Programa especializado Analítica de Datos en Finanzas​

El reciente auge de la analítica de datos ha generado nuevas oportunidades y retos para aprovechar los datos financieros en la toma de mejores decisiones que maximicen el valor y minimicen el riesgo para las organizaciones o personas. Independientemente que tenga conocimiento de finanzas o mercado financieros, si le interesa invertir en el corto o largo plazo, debe adquirir competencias básicas de analíticas que le ayuden a soportar sus decisiones de inversión basado en la relación de rentabilidad-riesgo.

A methodology for temperature option pricing in the equatorial regions

Weather derivatives are financial instruments that can be used by organizations or individuals to hedge risks associated with adverse weather conditions. Weather conditions can directly decrease profits by affecting the volume of sales or costs. This paper develops a methodology for temperature option pricing in equatorial regions. In this approach, temperature is forecast by combining deterministic and stochastic models. We find that forecasting daily temperature with a model that combines a truncated third-order Fourier series with a mean reversion stochastic process proves the most accurate for pricing the options. The methodology is calibrated with data gathered in Bogotá, Colombia, using Monte Carlo simulations.

Simulation of photo-voltaic power generation using copula autoregressive models for solar irradiance and air temperature time series

We propose a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature time series using copula functions. The use of copulas for the simulation gives flexibility to represent the serial stochastic variability of the solar irradiation and the air temperature affecting the photo-voltaic (PV) panel energy output. Moreover, it allows to have more control on the desired properties of the time series, not only in the temporal and cross-dependencies, but also in the marginal distributions. We use mixtures of zero mass adjusted density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time marginal distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, accurately models the stochastic phenomena.

Is the built-environment at origin, on route, and at destination associated with bicycle commuting? A gender-informed approach

There is limited evidence on the gender differences and location-specific built-environment factors associated with bicycling in Latin American cities. This study aimed to assess commuting in Bogotá by (1) analyzing the gender-specific trend of the standardized number of bicycle commuters during 2005–2017; and (2) assessing the socio-demographic, community, built-environment and natural factors associated with bicycle commuting stratified by gender. This secondary-data analysis included data from the Household Travel Surveys and Multipurpose Surveys to calculate the number of bicycle commuters per habitant from 2005 to 2017 by gender. We assessed the socio-demographic and built-environment factors fitting generalized additive models stratified by gender using the 2015 Household Travel Survey. Although both women and men increased the standardized number of bicycle commuters, male commuters show a steeper trend than women, evidencing the widening gender gap in bicycle commuting over time.

Optimal waterflooding management using an embedded predictive analytical model

In the petroleum industry, there is an ever-increasing interest in oil recovery processes with high hydrocarbon extraction rates. One of the most common oil recovery processes is waterflooding, which involves the injection of water into a reservoir. This process is often challenging, as there is uncertainty in the reservoir’s properties. In this paper, we propose an optimal waterflooding management methodology for setting the producer and injector wells conditions to maximize the net present value (NPV). Our methodology integrates a predictive analytical model, which models the reservoir performance and forecasts the production rates based on the producer and injector well operating conditions.