The global trading system is undergoing a period of transition. Shifting economic circumstances, major advances in technology and the emergence of new players all underscore that we are on the edge of major change. Persistent imbalances continue to be a cause of concern in some major economies. In such a climate of uncertainty only one thing is certain- that there is a great need to strengthen the global trading system.
Dynamic Models and Structural Estimation in Corporate Finance has three goals: * To explain the models and techniques used in this literature as simply as possible, with the intent of making the literature more accessible. * To introduce the reader to the main strands of this literature. This monograph can therefore be viewed in part as a literature review and in part as a tutorial. * To explain how dynamic models can be taken to the data and be estimated with the intent to provide a practical, hands-on guide to three specific methodologies that have been used in the literature: generalized method of moments, simulated method of moments, and maximum simulated likelihood. Dynamic Models and Structural Estimation in Corporate Finance provides a concise guide to the extant structural estimation literature in corporate finance. Following an introduction, Section 2 provides an overview of dynamic corporate finance models based on techniques developed in the continuous time contingent claims literature. Section 3 covers a separate strand of the literature that stems discrete time investment models. Section 4 reviews the relatively small number of different econometric techniques that have been used to estimate these models, as well as the studies that have used them. The authors close with a brief overview of directions for future research.
An introduction to the mathematical theory and financial models developed and used on Wall Street
Providing both a theoretical and practical approach to the underlying mathematical theory behind financial models, Measure, Probability, and Mathematical Finance: A Problem-Oriented Approach presents important concepts and results in measure theory, probability theory, stochastic processes, and stochastic calculus. Measure theory is indispensable to the rigorous development of probability theory and is also necessary to properly address martingale measures, the change of numeraire theory, and LIBOR market models. In addition, probability theory is presented to facilitate the development of stochastic processes, including martingales and Brownian motions, while stochastic processes and stochastic calculus are discussed to model asset prices and develop derivative pricing models.
The authors promote a problem-solving approach when applying mathematics in real-world situations, and readers are encouraged to address theorems and problems with mathematical rigor. In addition, Measure, Probability, and Mathematical Finance features:
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