Should we do more when we know less? The effect of technology risk on optimal effort

Li, Lü; Peter, Richard

doi:10.1111/jori.12339

Cited by 15 publications

(7 citation statements)

References 93 publications

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“…Additionally, note that decision makers in Ehrlich and Becker's (1972) setting are assumed to have precise information about the benefits of risk mitigation. In contrast, Li and Peter (2021) analyze self-insurance and self-protection activities where the effectiveness of risk mitigation is subject to exogenous environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Comparative risk aversion in two periods: An application to self‐insurance and self‐protection

Huber

2021

J of Risk & Insurance

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Risk management decisions provide a means to elicit individuals' risk preferences empirically. In such a context, the literature often presumes that the decision to invest in risk management and the benefit of this investment occur contemporaneously. There is, however, no consensus in the theoretical literature that one-period results can be transferred to intertemporal settings. To address this gap, we study the effect of an increase in risk aversion on the demand for risk management in a two-period context. Our findings reproduce the one-period results and, thus, support the focus of previous empirical literature on the structure of the risk rather than on the timing of investments and benefits. We also contrast our results with those obtained by employing widely used but limited preferences to examine risk aversion in intertemporal settings (standard additive expected utility setting, Selden, Epstein and Zin).

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Section: Introductionmentioning

confidence: 99%

Comparative risk aversion in two periods: An application to self‐insurance and self‐protection

Huber

2021

J of Risk & Insurance

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“…In real life, however, promises are not always kept. Reasons for nonperformance include insurer default, claims being denied or contested, delays in the claims handling process, and contractual uncertainty when it comes to the interpretation of the insurance contract (see Schlesinger 2013;Li and Peter 2021). Experiments have shown that individuals tend to react strongly to nonperformance risk, purchasing less insurance than predicted by EU.…”

Section: Nonperformance Riskmentioning

confidence: 99%

Probability weighting and insurance demand in a unified framework

Jaspersen

Peter

Ragin

2022

Geneva Risk Insur Rev

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We provide a comprehensive analysis of the impact of probability weighting on optimal insurance demand in a unified framework. We identify decreasing relative overweighting as a new local condition on the probability weighting function that is useful for comparative static analysis. We discuss the effects of probability weighting on coinsurance, deductible choice, insurance demand for low-probability, high-impact risks versus high-probability, low-impact risks, and insurance demand in the presence of nonperformance risk. Probability weighting can make better or worse predictions than expected utility depending on the insurance demand problem at hand.

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“…Regarding the instruments, we set the gross return on saving to R = 1 and specify selfprotection and self-insurance as p (x) = p 0 e −µx and L (y) = L 0 e −νy , where p 0 ∈ (0, 1) is the baseline probability of loss, L 0 the baseline severity of loss, and µ and ν are positive efficiency parameters. Briys et al (1991) use a negative exponential specification for risky self-insurance with uncertain effectiveness (see also Li and Peter, 2021), and Barro (2015) uses this functional form for self-protection in the context of optimal environmental investment. We set µ = 0.0015355 and ν = 0.0012866 in the base case.…”

Section: Preliminaries and Parametersmentioning

confidence: 99%