Rodríguez Aguilar, Román
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Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
rrodrigueza
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ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

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Estimation of the Stochastic Volatility of Oil Prices of the Mexican Basket: An Application of Boosting Monte Carlo Markov Chain Estimation

2021 , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román

The volatility of the returns on financial assets is not a constant number over time as many valuation models, mainly derivatives, developed during the 80's, assume. The complexity of non-heteroscedasticity and the difference in results when estimated with different methodologies such as historical, implicit or stochastic calculation, make this subject too extensive a field to be covered in this work. However, stochastic volatility has been widely accepted in recent years. Monte Carlo Markov Chain (MCMC) method is explained and used to estimate the distribution of oil prices of Mexican basket as a stochastic variable. MCMC in the univariate case, supposes that we can estimate the distribution of a latent (hidden) variable through the behavior of another variable observed posteriori with the help of Bayesian inference; this method allows an efficient inference independent of the underlying process through an algorithm. The results show a correct adjustment of stochastic volatility to the behavior of the oil prices. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Financial Fraud Detection Through Artificial Intelligence

2020 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian , Litvinchev, Igor

The present work shows the analysis and modeling of a database with information about the various credit card transactions. The objective is to detect transactions that are fraudulent. In the modeling process, the “Ridge and Lasso”, “Boosting” and “Random Forest” techniques were applied in the modeling and variables selection. The results show that the accuracy of the models was very high, so the metric “Recall” was chosen as a second criterion for selecting the best model. This metric measures the percentage of positive values of the variable “fraud”. It is concluded that the best model is that of “Boosting” with 1,500 trees and a K-Folds of 10 that presented the best results in both training and validation. © 2020, Springer Nature Switzerland AG.

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Text Mining and Statistical Learning for the Analysis of the Voice of the Customer

2020 , Andrade González, Rosalía , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio

This paper analyzes the content of texts through a Text Mining classification model for the particular case of the Tweets made about the Miniso brand in Mexico during the period from November 17 to 24, 2018. The analysis involves the extraction of the data, the cleaning of the text and supervised support models for high-dimensional data, obtaining as a result the classification of the tweets in the topics: Positive, Negative, Advertising or Requirements of new Branches. As well as the use of resampling techniques to measure the variability of the performance of the model and to improve the accuracy of the parameters. This practice allows to reduce time spent reading texts, especially in Social Networks, finding faster and more efficient trends that help decision-making and respond quickly to customer demand. © 2020, Springer Nature Switzerland AG.

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