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Current US tax laws provide investors an incentive to time the sales of their bonds to minimize tax liability. This gives rise to a tax-timing option that affects bond value. In reality, corporate bond investors’ tax-timing strategy is complicated by risk of default. Existing term structure models have ignored the effect of the tax-timing option, and how much corporate bond value is due to the tax-timing option is unknown. In this chapter, we assess the effects of taxes and stochastic interest rates on the timing option value and equilibrium price of corporate bonds by considering discount and premium amortization, multiple trading dates, transaction costs, and changes in the level and volatility of interest rates. We find that the value of the tax-timing option accounts for a substantial proportion of corporate bond price even when interest rate volatility is low. Ignoring the timing option value results in overestimation of credit spread, and underestimation of default probability and the marginal investor’s income tax rate. These estimation biases generally increase with bond maturity and credit risk.

This study aims to generalize the following result of McDonald and Siegel (1986) on optimal investment: it is optimal for an investor to invest when project cash flows exceed a certain threshold. This study presents other results that refine or extend this one by integrating timing flexibility and changes in cash flows with time-varying transition probabilities for regime switching. This study emphasizes that optimal thresholds are either overvalued or undervalued in the absence of time-varying transition probabilities. Accordingly, the stochastic nature of transition probabilities has important implications to the search for optimal timing of investment.

This paper recasts the land development problems of Williams (1991) and Quigg (1993) by explicitly dealing with the effects of scale elasticity of unit rental and unit construction cost in a real estate project. Two different diseconomies of scale constraints are imposed on the rental and cost variables. We assume a concave function for the rental variable with respect to the scale of development. Whereas, on the cost side, the diseconomies of scale effect of the variable component of the construction cost is incorporated via a elasticity of scale factor that is larger than unity. The comparative statics simulated positive relationships between the premium that keeps the option of waiting to develop alive and the volatilities of the unit rental and unit construction cost. It was also found that the cost elasticity of scale and the financing cost are factors that increase the premium of the waiting option, whereas, the rental yield factor reduces the incentive of waiting. A high rental yield tends to expedite a development project because the opportunity cost of not developing the land is high. In the case analysis involving a vacant land of 8,000 square meters at Spitafield, East London, a unit rental of £267.2 per square meter (psm) is obtained, which would breakeven a cash flows of the project when the traditional “invest now or never” assumption is made. Compared with the optimal unit rental of £677.0 psm estimated by the real option model, the traditional DCF results tend to accept the feasibility of the real estate project too early and at too low a cut‐off rental.

We consider the partial hedging of stochastic electricity load pattern with static forward strategies. We assume that the company under consideration maximizes the risk adjusted expected value of its electricity cash flows. First, we calculate an optimal hedge ratio and after that we use this hedge ratio to solve the optimal hedging time. Our results indicate, for instance that agents with high load volatility hedge later than agents that have low load volatility. Moreover, negative correlation between forwards and electricity load pattern postpones the hedging timing.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The objective of this paper is twofold: first, to model the value of the firm in the presence of contingent capital and multiple growth options over its life cycle in a stochastic universe to ensure financial stability and recover losses in case of default and second, to clarify how contingent convertible (CoCo) bonds as financial instruments impact the leverage-ratio policies, inefficiencies generated by debt overhang and asset substitution for a firm that has multiple growth options. Additionally, what is its impact on investment timing, capital structure and asset volatility?

The current paper elaborates the modeling of a dynamic problem with respect to the interaction between funding and investment policies during multiple sequential investment cycles simultaneously with dynamic funding. The authors model the value of the firm in the presence of contingent capital that provides flexibility in dealing with default risks as well as growth options in a stochastic universe. The authors examine the firm's closed-form solutions at each stage of its decision-making process before and after the exercise of the growth options (with and without conversion of CoCo) through applying the backward indication method and the risk-neutral pricing theory.

