance firms (Huang et al 2010; Barrieu and Scandolo 2015;Coqueret and Tavin 2016;Alexander and Chen 2019). In the financial services industry, model use is widespread for trading, investment and hedging purposes (Aloui et al 2014;Atil et al 2014;Choukroun et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

et al. 2022

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An objective of model validation within organisations is to provide guidance on model selection decisions that balance the operational effectiveness and structural complexity of competing models. We consider a practice-relevant model validation scenario where a financial quantitative analysis team seeks to decide between incumbent and alternative models on the basis of parameter risk. We devise a model risk management methodology that gives a meaningful distributional assessment of parameter risk in a setting where market calibration and historical estimation procedures must be jointly applied. Such a scenario is typically driven by data constraints that preclude market calibration only. We demonstrate our proposed methodology in a natural gas storage modelling context, where model usage is necessary to support profit and loss reporting, and to inform trading and hedging strategy. We leverage our distributional parameter risk approach to devise an accessible technique to support model selection decisions.

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Section: Introductionmentioning

confidence: 99%

Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

et al. 2022

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“…L. Li et al, 2019;Lu et al, 2020;Whitman, 2000), environmental science and engineering (Fisher et al, 2003;Hu et al, 2014;Huang et al, 2018;Lee et al, 2005;Rose & Gerba, 1991;Rodrigues et al, 2015;Troldborg et al, 2017;Zazouli et al, 2021), chemical engineering and technology (Cremer & Warner, 1981;Cozzani et al, 2005;De Silva et al, 2017;Roy & Kshirsagar, 2021), traffic engineering (Benekos & Diamantidis, 2017;Chakrabarti & Parikh, 2013a, 2013bFabiano et al, 2002), hydrocarbon storage and transportation engineering (Gong et al, 2020;Pei et al, 2018;Vanem et al, 2008), ship and ocean engineering (Guarin et al, 2009;Trbojevic, 2006Trbojevic, , 2009van Urk & de Vries, 2000;J. Wang, 2001), operations research (Alexander & Chen, 2021;Jose et al, 2008;; H. F. Wang & Hsu, 2009;Xu et al, 2014), and management science (Grauer, 1985;Heckmann et al, 2015;Huang et al, 2013Huang et al, , 2019R. O. Murphy & ten Brincke, 2018).…”

Section: Introductionmentioning

confidence: 99%

Exploring acceptable risk in engineering and operations research and management science by bibliometric analysis

et al. 2022

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As the decision-making basis for "safety" in risk management and risk assessment activities, acceptable risk has always been an important topic of risk-related research. Based on the records in the Science Citation Index Expanded database and Social Sciences Citation Index database via the Web of Science Core Collection, 1124 articles or reviews related to acceptable risk in engineering and operations research and management science were retrieved. These documents, published between 1961 and 2021, covered 3056 authors, 75 countries/territories, 1296 institutions, and 323 journals. In this study, bibliometric data such as annual growth trends were analyzed by using descriptive statistics. Through the co-occurrence maps created by CiteSpace and Gephi, the most productive and influential countries/territories, institutions, and authors as well as their cooperation networks were identified. Further analysis was conducted to determine the core publications and publication sources in this field through cocitation analysis. Insights into focus areas and research topics over time were obtained through keyword co-occurrence analysis. This study provides a macroscopic overview of acceptable risk research and may help researchers better understand this research field and predict its dynamic directions.

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“…Different methods for incorporating subjective information have been proposed, leading to many variants. See Copeland and Antikarov (2003); Morellec (2007, 2013); Evans et al (2008); Chronopoulos et al (2011); Grasselli (2011); Choi et al (2017); Alexander and Chen (2021) and others, and Borison (2005) for a comprehensive review. These models use individual inputs, although some variables could still rely on commonly-observed prices, or rates, or other 'objective' inputs.…”

Section: Introductionmentioning

confidence: 99%

Risk-Adjusted Valuation for Real Option Decisions

Alexander¹,

Chen²,

Ward³

2021

Preprint

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We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical realoption valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.

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Model risk in real option valuation

Cited by 16 publications

References 71 publications

Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

Practice-relevant model validation: distributional parameter risk analysis in financial model risk management

Exploring acceptable risk in engineering and operations research and management science by bibliometric analysis

Risk-Adjusted Valuation for Real Option Decisions

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