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Options Framework (option + framework)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	62%

Kinds of Options Framework

real option framework

Selected Abstracts

A Learning Real Options Framework with Application to Process Design and Capacity Planning

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 1 2005
Luke T. Miller
This paper studies the impact of learning on a multi-staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision-making, and information's impact on downstream decision-making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi-phased irreversible investment decision involving process design and capacity planning. [source]

Correlation at First Sight

ECONOMIC NOTES, Issue 2 2005
Andrew Friend
The synthetic collateralized debt obligation (CDO) market has, over the last year, seen a significant increase in liquidity and transparency. The availability of published prices such as TracX and iBoxx tranches permits the calibration of model parameters, which was not achievable a year ago. This paper details what we believe has become the market standard approach in CDO valuation. The valuation model is introduced and analysed in depth to develop a better practical understanding of its use and the implications of parameter selection and calibration. In particular, we examine the idea that correlation within a copula model can be seen to be an equivalent measure to volatility in a standard B&S option framework and, correspondingly, we seek to calibrate smile and skew. [source]

A reexamination of corporate risks under shadow costs of incomplete information

INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, Issue 1 2001
Mondher Bellalah
G3; G31; G32; G33 Abstract The valuation of the firm and its assets has been done for a long time in the classic context of complete information. Several empirical tests of the main valuation methods reveal a divergence between theoretical prices and observed prices. These deviations might be explained by the standard assumptions of complete information. It is possible to introduce information uncertainty as done by Merton and by Bellalah in the reexamination of corporate risks in the presence of information costs. The concept of risk is useful in modelling the value of the firm and its business risk and in the definition of the required rates of return and the cost of capital of corporations. However, the main well-known results ignore information uncertainty as defined by Merton. Using the main results from the study of Modigliani and Miller and the implications of Merton's model, we give expressions for the cost of capital and the value of the firm's equity and debt in the presence of information costs. We reexamine the relationships between interrelated risks in the same context. We introduce information costs in the computation of the cost of capital and in the pricing of equity in an option framework. When there are no information costs, the main relationships reduce to the classic results in the literature. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Time and space: Reframing the training and development agenda

HUMAN RESOURCE MANAGEMENT, Issue 1 2003
Lloyd Baird
In this article, we review how time and space relationships are reframing the training and development agenda. As the speed of business moves faster and boundaries become less relevant, we need ways of expanding and choosing how and when learning and development can take place. We need ways to target development on specific business issues and deliver improved performance in less time with reduced cost. We define a learning options framework to help organize, integrate, synthesize, and better fit training and development methodologies to business realities. Specific knowledge, tools, and examples are provided to show how organizations are identifying and choosing among development options. © 2003 Wiley Periodicals, Inc. [source]

Real Options, (Dis)Investment Decision-Making and Accounting Measures of Performance

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 3-4 2000
Andrew W. Stark
This paper suggests that a residual income-type measure of performance can be designed which supports optimal investment and disinvestment decision-making in a real options framework involving the options to wait before investing and to abandon. The measure has a number of advantages and disadvantages. Nonetheless, the balance of advantage versus disadvantage for the proposed measure must be set against the inadequacies of other competing measures of performance and associated organisational designs. Even if the measure of performance suggested is not regarded as practically useful, it has another general advantage , it can be used as a benchmark against which to evaluate other performance measures with regard to their support of optimal investment and disinvestment decision-making in a real options framework. [source]

Market risk and process uncertainty in production operations

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 7 2006
Bardia Kamrad
Abstract By adopting a real options framework we develop a production control model that jointly incorporates process and market uncertainties. In this model, process uncertainty is defined by random fluctuations in the outputs' yield and market risk through demand uncertainty for the output. In our approach, production outputs represent commodities or items for which financial contracts do not trade. Outputs are also functionally linked to the level of input inventories. To extend the model's applicability to a wide range of production industries, inputs are modeled to reflect either renewable or partially renewable or non-renewable resources. Given this setting, techniques of stochastic control theory are employed to obtain value maximizing production policies in a constrained capacity environment. The rate of production is modeled as an adapted positive real-valued process and analogously evaluated as a sequence of complex real options. Since optimal adjustments to the rate of production also functionally depend on the outputs' yield, we optimally establish "trigger boundaries" justifying controlled variations to the rate of production over time. In this context, we provide closed form analytic results and demonstrate their robustness with respect to the stochastic (including mean reverting) processes considered. Using these results, we also demonstrate that the value (net of holding costs) accrued to the producer from having an inventory of the output is equivalent to the producer's reservation price to operationally curb its process yield. These generalizations extend the scope of model applicability and provide a basis for applying the real options methodology in the operations arena. The model is explored numerically using a stylized example that allows for both output and demand uncertainty and achieves greater realism by incorporating an element of smoothing into the sequence of production decisions. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source]

A Learning Real Options Framework with Application to Process Design and Capacity Planning

Corporate Investment and Asset Price Dynamics: Implications for SEO Event Studies and Long-Run Performance

THE JOURNAL OF FINANCE, Issue 3 2006
MURRAY CARLSON
ABSTRACT We present a rational theory of SEOs that explains a pre-issuance price run-up, a negative announcement effect, and long-run post-issuance underperformance. When SEOs finance investment in a real options framework, expected returns decrease endogenously because growth options are converted into assets in place. Regardless of their risk, the new assets are less risky than the options they replace. Although both size and book-to-market effects are present, standard matching procedures fail to fully capture the dynamics of risk and expected return. We calibrate the model and show that it closely matches the primary features of SEO return dynamics. [source]