Valutazione di opzioni exchange attraverso la simulazione Monte Carlo e stima delle sensitivita'.

Exchange options give the holder the right to exchange the asset V for the asset D. They present an important role for the evaluation of investment projects which have uncertainty both in the gross value (underlying asset) and in the investment costs (exercise price). In this paper we propose to elaborate some MATLAB algorithms to estimate exchange options with Monte Carlo simulation. Then, through the estimate and analysis of sensitivity, we compare the most important exchange options emphasizing the american sequential exchange option.Exchange Options; Real Options; Monte Carlo Simulation;

A Strategic R&D Investment with Flexible Development Time in Real Option Game Analysis

The real option theory provides a useful tool to evaluate an R&D investment under uncertainty because, unlike the NPV (Net Present Value), it considers the managerial flexibility that may be expand the investment opportunity value. However, most R&D investment projects are open to competing firms in the same industry or line of business, and so the strategic considerations become extremely important. In this paper we analyze a real option game between two firms that invest in R&D. The firm that invests first, defined as the Leader, acquires a first mover advantage that we assume as a higher market share than other one, namely the Follower, that postpones its R&D investment decision. But, several R&D investments present positive externalities and so, the option exercise by the Leader generates an “Information Revelation” that benefits the Follower. Moreover, to value the flexibility time to realize the development phase, we consider the American-Exchange type options.American Exchange options, game theory, Montecarlo simulation, R&D, information revelation

An R&D Investment Game under Uncertainty in Real Option Analysis

One of the problems of using the financial options methodolgy to analyse investment decisions is that strategic considerations become extremely important. So, the theory of real option games combines two successful theories, namely real options and game theory. The value of flexibility can be valued as a real option while the competition can be analyzed with game theory. In our model we develop an interaction between two firms that invest in R&D. The firm that invests first, defined as the Leader, acquires a first mover advantage that we assume as a higher share of market. But the R&D investments present positive externalities and so, the option exercise by the Leader generates an Information Revelation that benefits the Follower.Real Options; Exchange Options; Option games; Information Revelation.

A Monte Carlo approach to value exchange options using a single stochastic factor

Exchange options give the holder the right to exchange one risky asset V for another risky asset D. The asset V is referred to as the optioned (underlying) asset, while D is the delivery asset. So, when an exchange option is valued, we generally are exposed to two sources of uncertainity, namely we have two stochastic variables. Exchange options arise quite naturally in a number of signicant nancial arrangements including bond futures contracts, investment performance, options whose strike price is an average of the experienced underlying asset price during the life ot the option and so on. In this paper we propose some algorithms to estimate exchange options by Monte Carlo simulation reducing the bi-dimensionality of valuation problem to single stochastic factor.Exchange Options; Monte Carlo Simulations.

R&D Cooperation in Real Option Game Analysis.

Cooperative investments in R&D are a significant driving force of the modern economy. As it well-known, the R&D investments are uncertain and the strategic alliances create synergies and additional information that increase the success probabilities about R&D projects. The theory of real option games takes into account both the flexibility value of an investment opportunity and the strategic considerations. In particular way, while the non-cooperative options are exercised in the interest of the option holders' payoffs, the cooperative ones are exercised in order to maximize the total partnership value. In our model we develop an interaction between two firms that invest in R&D and we show the effects of cooperative synergies on several equilibriums. Moreover, we consider that the R&D investments are characterized by positive network externalities that induce more benefits in case of reciprocal R&D success.Real Exchange Options; Cooperation games; Information Revelation; R&D investments.

Valuation of R&D Sequential Exchange Options using Monte Carlo approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlomethods. R&D projects generally involves multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase, it is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options' in which investments present uncertainty both in the gross project value and in costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to value the European and American Real Compound Exchange options through Monte Carlo simulation. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.Pseudo Compound American Exchange option; R&D;Monte Carlo Methods.

Valutazione di progetti di investimento e-commerce attraverso le opzioni reali

The traditional NPV (Net Present Value) method doesn't value opportunely the e-commerce investment projects which are characte\-rized by initial limited cash flows and a high uncertainty. The real options theory instead individuates the strategical opportunities as basic part of the project value. An e-commerce opportunity is more like exchange than simple call option, because there is uncertainty both in the gross project value (underlying asset) and in the investment costs (exercise price). Then we propose to value an american sequential exchange option that takes into account flexibility and sequence properties of e-commerce project investments.Real Options; Exchange Options; E-commerce investments

Valuation of R&D compound option using Markov chain approach

AbstractIncorporation of technical risk in compound real options has been considered in Cassimon et al. (2011) concerning the valuation of multi-stage pharmaceutical R&D. There, the technical success probabilities at each development stage were assumed to be generated independently of each other. This assumption can be unrealistic in many applied problems, pharmaceutical R&D included. We present a valuation procedure dealing with dependent success probabilities and random development stage times. This greater flexibility allows a better description of the sequence of decision stages and results, which in turn, impact the value of the considered project. The theoretical results are illustrated through a numerical example that shows the implementation of the model to a pharmaceutical R&D problem

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights