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The purpose of this paper is to study the importance of business time, and market opening/closing times and days, for American option pricing.

A Bermudan pricing approach is employed whereby the option can be exercised only during the times and days the market is open. The authors apply the approach to the S&P 100 options market.

It was found that the potential biases that can arise from ignoring the non‐continuous operation of the market are not negligible.

For expositional purposes, the authors assume that the price of the underlying follows a Geometric Brownian motion. This assumption could be relaxed by future research and more complex price dynamics models could be considered.

The findings in this paper could be used in correcting observed option prices, prior to investigating the rationality of early exercise decisions, or in measuring the size of early exercise premia.

This is the first study to examine the effects of business time, and market opening/closing times and days, to American option prices.

This paper aims to analyze the valuation of stock options from the perspective of an employee exhibiting preferences as described by cumulative prospect theory (CPT). In addition, it elaborates on their incentives effect and some implications in terms of design aspects.

The paper draws on the CPT framework to derive a continuous time model of the stock option subjective value using the certainty equivalence principle. Numerical simulations are used in order to analyze the subjective value sensitivity with respect to preferences‐related parameters and to investigate the incentives effect.

Consistent with a growing body of empirical and experimental studies, the model predicts that the employee may overestimate the value of his options in‐excess of their risk‐neutral value. Moreover, for typical setting of preferences parameters around the experimental estimates, and assuming the company is allowed to adjust existing compensation when making new stock option grants, the model predicts that incentives are maximized for strike prices set around the stock price at inception. This finding is consistent with companies’ actual compensation practices. Finally, the model predicts that an executive who is subject to probability weighting may be more prompted than a risk‐neutral executive to act in order to increase the firm's assets volatility.

This research proposes an alternative theoretical framework for the analysis of pay‐to‐performance sensitivity of equity‐based compensation that takes into account a number of prominent patterns of employee behavior that expected utility theory cannot explain. It contributes to recent empirical and theoretical researches that have advanced CPT framework as a promising candidate for the analysis of equity‐based compensation contracts.

The paper aims to propose a consistent and robust pricing/hedging methodology for callable fixed income structures with embedded caplet‐linked options.

A range of recently published (1997‐2003) works about the Libor Market Model (LMM) tackle the problems of modelling the forward curve with more than two factors and calibrating it to caps either/or to swaps. Other articles involve the pricing of Bermudan options using Monte Carlo simulation. In the form of case study, the very popular structure of multicallable range accrual bonds is used. A complete calibration methodology is described in detail, which links the structure's price to the market caps and swaptions prices as well as to the historical correlations between forward rates. We present the direct implementation of the Monte Carlo technique for this particular problem. Furthermore, we explore the application of the Longstaff–Schwartz least squares algorithm and its variations for the estimation of the expected value of continuation.

This paper suceeds in producing a consistent and robust pricing/hedging methodology for callable fixed income structures with embedded caplet‐linked options.

The increased complexity of similar fixed income structures makes traditional approaches like Black–Derman–Toy or Hull‐White trees inadequate for the task of consistent pricing and hedging. Therefore, care must be taken to ensure consisted hedging across the different volatility markets.

This article explores variations and settings of the popular LMM and the Longstaff‐Scwartz algorithm that can be relatively consistent with both the cap and swaption volatility market. The framework is built using as a benchmark the most liquid fixed income structure so that it can be tested for robustness.

Steel price uncertainty exposes pipeline projects that are inherently capital intensive to the risk of cost overruns. The current study proposes a hedging methodology for tackling steel pipeline price risk by deploying Asian option contracts that address the shortcomings of current risk mitigation strategies.

A stepwise methodology is introduced, which uses a closed-form formula as an Asian option valuation method for calculating this total expenditure. The scenario analysis of three price trends examines whether or not the approach is beneficial to users. The sensitivity analysis then has been conducted using the financial option Greeks to assess the effects of changes in volatility in the total price of the option contracts. The total price of the Asian options was then compared with those of the European and American options.

The results demonstrate that the Asian option expenditure was about 1.87% of the total cost of the case study project. The scenario analysis revealed that, except for when the price followed a continuous downward pattern, the use of this type of financial instrument is a practical approach for steel pipeline price risk management.

This approach is founded on a well-established financial options theory and elucidates how pipeline project participants can deploy Asian option contracts to safeguard against steel price fluctuations in practice.

Although the literature exists about the theory and application of financial derivative instruments for risk management in other sectors, their application to the construction industry is infrequent. In the proposed methodology, all participants involved in fixed price pipeline projects readily surmount the risk of exposure to material price fluctuations.

