Grado de predicción del valor de las acciones ante nuevas emisiones en el sector de extracción de petróleo crudo y gas natural en Colombia, mediante modelos de caos y análisis recurrente

El presente trabajo se enfoca en el análisis de las acciones de Ecopetrol, empresa representativa del mercado de Extracción de Petróleo y Gas natural en Colombia (SP&G), durante el periodo, del 22 de mayo de 2012 al 30 de agosto de 2013. Durante este espacio de tiempo la acción sufrió una serie de variaciones en su precio las cuales se relacionaban a la nueva emisión de acciones que realizo la Compañía. Debido a este cambio en el comportamiento del activo se generaron una serie de interrogantes sobre, (i) la reacción del mercado ante diferentes sucesos ocurridos dentro de las firmas y en su entorno (ii) la capacidad de los modelos financieros de predecir y entender las posibles reacciones observadas de los activos (entendidos como deuda). Durante el desarrollo del presente trabajo se estudiará la pertinencia del mismo, en línea con los objetivos y desarrollos de la Escuela de Administración de la Universidad del Rosario. Puntualmente en temas de Perdurabilidad direccionados a la línea de Gerencia. Donde el entendimiento de la deuda como parte del funcionamiento actual y como variable determinante para el comportamiento futuro de las organizaciones tiene especial importancia. Una vez se clarifica la relación entre el presente trabajo y la Universidad, se desarrollan diferentes conceptos y teorías financieras que han permitido conocer y estudiar de manera más específica el mercado, con el objetivo de reducir los riesgos de las inversiones realizadas. Éste análisis se desarrolla en dos partes: (i) modelos de tiempo discreto y (ii) modelos de tiempo continúo. Una vez se tiene mayor claridad sobre los modelos estudiados hasta el momento se realiza el respectivo análisis de los datos mediante modelos de caos y análisis recurrente los cuales nos permiten entender que las acciones se comportan de manera caótica pero que establecen ciertas relaciones entre los precios actuales y los históricos, desarrollando comportamientos definidos entre los precios, las cantidades, el entorno macroeconómico y la organización. De otra parte, se realiza una descripción del mercado de petróleo en Colombia y se estudia a Ecopetrol como empresa y eje principal del mercado descrito en el país. La compañía Ecopetrol es representativa debido a que es uno de los mayores aportantes fiscales del país, pues sus ingresos se desprenden de bienes que se encuentran en el subsuelo por lo que la renta petrolera incluye impuestos a la producción transformación y consumo (Ecopetrol, 2003). Por último, se presentan los resultados del trabajo, así como el análisis que da lugar para presentar ciertas recomendaciones a partir de lo observado.

Relationship between åström control and the kalman linear regulator — Caines revisited

The relationship between minimum variance and minimum expected quadratic loss feedback controllers for linear univariate discrete-time stochastic systems is reviewed by taking the approach used by Caines. It is shown how the two methods can be regarded as providing identical control actions as long as a noise-free measurement state-space model is employed.

Optimal observers for ARMA models

A polynomial-based ARMA model, when posed in a state-space framework can be regarded in many different ways. In this paper two particular state-space forms of the ARMA model are considered, and although both are canonical in structure they differ in respect of the mode in which disturbances are fed into the state and output equations. For both forms a solution is found to the optimal discrete-time observer problem and algebraic connections between the two optimal observers are shown. The purpose of the paper is to highlight the fact that the optimal observer obtained from the first state-space form, commonly known as the innovations form, is not that employed in an optimal controller, in the minimum-output variance sense, whereas the optimal observer obtained from the second form is. Hence the second form is a much more appropriate state-space description to use for controller design, particularly when employed in self-tuning control schemes.

