All decisions, including investing in energy transition projects, should satisfy the preferences of the owners. Yet owners of public firms have potentially contrasting preferences for emission reduction, value, and risk. For managers as agents of the owners, satisfying all tastes would be challenging. In this paper, instead of the commonly suggested multi-attribute-decision-making with such unclear trade-offs, we use simplifying measures from finance theory. Using insights from emission markets and an example from energy transition, in this paper we discuss that the single goal of “maximizing shareholder value” would eventually satisfy all the owners. This means that any preferences other than those of the market are irrelevant to business decisions. Corporations should make value maximizing decisions within the rules of the game (set by the regulatory system) that promote energy transition preferences.

This article presents a generalized method for multi-dimensional optimal stopping, tailored to problems that arise in electricity markets, when addressing decisions under uncertainty with Real Options Analysis. Electricity markets are highly transparent with supply and demand volumes available as high-resolution time series and with a fair transparency on production costs. Both supply and demand show strong periodic behavior in the form of, e.g., standard load profiles or similar, making mean-reverting stochastic processes with periodic time-dependent trend functions a good choice for modeling the dynamics. However, this class of problems does not fit well to established methods for optimal stopping -- e.g., reducing dimensions or making use of properties of the reward function -- and therefore, we propose an alternative, generalized approach. We derive a general form of the Hamilton-Jacobi-Bellman equation instead and propose a numerical solution via the Bellman-Howard operator iteration. We demonstrate the functionality of this approach by setting up an example which represents the retrofit of an electrolyzer to an offshore wind farm. We solve the optimal stopping problem numerically and show that the method supports decision making well on such an irreversible investment under uncertainty.

Most carbon capture and storage projects do not pass the economic feasibility studies. Often this is because their benefits are small due to unattractive value of carbon emission mitigation measures. We believe one reason that such measures are currently unattractive is due to a lack of informed understanding of the future of this commodity. To address this issue, we conducted a study to develop forecasts of carbon allowance prices based on the two-factor stochastic model of Schwartz and Smith (2000). We implemented the analytical framework described in Jafarizadeh (2022a, 2022b) using the Distribution of Sum Discounted Prices technique. The analysis led to informed forecasts of pessimistic, expected, and optimistic carbon prices. We further used these forecasts in economic analysis of a carbon storage project.

Measuring the sustainability of the bioeconomy is crucial to evaluating its continuous contribution to wellbeing. Previous studies that have addressed sustainability measurement vary in their emphasis on sustainability dimensions and countries’ scores. Studies that have addressed the sustainability of the bioeconomy have focused on indicators that measure specific contributions to sustainability. We devise a framework directly linked to the 1987 Brundtland Report’s definition of sustainable development. Our framework uses the concepts of intergenerational wellbeing and genuine investment, whereby sustainability is defined as non-declining intergenerational wellbeing over time. Sustainability-related investment projects include uncertainty and irreversibility, which we model explicitly in contrast to previous works. We calculate two related indicators—hurdle rate and maximum incremental social tolerable irreversible costs (MISTICs)—which have a forward-looking approach, investigating whether future investment projects in the bioeconomy are sustainable. We use these two indicators to empirically analyze the sustainability of European Union (EU) Member States’ (MSs) bioeconomies and sectors. We found that the hurdle rate in the bioeconomy is lower for the bio-based part than for the non-bio-based part for most countries, indicating a high potential for further sustainable investments in the transition toward an EU bioeconomy. The majority of countries have overall negative MISTICs for their bioeconomy, implying that bioeconomy projects need to provide irreversible benefits. However, all the countries have bioeconomy sectors with positive MISTICs. Our findings are consistent with Ecological Footprint’s report indicating ecological deficits for most EU MSs, as they have a greater footprint than biocapacity.

