This paper provides an evaluation of the predictive performance of the volatility of three crypto... more This paper provides an evaluation of the predictive performance of the volatility of three cryptocurren-cies and three currencies with recognized stores of value using daily and hourly frequency data. We combined the traditional GARCH model with the machine learning approach to volatility estimation, estimating the mean and volatility equations using Support Vector Regression (SVR) and comparing to GARCH family models. Furthermore, the models' predictive ability was evaluated using Diebold-Mariano test and Hansen's Model Confidence Set. The analysis was reiterated for both low and high frequency data. Results showed that SVR-GARCH models managed to outperform GARCH, EGARCH and GJR-GARCH models with Normal, Student's t and Skewed Student's t distributions. For all variables and both time frequencies, the SVR-GARCH model exhibited statistical significance towards its superiority over GARCH and its extensions.
This paper presents a new version of Promethee IV which considers the empirical distribution of t... more This paper presents a new version of Promethee IV which considers the empirical distribution of the criteria through kernel density estimation to evaluate alternatives. The developed method has the ability to treat criteria according to their distribution. The classic Promethee IV can produce divergent integrals, and this could be the cause for its insufficient exploration in literature. The proposed method overcomes this situation since large values have little weight compared to values near the mean.
This paper provides an evaluation of the predictive performance of the volatility of three crypto... more This paper provides an evaluation of the predictive performance of the volatility of three cryptocurren-cies and three currencies with recognized stores of value using daily and hourly frequency data. We combined the traditional GARCH model with the machine learning approach to volatility estimation, estimating the mean and volatility equations using Support Vector Regression (SVR) and comparing to GARCH family models. Furthermore, the models' predictive ability was evaluated using Diebold-Mariano test and Hansen's Model Confidence Set. The analysis was reiterated for both low and high frequency data. Results showed that SVR-GARCH models managed to outperform GARCH, EGARCH and GJR-GARCH models with Normal, Student's t and Skewed Student's t distributions. For all variables and both time frequencies, the SVR-GARCH model exhibited statistical significance towards its superiority over GARCH and its extensions.
This paper presents a new version of Promethee IV which considers the empirical distribution of t... more This paper presents a new version of Promethee IV which considers the empirical distribution of the criteria through kernel density estimation to evaluate alternatives. The developed method has the ability to treat criteria according to their distribution. The classic Promethee IV can produce divergent integrals, and this could be the cause for its insufficient exploration in literature. The proposed method overcomes this situation since large values have little weight compared to values near the mean.
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Papers by Mariana Montenegro