The purpose of this paper is to investigate the impact of bias error resulted from using Monte Carlo simulation in evaluating the American-style option value.

The authors develop an analytical approximation formula to quantify the bias error under the assumption of conditionally independent and identically distributed samples of asset prices. The bias arises from the nested optimization and expectation calculation. The formula is then used to numerically quantify the bias and as an objective function for bias minimization for a given budget of samples.

Monte Carlo methods used in valuation of American-style options can results in bias error ranging from 2 to 10 per cent of the option value. The bias error can be reduced up to 50 per cent either by performing a better scheme for sampling or by efficiently allocating sample size.

The running time of the proposed procedure can be improved by using a specialized algorithm to solve the sample size allocation problem instead of using a commercially available subroutine MINOS. Other sampling procedures for bias reduction may be extended and applied to this multi-stage problem.

The methodology can help to more accurately approximate the option value.

The paper provides a method to develop an analytical approximation for bias error and provide a numerical experiment to test the methodology.

Railroad transit infrastructures are amongst major capital-intensive projects worldwide, which impose significant risks to the contractors of build-operate-transfer projects because of the fluctuations in steel price fluctuation. The purpose of this paper is to introduce a methodology for hedging steel price risk using financial derivatives.

Cox–Ross valuation lattice has been used as an option valuation model for determining option’s price for the construction companies involved in fixed-price railroad projects. A sensitivity analysis has been conducted using the financial option Greeks to evaluate the impacts of option’s pricing factors in the total price of option.

The result of valuation shows that European options cost to safeguard against the effects of price risk is only a fraction in contrast to the total cost of steel procurement for a typical railroad construction company. This confirms that using this kind of financial derivative is a beneficial yet effective approach for hedging steel price risk for railroad construction companies.

The applicability of the financial derivatives, both exchange-traded and over-the-counter instruments, is evident in broad financial industry. This paper shows how European options can be readily used for risk management of a typical railroad project, and explains the methodology in a step-by-step procedure.

Although the financial engineering literature is rife of theory and application of derivatives in various contexts, to the best knowledge of authors there is only few papers on the application of these well-developed financial instruments for risk management in construction industry. This study intends to illustrate how financial derivatives can add value to risky construction projects and shed new light in this important application area.

Post-disaster housing reconstruction (PDHR) demands a considerable percentage of global property investment, yet the post-disaster environment presents intricate challenges to reconstruction financing for governments and at the same time, revenue uncertainty for private investors. The purpose of this study is to develop a methodology for tackling land shortage and the financial challenges of PDHR in the aftermath of a disaster.

This study developed a methodology based on a combined minimum revenue guarantee and maximum revenue cap model using a well-established real options analysis (ROA) for revenue risk sharing in PDHR projects and land readjustment (LR) for finance. The applicability of the purported model is demonstrated through an illustrative example.

The results show that flexibility in the options could increase the PDHR contractor’s risk profile by increasing the expected value of the contractor investment and reducing the probability of investment loss. On the other side, a cap on the contractor revenue stream would allow the government to benefit from any excess in revenue and would counterbalance the value of the option.

The framework proposed in this study could serve as a practical risk-revenue sharing in PDHR projects. Governments and policymakers could use the findings to enable the successful delivery of PDHR projects and consequently bring the quality of life of affected people to pre-disaster conditions.

This study can be considered as a first attempt toward the use of the Australian barrier style options structure, and the trinomial lattice valuation model in PDHR projects, which incorporates LR, public-private partnerships, governmental guarantees and PDHR concepts in one ROA-based framework.

The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables.

This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft.

Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers.

Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks.

Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way.

Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks.

The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.

We propose a new model for the dynamics of the aggregate credit portfolio loss. The model is Markovian in two dimensions with the state variables being the total accumulated loss Lt and the stochastic default intensity λt. The dynamics of the default intensity are governed by the equation dλt=κ(ρ(Lt,t)−λt)dt+σλtdWt. The function ρ depends both on time t and accumulated loss Lt, providing sufficient freedom to calibrate the model to a generic distribution of loss. We develop a computationally efficient method for model calibration to the market of synthetic single tranche collateralized debt obligations (CDOs). The method is based on the Markovian projection technique which reduces the full model to a one-step Markov chain having the same marginal distributions of loss. We show that once the intensity function of the effective Markov chain consistent with the loss distribution implied by the tranches is found, the function ρ can be recovered with a very moderate computational effort. Because our model is Markovian and has low dimensionality, it offers a convenient framework for the pricing of dynamic credit instruments, such as options on indices and tranches, by backward induction. We calibrate the model to a set of recent market quotes on CDX index tranches and apply it to the pricing of tranche options.