Managing risks from climate impacted hazards – The value of investment flexibility under uncertainty

Truong, Chi; Trück, Stefan; Mathew, Supriya

doi:10.1016/j.ejor.2017.07.012

Cited by 29 publications

(8 citation statements)

References 45 publications

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“…Recent papers close to our contribution are References 11,13,14 and 15 provide an empirical dynamic assessment of model risk for a range of US financial assets, proposing a measure of model risk defined as the ratio of the maximum over the minimum VaR. Contrary to what we do here, they do not consider a reference estimate and implicitly give all models the same level of plausibility, thus making the initial choice of the competing models more crucial than in our paper; we view this as a drawback of their approach.…”

Section: Introductionmentioning

confidence: 83%

“…In addition, the unconditional distributions of returns and price changes are definitely not normal. 15 Furthermore, the inspection of empirical autocorrelation and partial autocorrelation functions, together with the time-structure and non-normality of the data, suggest us to select autoregressive processes for the conditional mean and a GARCH process for the conditional variance. For all series X t , where either X t = R t (log-returns, for the first five assets/indices) or X t = Y t (price differences for electricity), we consider K = 9 competing models.…”

Section: Data and Competing Modelsmentioning

confidence: 99%

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Assessing model risk in financial and energy markets using dynamic conditional VaRs

Gianfreda,

Scandolo

2023

Appl Stoch Models Bus & Ind

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It has been recognized that model risk has an important effect on any risk measurement procedures, particularly when dealing with complex markets and in the presence of a wide range of implemented models. We consider a normalized measure of model risk for the forecast of daily Value‐at‐Risk, combined with a model selection and an averaging procedure. This allows us to restrict the set of plausible models on a daily basis, making the initial choice of competing models less crucial and then yielding a more reliable assessment of model risk. Using AR‐GARCH‐type models with different distributions for the innovations, we assess the dynamics of model risk for different financial assets (a stock, an equity index, an exchange rate) and commodities (electricity, crude oil and natural gas) over 15 years.

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Section: Introductionmentioning

confidence: 83%

Section: Data and Competing Modelsmentioning

confidence: 99%

Assessing model risk in financial and energy markets using dynamic conditional VaRs

Gianfreda,

Scandolo

2023

Appl Stoch Models Bus & Ind

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“…The various ways in which biodiversity may directly lower risks, e.g. associated with poverty (SDG 1), ill health of the workforce (SDG 3), cities (SDG 11) or climate change (SDG 13), may induce greater financing by the private sector 112 (SDG 17). Finally, how biodiversity directly contributes to delivery of a range of products, e.g.…”

Section: Indirect Contributions Of Biodiversitymentioning

confidence: 99%

Biodiversity’s contributions to sustainable development

et al. 2019

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“…The researchers proposed an economic framework to determine the optimal time to invest in climate disaster-prevention infrastructure by using ROA. They determined the optimal investment sequence of different alternative projects (Truong et al 2018). Linnerooth-Bayer et al (2009) asserted that insurance tools would provide opportunities for developing countries to avoid disasters such as drought, flood, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They determined the optimal investment sequence of different alternative projects (Truong et al. 2018). Linnerooth‐Bayer et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A Real Options‐Based Decision‐Making Model for Infrastructure Investment to Prevent Rainstorm Disasters

Wang

Liu

Zhang

et al. 2019

Production and Operations Management

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Extreme precipitation caused by global climate change is expected to have a severe impact on urbanized areas. While decision‐makers struggle with climate uncertainty, an effective infrastructure adaptation strategy attaches great importance to preventing disasters resulting from rainfall. We propose a decision‐making model to incorporate the probability of rainfall disasters and recommend investing time when evaluating projects related to climate adaptation. We use a hydrological statistical model and economic and technical factors to estimate the expected economic losses in several rainfall disaster scenarios, and the value of the adaptation infrastructures is calculated using a real options pricing approach. Then the decision‐making model is applied to a case study involving a campus rainfall disaster prevention facility at the Central University of Finance and Economics in Beijing, China. We established three submerged scenarios with different rainfall intensities, then we evaluated the premium of holding an option to defer and pointed out the optimal investing time in each scenario. This model is expected to provide guidance for the development of adaptation infrastructure for relatively small areas such as communities and universities. And we proved that using real options‐based approach could provide more managerial flexibility for investors.

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Managing risks from climate impacted hazards – The value of investment flexibility under uncertainty

Cited by 29 publications

References 45 publications

Assessing model risk in financial and energy markets using dynamic conditional VaRs

Assessing model risk in financial and energy markets using dynamic conditional VaRs

Biodiversity’s contributions to sustainable development

A Real Options‐Based Decision‐Making Model for Infrastructure Investment to Prevent Rainstorm Disasters

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