Insecurity caused by the growth of violence, is a social phenomenon that is affecting the way and quality of life of people. In Lima city, this is caused by multiple factors: Social, economic, political, etc., currently It is generalizing and replicating not only at provincial level also at regional and country level, especially in department capitals, this has resulted in the increase in crime in its different modalities: Homicides, injuries, theft, illicit drug traffic, kidnapping, family violence, which can be cataloged in urban violence and organized violence. This research presents a monitoring and early warning model based on four phases: a) Information talked through a survey, b) determination of factors associated with the perception of insecurity, using correlations and decision tree c) determination of unsafe areas, based on the factors associated with the perception of insecur...

The present work, uses unstructured information in order to predict the academic risk of a student, making use of Machine Learning techniques. Phases: Construction of the datamart: The data from the different sources will be integrated to build the objective data repository, which will be divided into two: Training data and test data, Training of the model: which consists in elaborating the training model based on data from thedatamart, applying vectorial support machine. Validation of the model: It consists of evaluating the model obtained previously, using the test data from the datamart.

This paper extends the threshold stochastic volatility (THSV) model specification proposed in So et al. (2002) and Chen et al. (2008) by incorporating thick-tails in the mean equation innovation using the scale mixture of normal distributions (SMN). A Bayesian Markov Chain Monte Carlo algorithm is developed to estimate all the parameters and latent variables. Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting via a computational Bayesian framework are considered. The MCMC-based method exploits a mixture representation of the SMN distributions. The proposed methodology is applied to daily returns of indexes from BM&F BOVESPA (BOVESPA), Buenos Aires Stock Exchange (MERVAL), Mexican Stock Exchange (MXX) and the Standar & Poors 500 (SP500). Bayesian model selection criteria reveals that there is a significant improvement in model fit for the returns of the data considered here, by u...

This paper extends the threshold stochastic volatility (THSV) model specification proposed in So et al. (2002) and Chen et al. (2008) by incorporating thick-tails in the mean equation innovation using the scale mixture of normal distributions (SMN). A Bayesian Markov Chain Monte Carlo algorithm is developed to estimate all the parameters and latent variables. Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting via a computational Bayesian framework are considered. The MCMC-based method exploits a mixture representation of the SMN distributions. The proposed methodology is applied to daily returns of indexes from BM&F BOVESPA (BOVESPA), Buenos Aires Stock Exchange (MERVAL), Mexican Stock Exchange (MXX) and the Standar & Poors 500 (SP500). Bayesian model selection criteria reveals that there is a significant improvement in model fit for the returns of the data considered he...

Documento de trabajo; 481

The stochastic volatility in mean (SVM) model proposed by Koopman and Uspensky (2002) is revisited. This paper has two goals. The first is to offer a methodology that requires less computational time in simulations and estimates compared with others proposed in the literature as in Abanto-Valle et al. (2021) and others. To achieve the first goal, we propose to approximate the likelihood function of the SVM model applying Hidden Markov Models (HMM) machinery to make possible Bayesian inference in real-time. We sample from then posterior distribution of parameters with a multivariate Normal distribution with mean and variance given by the posterior mode and the inverse of the Hessian matrix evaluated at this posterior mode using importanc sampling (IS). The frequentist properties of estimators is anlyzed conducting a simulation study. The second goal is to provide empirical evidence estima...