Adaptive deadbeat control of stochastic systems

This paper considers the use of a discrete-time deadbeat control action on systems affected by noise. Variations on the standard controller form are discussed and comparisons are made with controllers in which noise rejection is a higher priority objective. Both load and random disturbances are considered in the system description, although the aim of the deadbeat design remains as a tailoring of reference input variations. Finally, the use of such a deadbeat action within a self-tuning control framework is shown to satisfy, under certain conditions, the self-tuning property, generally though only when an extended form of least-squares estimation is incorporated.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

Weighted input predictive controller

A discrete-time algorithm is presented which is based on a predictive control scheme in the form of dynamic matrix control. A set of control inputs are calculated and made available at each time instant, the actual input applied being a weighted summation of the inputs within the set. The algorithm is directly applicable in a self-tuning format and is therefore suitable for slowly time-varying systems in a noisy environment.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.

A new approach for the solution of the optimal set-point tracking problem for nonlinear systems

In this paper, a discrete time dynamic integrated system optimisation and parameter estimation algorithm is applied to the solution of the nonlinear tracking optimal control problem. A version of the algorithm with a linear-quadratic model-based problem is developed and implemented in software. The algorithm implemented is tested with simulation examples.

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics.

Regularization of Descriptor Systems by Derivative and Proportional State Feedback

For linear multivariable time-invariant continuous or discrete-time singular systems it is customary to use a proportional feedback control in order to achieve a desired closed loop behaviour. Derivative feedback is rarely considered. This paper examines how derivative feedback in descriptor systems can be used to alter the structure of the system pencil under various controllability conditions. It is shown that derivative and proportional feedback controls can be constructed such that the closed loop system has a given form and is also regular and has index at most 1. This property ensures the solvability of the resulting system of dynamic-algebraic equations. The construction procedures used to establish the theory are based only on orthogonal matrix decompositions and can therefore be implemented in a numerically stable way. The problem of pole placement with derivative feedback alone and in combination with proportional state feedback is also investigated. A computational algorithm for improving the “conditioning” of the regularized closed loop system is derived.

Microcontroller-based peak current mode control using digital slope compensation

Microcontroller-based peak current mode control of a buck converter is investigated. The new solution uses a discrete time controller with digital slope compensation. This is implemented using only a single-chip microcontroller to achieve desirable cycle-by-cycle peak current limiting. The digital controller is implemented as a two-pole, two-zero linear difference equation designed using a continuous time model of the buck converter and a discrete time transform. Subharmonic oscillations are removed with digital slope compensation using a discrete staircase ramp. A 16 W hardware implementation directly compares analog and digital control. Frequency response measurements are taken and it is shown that the crossover frequency and expected phase margin of the digital control system match that of its analog counterpart.

A dynamical systems framework for intermittent data assimilation

We consider the problem of discrete time filtering (intermittent data assimilation) for differential equation models and discuss methods for its numerical approximation. The focus is on methods based on ensemble/particle techniques and on the ensemble Kalman filter technique in particular. We summarize as well as extend recent work on continuous ensemble Kalman filter formulations, which provide a concise dynamical systems formulation of the combined dynamics-assimilation problem. Possible extensions to fully nonlinear ensemble/particle based filters are also outlined using the framework of optimal transportation theory.

Memory and burstiness in dynamic networks

A discrete-time random process is described, which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time t is given by a fixed probability x, is modified to include a memory effect where the event probability is increased proportionally to the number of events that occurred within a given amount of time preceding t. For small values of x the interevent time distribution follows a power law with exponent −2−x. We consider a dynamic network where each node forms, and breaks connections according to this process. The value of x for each node depends on the fitness distribution, \rho(x), from which it is drawn; we find exact solutions for the expectation of the degree distribution for a variety of possible fitness distributions, and for both cases where the memory effect either is, or is not present. This work can potentially lead to methods to uncover hidden fitness distributions from fast changing, temporal network data, such as online social communications and fMRI scans.

Efficient access to conjugated 4,4’-bipyridinium oligomers using the Zincke reaction: synthesis, spectroscopic and electrochemical properties

The cyclocondensation reaction between rigid, electron-rich aromatic diamines and 1,1′-bis(2,4-dinitrophenyl)-4,4′-bipyridinium (Zincke) salts has been harnessed to produce a series of conjugated oligomers containing up to twelve aromatic/heterocyclic residues. These oligomers exhibit discrete, multiple redox processes accompanied by dramatic changes in electronic absorption spectra.