Werner Kristjanpoller

Volatility forecast using hybrid Neural Network models

In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico. A detail of the methodology and application of the volatility forecast of financial series using a hybrid artificial Neural Network model are presented. The results demonstrate that the ANN models can improve the forecasting performance of the GARCH models when studied in the three Latin-American markets and it is shown that the results are robust and consistent for different ANN specifications and different volatility measures.

Gold price volatility

In this study, a hybrid model is analyzed to predict the price return volatility of the gold spot price and future price.The hybrid model used is a ANN-GARCH model.The incorporation of the ANN over the best GARCH model with regressors prediction reduces the error increasing the precision of the price return volatility forecasting.It was possible to determine the influence of financial variables into the gold price return volatility. One of the most used methods to forecast price volatility is the generalized autoregressive conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted to improve forecasting models employing a variety of techniques. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network (ANN) to the GARCH method generating an ANN-GARCH. The hybrid ANN-GARCH model is applied to forecast the gold price volatility (spot and future). The results show an overall improvement in forecasting using the ANN-GARCH as compared to a GARCH method alone. An overall reduction of 25% in the mean average percent error was realized using the ANN-GARCH. The results are realized using the Euro/Dollar and Yen/Dollar exchange rates, the DJI and FTSE stock market indexes, and the oil price return as inputs. We discuss the implications of the study within the context of the discipline as well as practical applications.

Forecasting volatility of oil price using an artificial neural network-GARCH model

A hybrid model is analyzed to predict oil price return volatility.The hybrid model used is an ANN-GARCH model.The ANN improves forecasting accuracy over the GARCH and ARFIMA model prediction.The precision of the price return volatility forecasting increases by 30%.The main financial variables to improve the forecast were determined. This paper builds on previous research and seeks to determine whether improvements can be achieved in the forecasting of oil price volatility by using a hybrid model and incorporating financial variables. The main conclusion is that the hybrid model increases the volatility forecasting precision by 30% over previous models as measured by a heteroscedasticity-adjusted mean squared error (HMSE) model. Key financial variables included in the model that improved the prediction are the Euro/Dollar and Yen/Dollar exchange rates, and the DJIA and FTSE stock market indexes.

A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis

Abstract Measurement, prediction, and modeling of currency price volatility constitutes an important area of research at both the national and corporate level. Countries attempt to understand currency volatility to set national economic policies and firms to best manage exchange rate risk and leverage assets. A relatively new technological invention that the corporate treasurer has to turn to as part of the overall financial strategy is cryptocurrency. One estimate values the total market capitalization of cryptocurrencies at

Impact of fuel price fluctuations on airline stock returns

557 billion USD at the beginning of 2018. While the overall size of the market for cryptocurrency is significant, our understanding of the behavior of this instrument is only beginning. In this article, we propose a hybrid Artificial Neural Network-Generalized AutoRegressive Conditional Heteroskedasticity (ANN-GARCH) model with preprocessing to forecast the price volatility of bitcoin, the most traded and largest by market capitalization of the cryptocurrencies.