Oil and gas (O&G) companies have changed their profile in pursuing potential reserves in new frontier areas. Entry into business opportunities has been cautious, as the ongoing energy transition requires positioning in reserves with lower carbon footprints and pollutants. Minimal investments are made in the exploratory period and are essential in separating an environmentally and economically efficient portfolio. Thus, an investment program in information to de-risk exploratory prospects is essential to avoid failures and better estimate the commercially recoverable volume before a high-cost investment. Usually, the decision-making does not consider calculating information benefit for exploratory block acquisition. When it is done, the value of the decision does not add the benefit of postponing the high-cost investment of a wildcat well. This paper systematizes the appreciation of the option to invest in the exploratory well combined with the de-risking information investment, with binomial and least-square Monte Carlo approaches, associating the theory of real options (RO) with the value of information (VOI).

Injecting carbon dioxide into mature gas reservoirs could create environmental and commercial benefits. The resulting lower emissions and higher productivity could outweigh the associated costs. But what is the value of such inherent project flexibility? especially when value drivers like costs and production are uncertain. To reflect the value potential of such decisions, the commonly used “high” and “low” price forecasts are unlikely to reflect the value potentials. Instead, we use a stochastic model to describe uncertain price expectations and use an integrated techno-economic framework to address the managerial flexibility of converting some of the production wells to CO2 injection. Using numerical subsurface reservoir model along with a price model, we show the value of creating opportunity from optimally converting wells. The framework leads to insights into the feasibility of CO2 injection in depleted reservoirs. The outcomes show the key drivers of value in integrated project appraisals.

Resource redeployment and resource idling are two important resource allocation strategies that have always been considered separately from each other. This study develops a formal model that demonstrates that resource idling is an important precursor to resource redeployment. Not only does idling directly increase the use of redeployment but it also significantly enhances the effects of the inducement and cost to redeploy, which are two key determinants of redeployment. These theoretical predictions are tested with data on oil wells drilled in Texas over 25 years. The resource that can be idled and redeployed in this context is the rig owned by an oil-drilling contractor. Empirical analyses corroborate the theoretical predictions and demonstrate that the results are economically meaningful. In addition, the study demonstrates the biases that exist when redeployment is considered separately from idling.

This paper researches the academic and market state of the art relative to financial quantification of risks in Process Safety and develops a Real Options methodology for the valuation of these risks. Contingent asset valuation methodologies are used, such as insurance and contractual clauses, using in particular the Real Options approach. This makes it possible to correctly quantify projects and assets under an environment of uncertainty, in the case of events such as accidents, or equipment breakdown, of low statistical occurrence, but with harmful and loss-incurring consequences. And also investments to mitigate these risks, thus reducing the negative financial effect of the occurrence of these events.

Infrastructure concessions, especially for transport projects, are long-term investments subject to demand volatility. Yet disruptive events, such as the Covid-19 pandemic, may negatively affect demand for traffic infrastructure, thus impacting the cash flows of such projects and decreasing the return of the concessionaire. Historical demand series shows that these events are more frequent than they would appear, but that their effects are usually transitory. In the analysis of passenger demand series in major airports in the United States, Europe, and Brazil, it is apparent how the Covid-19 pandemic affected demand for a period of time. Nonetheless, after the second year of the pandemic, demand resumed its historical levels with an observable time lag, but at a fast pace. We propose an approach to hedge the effects of such disruptive events by considering a concession term extension proportional to the observed reduction in the demand and the length of this reduction. We model this mechanism as a European Call Option on the time-term extension of the concession contract. Traffic demand is modeled as a mean reversion to which negative Poison jumps are added to simulate such disruptive events. Results are then compared to the case without real options.

In this study, we investigated the relationships between ex ante regulations and innovations via firm investment decisions. We first present a conceptual framework based on the option value theory. We then exploited this model to provide an interpretative framework for the results of an empirical analysis in which we assessed the authorization procedures of the EU novel food regulation. Our findings were based on a novel detailed dataset of 289 applications submitted under both the former (Regulation 258/97) and current EU novel food regulation (Regulation 2283/2015), in which we gathered information on the number of applications, duration of the authorization procedure, and determinants of approval of novel food applications. We found relatively stable applications across the years, with an upsurge following the enforcement of the current novel food regulation, which is explained by a reduction in option value. We also found a decreasing trend in the ceiling value for the expected duration of the relevant procedures. However, this upper limit appears irrelevant in determining investment decisions. Finally, our results suggest that compared with public entities, private entities and applying for regulatory approval of NF ingredients instead of a product have higher success.

