During the past decade, many sophisticated mathematical and computational techniques have been developed for analyzing finance data. Financial engineering is a multidisciplinary field that uses tools and concepts of statistics, economics, computational methods and applied mathematics, to solve financial problems and to support the decision-making process of investment, loans, and risk management.

Electricity is a commodity that behaves unlike others in the way that cannot be stored cheaply; its transport depends on existent electrical networks, and can be generated from different sources. This makes the price of electricity highly volatile. As a risk hedging strategy, different financial instruments have been developed, among them, options with electric energy as underlying. We developed a methodology for option pricing in electricity markets, taking into account multi seasonal behavior. Future electricity prices estimation is presented for equatorial countries with similar climate conditions like hydro-dependent energy generation and tropical seasons (e.g. dry season, wet season), affected by El Niño Southern Oscillation. We made a descriptive analysis of the Colombian electricity market. Using mean reversion and mean-reverting jump diffusion models, we made a forecast estimation of intra-day (Hourly) electricity prices based on historical spot data from Colombian electricity market. The models were calibrated and the estimation of derivatives is presented and discussed. We concluded that there is no significant difference between modelling seasonality with dummy variables or with Fourier series.

Although technological developments have provided momentum to extend the frontier of commercially feasible network deployments, the latest Millennium Development Goals Report from the United Nations shows that the digital divide between the rich and poor is increasing due to the lack of affordable services. Therefore, an economic framework is needed to create conditions for affordable network services. In this paper, we introduce a set-aside mechanism that can satisfy this need by reserving resources for targeted groups and resolving the practical problem of having greedy users that rationally compete for cheaper resources. In this mechanism, prices are tailored to users’ budget capacities and quality needs. The simulation results indicate that it is possible to increase the resource allocation for delivering services to the poorest while simultaneously maintaining operators’ revenues by inducing users to discriminate among themselves.

Contact: Sergio Cabrales

This paper explores the properties of using a generalized additive model with embedded variable selection for the prediction of bankruptcy. During the last two decades, many machine learning and artificial intelligence algorithms have been implemented on this problem with the main purpose of provide higher prediction accuracy. However, there exists a duality between interpretation and prediction that have prevented the massive use of these novel methods. Using an additive model allows to incorporate nonlinear effects for each predictor enhancing the predictive power over classical linear models, but at the same time, keeping separated the marginal effects for interpretation. Also, an innovative penalization likelihood approach (GAMSEL, Chouldechova and Hastie (2015)) automatically selects important financial ratios and classifies them between linear and nonlinear effects, improving the interpretation of the estimations.

Contact: Carlos Valencia, Sergio Cabrales