The numerical results show that inefficiencies related to debt overhang and asset substitution can go down with a higher conversion ratio and a larger number of growth options. Additionally, the authors’ analysis reveals that the firm systematically opts for conservative leverage to minimize the effect of debt overhang on decisions so as to exercise growth options in the future. However, the capital structure of the firm has a substantial effect on the leverage ratio and the asset substitution. In fact, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process. Contrarily to traditional corporate finance theory, the study displays that the value of the firm before the investment expansion decreases and then increases with asset volatility, instead of decreasing overall with asset volatility.

The study’s findings reveal that funding, default and conversion decisions have crucial implications on growth option exercise decisions and leverage ratio policy. The model also shows that the firm consistently chooses conservative leverage to reduce the effect of debt overhang on decisions to exercise growth options in the future. The risk-shifting incentive and the debt overhang inefficiency basically decrease with a higher conversion ratio and multiple growth options. However, the effect of the leverage ratio and the risk-shifting incentive will be greater when the capital structure changes during the firm's decision-making process.

The firm's composition between assets in place and growth options evolves endogenously with its investment opportunity and growth option financing, as well as its default decision. In contrast to the standard capital structure models of Leland (1994), the model reveals that both exogenous conversion decisions and endogenous default decisions have significant implications for firms' growth option exercise decisions and debt policies. The model induces some predictions about the dynamics of the firm's choice of leverage as well as the link between the dynamics of leverage and the firm's life cycle.

This work seeks to present the theoretical study considerations and the characteristics of a general design methodology in optimal time for electronic systems using numerical methods and optimal control theory. Through this, the design problem of a system is formulated in terms of optimal control in minimal time.

This general design methodology includes the traditional design strategy (TDS), and the modified traditional design strategy (MTDS), where the model of the system is part of the optimization procedure but an objective function of the optimization process is constructed such as includes the traditional objective function and some penalty functions that feign the model of the system. Many special control functions are introduced artificially to generalize the methodology and produce several design trajectories for the same optimization process – the first and final trajectories correspond to TDS and MTDS, respectively. The combination of these trajectories produce an infinite number of design strategies, some of these are quasi‐optimal in time and only one is optimal in time.

Qualitative and numeric results of this iterative process are generated in a personal computer in a C++ language elaborated with a visual C++ graphic user interface. An algorithm is constructed to form an optimal in time design strategy switching from a MTDS subset to a TDS subset. Results of measured times are analyzed, showing that there is a control input U, such that the objective function is minimized in a minimum time.

These ideas are proposed using method of gradient optimization and special acceleration effect.

The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle feedlot operation, over single commodity time-varying and naive hedge ratios.

A parsimonious regime-switching dynamic correlations (RSDC) model is estimated in two-stages, where the dynamic correlations among prices of numerous commodities vary proportionally between two different regimes/levels. This property simplifies estimation methods for a large number of parameters involved.

There is significant evidence that resulting simultaneous correlations among the prices (spot and futures) for each commodity attain different levels along the time-series. Second, for in and out-of-sample data there is a substantial reduction in the operation's margin variance provided from both multiproduct and single time-varying optimal hedge ratios over naive hedge ratios. Lastly, risk mitigation is attained at a lower cost given that average optimal multiproduct and single time-varying hedge ratios obtained for corn, feeder cattle and live cattle are significantly below the naive full hedge ratio.

The application studied is limited in that once a hedge position has been set at a particular period, it is not possible to modify or update at a subsequent period.

Agricultural producers, specifically cattle feeders, may profit from a tool using improved techniques to determine hedge ratios by considering a larger amount of up-to-date information. Moreover, these agents may apply hedge ratios significantly lower than one and thus mitigate risk at lower costs.

Feedlot operators will benefit from the potential implementation of this parsimonious RSDC model for their hedging operations, as it provides average optimal hedge ratios significantly lower than one and sizeable advantages in margin risk mitigation.

The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system.

The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly.

An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic.

In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.