The objective of this article is to propose a Real Options approach to the specific situation that will very probably develop in Brazil in the new few years with a significant green energy surplus and give to Green Hydrogen (GH2) producers an opportunity or real option to get access to GH2 production on a massive scale at a competitive price and steady supply. One possible outcome of such a scenario is the regulator exempting this GH2 production of the so-called Wire Cost (TUST) as it is intended not for consumption but for renewable storage through GYH2 production. We model the gains expected to be obtained by H2 producers through two spark-spreads as switch options.

The northeast region of Brazil is rich in wind and solar resources, making it an ideal location for wind and photovoltaic (PV) generation. However, there is a ubiquitous degree of uncertainty regarding the demand for the generated electricity. Therefore, any excess electrical energy produced by the wind and PV farms has the flexibility to be converted into green hydrogen to address this uncertainty. Green hydrogen is a clean and renewable energy source that offers many flexibilities for efficiently using excess electric energy. This helps to manage the variability of the wind and solar resources and provides a long-term storage solution for the extra power. In addition, the converted green hydrogen can be used as fuel for transportation, heating, and back to electricity generation, or stored for further use, offering significant value maximisation to all stakeholders.

Hydropower plants provide flexibility and storage to support the penetration of renewable energy sources needed to meet climate goals. Investments to upgrade their capacity dependon accurate valuation. Such strategic valuation in principle depends on long-term market price movements, tactical capacity allocation, and capacity bids that respond to short-term price fluctuations. Given the complexity of this holistic problem, hierarchical planning is commonplace, where investment models simplify tactical capacity allocation decisions and ignore the value ofshort-term production flexibility. We formulate a novel investment model that accounts for these aspects. While our problem is complex, we show how a combination of price modeling, informed by empirical analysis, and the use of reinforcement learning to solve for capacity allocation can lead to insightful semi-analytical investment policies. In particular, these policies highlight that capacity investment is supported at lower power prices when the short-term variability of these prices increases, that is, when the value of short-term production flexibility is higher. A numerical study based on real operational and market data shows that valuations from our model can be computed efficiently. Our findings suggest that investment models enabled by reinforcement learning that value the operational flexibility of production assets at long and short time scales can significantly help promote additional capacity in hydropower. The tools we develop are potentially relevant for analogous valuation of investments in other renewable energy production assets.

The effects of climate change are becoming more apparent and with that the need to act. Meeting the announced pledge to limit the effects of climate change requires large financial resources, for which there is still a financing gap. Hence, finding efficient financing instruments is an important element to combat climate change. In this paper, we compare different green bond designs including fixed-rate, carbon-linked, inflation-linked, and convertible (green) bonds. We assume that the proceeds are invested into an emission-reducing project thus generating returns in form of saved CO2 certificates. The carbon price is assumed to follow a geometric Brownian motion simulating a general optimal stopping time problem for the start of the investment project. Our simulation results indicate that most alternative bond designs do not set superior incentives compared to traditional green fixed-rate bonds. The only design that outperforms fixed-rate bonds, are green carbon-linked bonds following a coupon design inversely linked to the development of carbon prices. This is surprising given the latest issuance of green inflation-linked and green convertible bonds. Thus, the findings question whether alternative and more complex green bond designs are the right tool to combat climate change.

This paper develops a real options model which shows the theoretical effect of business uncertainty and of having (or not having) a tax haven (TH) affiliate on the timing of foreign direct investment (FDI). It assumes that a multinational firm has a monopoly over the FDI decision, which is seen as a real option and, if it is optimally exercised, leads to an enhancement of the firm’s pre-tax profits. In line with the real options literature, we conclude that business uncertainty delays the FDI decision irrespective of whether the multinational firm has a TH affiliate or not, but multinational firms with a TH affiliate invest earlier. This runs counter to the existing literature which indicates that the use of tax havens deters investment. We test this theoretical finding using a firm-level dataset that covers the time period 2009 to 2017 and includes information on 22,703 multinational firms. Using a range of empirical models, we find robust evidence to corroborate our theoretical predictions.

