Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model

Mohammed, Syakirah Afiza; Bakar, Mohd Aftar Abu; Ariff, Noratiqah Mohd

doi:10.1080/03610926.2018.1535073

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Other methods could be quoted: classic econometric models, as the wavelet-based exponential generalized autoregressive conditional heteroscedasticity model (Mohammed et al, 2020;Guasti Lima and Assaf Neto, 2022), or causal inference on time series datasets (and thus over stochastic processes) (Palachy, 2019;Shimoni et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Lansiaux¹,

Tchagaspanian²,

Forget

2022

Front. Blockchain

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Context: The third generation of cryptocurrencies gathers cryptocurrencies that are as diverse as the market is big (e.g., Dogecoin or Litecoin). While Dogecoin is seen as a memecoin, the other gathers a very different category of investors. To our knowledge, no study has independently assessed the crypto community’s economical impact on these cryptocurrencies. Furthermore, various methodological possibilities exist to forecast cryptocurrency price—mainly coming from online communities.Method: Our study has retrospectively studied (from 01/01/2015 to 03/11/2021)—using open access data—the association strength (using normalized mutual information) and the linear correlation (using Pearson’s correlation) between Twitter activity and cryptocurrency economical attributes. In addition, we have computed different models (ADF, ARIMA, and Interpretable MultiVvariable Long Short-Term Memory recurrent neural network) that forecast past price values and assessed their precision.Findings and conclusions: While the average Dogecoin transaction value is impacted by tweets, tweets are impacted by Litecoin transactions number and average Litecoin transaction value. Tweet number is impacted by Dogecoin whale behavior, but no significant relationship was found between Litecoin whales and tweets. The forecasting error resulting from our ARIMA (0,0,0) models was 0.08% (with Litecoin) and 0.22% (with Dogecoin). Therefore, those are just the beginning of scientific findings that may lead to building a trading robot based on these results. However, in itself, this study is only for academic discussion, and conclusions need to be drawn by further research. The authors cannot be liable if any financial investment is made based on its conclusions.

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Section: Methodsmentioning

confidence: 99%

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Lansiaux¹,

Tchagaspanian²,

Forget

2022

Front. Blockchain

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“…Wavelet analysis is a technique that can be used to analyze time-frequency characteristics of time domain signals. It has been used extensively in many areas, such as in engineering [5,6], finance [7] and signal processing [8]. Several studies have shown that combining the time series model with wavelet analysis could improve the time series model accuracy [9,10].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Convolutional Neural Network for Forecasting Malaysian PM10 Time Series Data

Bakar¹,

Ariff²,

Nadzir³

2022

Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

Self Cite

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Hourly particulate matter time series data from eight air quality monitoring stations in Peninsular Malaysia were forecast by using the Convolutional Neural Network (CNN) algorithm. Instead of using the original time series, which are time-domain sequence data, this study used the time-frequency domain sequence data retrieved by wavelet transformation. Air pollutants' concentration considered for this study is the particulate matter with a diameter of 10 microns or less, PM 10 . The transformation used in this study is the Morlet wavelet transform, which is continuous wavelet transformation (CWT). Different time steps for the time series dependencies were considered to assess the PM 10 dependencies on its past values. The results were compared with the results from the CNN algorithm using the original time series. It is shown that the Wavelet Convolutional Neural Network algorithm improves the forecast accuracy of the PM 10 time series.

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“…First, the data is clustered or decomposed to generate multiple training subsets [28], and then a prediction model is established for each subset, and finally, multiple prediction results are integrated. The main decomposition methods are wavelet decomposition [29], empirical mode decomposition (EMD) [30], variational mode decomposition (VMD) [31,32], etc. In case of the diversity of data, it is of great difficulty to utilize a single model to learn all the characteristics of the data.…”

Section: Introductionmentioning

confidence: 99%

Adaboost-based Integration Framework Coupled Two-stage Feature Extraction with Deep Learning for Multivariate Exchange Rate Prediction

Wang

Chen

2021

Neural Process Lett

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The foreign exchange market plays an important role in the financial field. Accurately predicting the exchange rate appears to be difficult on account of the characteristics of time variability and randomness. This study proposes an Adaboost-based reinforcement ensemble learning framework, which combines two-stage feature extraction with deep learning models to perform multivariate exchange rate prediction. Considering the impact of data information hidden in other financial markets on the foreign exchange market, multiple exogenous variables are introduced as input factors of the proposed model. Auto-encoder and Self-organizing map, as the main two-stage feature extraction models, have their advantages in simplifying model input and clustering similar feature data respectively. Feature extraction paves the way for the subsequent establishment of deep recurrent neural network (DRNN) for prediction, which improves the robustness of the model while improving the prediction accuracy. Finally, the Adaboost algorithm is utilized to integrate the DRNN prediction results. The empirical results reveal that the proposed model has higher accuracy in exchange rate prediction. The prediction effect of the model is significantly better than comparable models and it is a promising way of forecasting exchange rates.

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Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model

Cited by 6 publications

References 29 publications

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Wavelet Convolutional Neural Network for Forecasting Malaysian PM10 Time Series Data

Adaboost-based Integration Framework Coupled Two-stage Feature Extraction with Deep Learning for Multivariate Exchange Rate Prediction

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