Valoración probabilística versus borrosa, opciones reales y el modelo binomial. Aplicación para proyectos de inversión en condiciones de ambigüedad

Abstract

El trabajo tiene por objeto exponer la metodología, las ventajas y las debilidades del modelo binomialborroso de valoración de opciones reales como complemento del modelo binomial probabilístico. Paralograr lo anterior primero se presentan los modelos de opciones reales clasificados en probabilístico yborroso; luego se desarrolla el modelo binomial borroso incorporando: el método Marketed Asset Disclai-mer (MAD), rejillas binomiales borrosas y el índice pesimismo-optimismo, para estimar el valor esperadode las opciones del proyecto (VEOP). Se ilustra con un caso comparando los resultados obtenidos con elmodelo borroso y el probabilístico. Finalmente se concluye que, en situaciones de falta de información(ambigüedad), la lógica borrosa es un complemento del modelo probabilístico para determinar el valorde la flexibilidad estratégica.

Valuation of an investment project in research and development in the pharmaceutical industry

“…Zadeh (1965) introduced the concept of fuzzy set, thus allowing for the elements of a set take different grades of membership, which can be mapped into the interval [0,1], to unlike classical theory of sets, which considers the membership of elements of a set in absolute terms, that is in the set {0,1}. The fuzzy model in discrete time is to adapt the traditional binomial model fuzzy logic; allowing operate and define the ambiguity of the underlying through triangular or trapezoidal fuzzy numbers, particularly in order to estimate movements upward or downward as it points (Muzzioli and Torricelli (2004), Yoshida et al, (2006), Zdnek (2010), Liao and Ho (2010), Wang (2007), Milanesi (2014), andCruz-Aranda et al, (2016).…”

“…The advantage of the fuzzy theory applied in valuation models by means of real options is that it allows the possibility of capturing a value from a lower end and growing linearly to a maximum value and decreasing linearly to the upper end of an interval, this being the base of the triangle and originating a set of possible project values. In this model according to Milanesi (2014) and Liao and Ho (2010), values upward and downward are determined considering the volatility of returns of cash flows and its coefficient of variation cv, i.e. :…”

“…To apply the diffuse binomial model (Milanesi 2014), it requires estimation three grids (or binomial trees): The first fuzzy binomial grid corresponds to the performance of the underlying. To do this, it is considered the ratio of cash flows to present value of the project.…”

Lógica difusa y el riesgo financiero. Una propuesta de clasificación de riesgo financiero al sector cooperativo

“…Dentro de estos tipos podemos clasificarlos en 3: 1) modelo en tiempo continuo fuzzy (MCF): se recurrir para estimar opciones financieras reales, mediante el uso de números trapezoidales; 2) Fuzzy pay-off method (FPOM): trabaja con distribuciones triangulares, cuyo valor surge del fracción representativo del área de valores positivos divido para el área total de posibles valores del triángulo y el valor posible medio del escenario borroso, y 3) modelos en tiempo discreto fuzzy (MDF), adapta el modelo binomial a la lógica borrosa que permite estimar los movimientos ascendentes y descendentes (Milanesi, 2014).…”

Fuzzy logic and financial risk. A proposed classification of financial risk to the cooperative sector

“…There are three classifications for this type of logic: (1) models in fuzzy continuous-time (MFC), used to estimate real financial options through the use of trapezoidal numbers; (2) fuzzy pay-off method (FPOM), works with triangular distributions, the value of which emerges from the representative fraction of the positive value area divided for the total area of possible values of the triangle and the possible average value of the fuzzy landscape; (3) models in fuzzy discrete-time (MFD), which adapt the binomial model to the fuzzy logic allowing to estimate the upward and downward movements (Milanesi, 2014). Rico and Tinto (2008) present the application of the fuzzy sets in five areas of business organizations related to accounting, where we find problems concerning: portfolio selection, financial mathematics, capital budget, technical analysis, credit analysis, and financial analysis.…”