A main issue in patent licensing agreements is the contract payment structure and how this distributes the value created between the licensor and licensee. In this article, we analyze the key factors affecting the allocation of value between the licensee and licensor from a real options perspective. In doing so, we explicitly recognize the value of real options embedded in the development process and the sequential structure of licensing contracts. We test our hypotheses on a sample of 175 licensing deals in the U.S. biopharmaceutical industry. Our results show that the value appropriated by the licensee is higher when the agreement is signed in the later stages of technology development, when a lower fraction of the payments is made in upfront fixed fees compared to royalties, and when therapy volatility is lower. Further, the value captured by the licensee is higher in licensing schemes where the licensee pays for development or there is co-development. The study contributes to the contract design literature in R&D alliances and provides insight for future research to incorporate real options to disentangle the complexities of inter-firm strategies in innovation ecosystems. We conclude with implications for the design and management of licensing deals and for policymaking.

The increased prevalence of infectious diseases affecting plants and the magnitude of monetary damages from infection suggest a strong incentive for control measures which reduce transmission likelihood. However, control is costly and generates positive spillover effects, reducing the infection likelihood not only for one area/firm but also for neighbouring sites. If decision-makers consider only internal costs and benefits, this gives rise to a second-mover advantage problem: Control by firm A benefits firm B by reducing B's expected loss from infection. However this delays firm B's implementation of costly control, which in turn reduces A's incentives to control as first-mover. We initially consider how these spillover effects alter investment incentives in the absence of competitive interactions when the decision-maker internalises the spillover benefits of biosecurity measures between sites, i.e. when it is optimal for a single decision-maker to implement biosecurity controls on each of two distinct sites. We investigate the trade-off between sequential and simultaneous control where biosecurity measures have different costs and levels of risk reduction and, as in Kort, Murto and Pawlina (2010), find the flexibility advantage means sequential control dominates unless simultaneous control has a sufficiently high cost advantage. We next consider the case where biosecurity decision-making is on a site-by-site basis (e.g. sites owned by competing enterprises), so positive spillovers are ignored. This delays initial control and reduces value for both firms/sites (relative to the combined decision-making case). In future work we will investigate incentives for negotiated simultaneous control and the impact of transfers between the parties.

Previously, we introduced a methodology based on exercise boundary fitting (EBF) in an effort to develop a practical Monte Carlo simulation-based real options approach. Our theoretical and numerical presentation of the EBF method shows how real world complexity can be overcome through the use of Monte Carlo simulation and that the EBF methodology is very tractable in an industry setting for it is simple enough for managers to understand, yet can account for important real world factors that make the real options model suitable for valuation. However, we recognize that in an effort to account for real world complexities, multiple stochastic factors will need to be modelled. In such cases, the exercise boundaries will be multi-dimensional hyper surfaces. Modelling such surfaces will have its own challenges and will further tax the optimization required with the EBF method. A promising solution to the problem may be the use of reinforcement learning (RL) which we explore in this paper.

We build on previous solutions for mutually exclusive options in a duopoly with switching and divestment alternatives. We examine the implications of increasing the leader's market share and/or changing volatility over progressive regimes. The consequences of market share and volatility changes on the values for both the leader and follower are often surprising, and provide a rich context for evaluating real option games involving market shares, volatilities and eventually other factors.

This paper presents a new model for analyzing the interplay between capital structure and merger decisions, incorporating both operational and financial synergies. The model posits that operational and financial synergies are inversely correlated. Al- though leverage tends to increase following a merger, the proposed model suggests that this outcome may not always be the case. The paper also examines the impact of exogenous and endogenous leverage decisions on merger timing, leverage ratios, and credit spreads. The model suggests that firms with the option to merge may have lower or higher leverage ratios than other firms depending on whether they adjust leverage in anticipation of the merger.

This paper offers an analytical representation for evaluating optimal investment decisions associated to a feed-in tariff (FIT) contract with a minimum price guarantee (i.e., a price-floor regime) under the constant elasticity of variance (CEV) model. The proposed analytic solutions can be used to optimally design FIT contractual schemes with both perpetual and finite maturity guarantees. We show that the argument that a perpetual guarantee only induces investment for prices below the price floor when offering a risk-free investment opportunity is still valid under the CEV process. We also demonstrate that the optimal price-floor level triggering immediate investment in the presence of a perpetual guarantee is independent of the elasticity parameter of the CEV model. By contrast, we show that such independence is not valid any more in the case of FIT contracts with a finite maturity guarantee. Our results provide evidence that care must be taken when a policy-maker aims to design a given instrument to induce investment decisions with FIT contracts because the differences between trigger points under alternative modeling assumptions are quite significant and the excessive rents are usually paid at the expense of tax payers.

This paper considers the problem of a monopolist that can invest in R&D to improve the quality of a smart product (e.g., AVs). A higher quality directly results in a lower frequency of incidents caused by the AVs. The R&D process is uncertain both in terms of duration and outcome. In addition, the firm holds two (nested) options: the option to launch the product on the market and the option to update the product after launch. The firm chooses not only the timing to exercise its options, it also chooses its R&D intensity and production capacity size. We analyze the problem of the firm as well as the impact of measures by a regulator. In particular, we are interested in the impact of the option to update, and safety regulation, on the firm's R&D and investment strategy as well as the impact on the accident rate.

We propose an alternative to the standard nonstationary earnings dynamics framework for studying refinancing decisions by building a dynamic two-stage trade-off model with refinancing for firms following mean reverting earnings. The model predicts a negative relation between profitability and leverage ratios at refinancing, showing that firms with earnings below their long-term profitability increase their leverage ratios at refinancing, whereas firms currently above their long-term profitability decrease their leverage ratios at refinancing. Our empirical results confirm the prevalence of mean reversion in earnings among US firms and largely corroborate with theoretical model predictions supporting a negative relation of profitability with leverage ratios at refinancing. We also find that on average, firms’ leverage ratios increase during refinancing, driven mostly by firms on the lowest quantile with low leverage ratios.

We build on previous solutions for mutually exclusive options in a duopoly with switching and divestment alternatives. We study the likely implications for increasing the leader’s market share MS at successive levels of market revenue. The conventional net present value NPV thresholds for switching and divestments, ignoring rival and strategic options, are likely to be a misleading basis for making MS decisions. The consequences of MS changes on the values of both the leader and follower are often surprising. The NPV of operations for the leader is reduced by increased MS when revenue is low, with a further negative change of value in the net switch and divest option values. The NPV of operations is increased for the leader by increased MS when revenue is higher, reduced by the decrease in value of the leader’s rival option value. Those strategy results are consistent with the sign and dimension of MS partial derivatives, with some novel analytical solutions.

This paper analyzes the effect of revenue and investment subsidies on strategic in- vestment and optimal investment timing within an asymmetric duopoly, whereby two heterogeneous firms have different maximum capacities, marginal costs, and invest- ment costs. Within this market, a firm optimally decides whether to be a leader or a follower and the optimal quantity to produce. In addition, firms can be active or idle after investment. An interesting finding from our analysis is that the subsidies accelerate investment when a market has an incumbent firm and the other firm has the option to invest. When both firms have the option to invest, we observe differ- ent equilibria and investment triggers’ slopes as the revenue and investment subsidies change.

We study a market where consumers search both extensively and intensively for match information from a finite number of sellers. Consumers choose to acquire more information in earlier visits and become less demanding in later visits if earlier searches are not fruitful. We establish existence of a unique pricing equilibrium in an oligopoly with active search and show the equilibrium price decreases with the number of sellers as in extensive search models.

