Wavelet Co-movement Estimation and Neural Network Forecasting for Energy Commodities, US Stock and US Dollar Indexes

EasyChair Preprint 2028

Abstract

The use of wavelet techniques for studying the dynamics of the time series of oil and gas prices, the Dow Jones index and the US dollar index allowed to establish some correlation relationships between volatility in the relevant markets. By means of discrete wavelet transform and continuous wavelet transform, the wavelet power spectrum of each series was constructed, wavelet coherence for time series paires was investigated, and wavelet multiple correlation was determined. In order to study the co-movement of the time series we measure wavelet coherence, wavelet multiple correlation and cross correlation of the research indicators. Common revenue movements of the studied time series characterize the behavior of the relevant markets. The levels of high volatility at similar intervals explain that there is a link between the changes in these markets, and the global economy is vulnerable to oil and gas prices, the value of the dollar index and the Dow Jones index. At the next stage, a comparison of the predictive capabilities of various neural networks is made. For series-leaders, forecasting models based on neural network of Long Short Term Memory and Wavelet based Back Propagation were built. Comparison of the forcasting errors suggests that the application of both methods on short horizons gives good modeling results.

Keyphrases: Continuous Wavelet Transform, Recurrent Neural Network, Volatility, Wavelet Coherence, cross-correlation, neural networks, oil market, short-term memory, stock market, time scale, time series, us dollar index, wavelet analysis, wavelet multiple correlation and cross correlation, wavelet power spectrum

@booklet{EasyChair:2028,
  author    = {Olena Liashenko and Tetyana Kravets},
  title     = {Wavelet Co-movement Estimation and Neural Network Forecasting for Energy Commodities, US Stock and US Dollar Indexes},
  howpublished = {EasyChair Preprint 2028},
  year      = {EasyChair, 2019}}