We develop a dynamic model of the timing of share repurchases within a duopolistic industry to analyze the dynamics of share repurchases in the context of the `peer effect' documented in the extant empirical literature. Share repurchase decisions are taken as part of a broader liquidity management policy but also take into account i) the firm's financial resources needed to invest in a potential growth opportunity, and ii) the feedback effect of the competitor's investment threat on the firm's willingness to hold cash to respond to such a threat. We derive the equilibrium timing of such strategic repurchases of both firms, demonstrate that repurchase triggers depending on parameter values can be either strategic complements or strategic substitutes, and generate a number of empirical predictions regarding the expected strength of the peer effect, defined as the distance between the leader and follower repurchase thresholds. Subsequently, based on the universe of Compustat firms, we find empirical support for the model predictions: the peer effect is weakened by the degree of product market competition, financial constraints, and stock illiquidity. Finally, we also present evidence of several cross-effects that are consistent with model predictions.

This paper provides a theoretical model for a better understanding of the impact of dual ownership (DO) on different types of corporate finance decisions, focusing on the dynamics of debt restructuring, default, capital structure, and irreversible investment decisions. The study links the recent trend of literature on DO with that on dynamic capital structure and debt renegotiation. In our model bank debt and market debt coexist with different types of seniority. The main findings show that moderate levels of DO may lead to underinvestment and lower leverage, while the optimal bank (market) debt decreases (increases) as the weight of DO increases in the firm. Additionally, the bank (market) credit spreads decrease (increase) as DO increases.

This study considers how an upper reflecting barrier affects the interaction between financing and investment decisions. Here, a magnitude of upper reflecting barrier can be regarded as the degree of intense market competition. We show that fierce competition (a decrease in upper reflecting barrier) reduces the amount of debt issuance and delays investment, which decreases the credit spreads and leverage.

This paper analyzes a firm's capacity expansion and reduction problem under uncertainty. The firm expands (resp., contracts) the capacity if the output price is sufficiently large (resp., low). Consequently, the firm has two types of operational flexibility. The firm faces uncertainty about the output price and can not uniquely identify its distribution. The firm treats the price dynamics as an approximation of its actual dynamics. Then, the firm decides its managerial strategy under model uncertainty. To deal with the model uncertainty, we employ the robust control approach. We reveal the effect of both sides' operational flexibility on the firm's decision-making under model uncertainty.

This paper considers the impact of \emph{Knightian uncertainty} or \emph{ambiguity} about the reliability of a patient's acquired comorbidities and risk factors as predictors of treatment outcome on the optimal time to initiate treatment. I show that high levels of such ambiguity is detrimental to patient welfare. Hence, learning about the clinical state (comprised of the comorbidities and risk factors) as a predictor of treatment outcome in order to resolve, at least partially, this ambiguity is crucial to improving their welfare. The learning is achieved via a sequential hypothesis test in which the clinician will only treat if her ambiguity about outcome is sufficiently low; i.e., below some threshold which is derived based on the cost of making a wrong decision by treating. I show that learning in this way does indeed improve patient welfare with respect to the optimal treatment (timing) strategy. The paper concludes with a discussion on the practical considerations for clinicians, how they can use these results in managing patient care, and notes that the results support specialisation across hospitals so that certain treatments are only carried out a small number of specialist centres.

We consider a real-options problem in which the underlying project value follows a geometric or exponential Levy process, capturing rare events besides continuous fluctuations. Such rare events lead to ambiguity because of inconclusive empirical data or market incompleteness. We use ambiguity theory leveraging the notion of variational preferences and $g$-expectations to pin down for the general case a pricing kernel under which to value real options and derive the firm's optimal real-option exercise strategy under this pricing kernel. We also provide sufficient conditions for the optimality of a threshold policy in the general case. For the specialized case with multi-priors preferences, we} obtain explicit expressions for the optimal investment threshold, expected investment time, and value function {\color{blue} and prove comparative statics to assess analytically the effect of small jumps on these. Closed-form expressions are not readily available for multipliers preferences, but we provide approximate solutions for the cases with negligible and deep ambiguity.} Rare events, which are priced under ambiguity aversion, generally lead to a higher investment threshold, delayed investment and higher option value.