Forecasting oil price trends using wavelets and hidden Markov models

Silva, Edmundo de Souza e; Legey, Luiz Fernando Loureiro

doi:10.1016/j.eneco.2010.08.006

Cited by 74 publications

(34 citation statements)

References 17 publications

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“…The number of exceedances for different confidence levels N cl , (cl = 95%, 97.5%, 99%) are listed in the parenthesis as (N 99% , N 97.5% , N 95% ). The number of exceedances for EWMA and DCC-GARCH model are (1,4,10) and (3,12,18) respectively. Table 3 lists the p value of Kupiec backtesting procedure for exceedances observed.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Compared with the hard thresholding, the data processing following soft threshold selection rules are smoother but lost the abrupt changes in the original data. It filters the signal as in (18):…”

Section: Multivariate Wavelet Denoising Algorithmmentioning

confidence: 99%

“…For instance, Yousefi et al [15] used wavelet analysis to decompose crude oil price and extended them directly to make forecasts [15]. de Souza e Silva et al [18] uses wavelet analysis to remove high frequency noises, then uses HIdden Markov Model (HMM) to predict future price movement [18]. Naccache [19] uses wavelet analysis analyze the relationship between macroeconomic conditions proxied by Morgan Stanley Capital International (MSCI) and the crude oil price at finer scales [19].…”

Section: Introductionmentioning

confidence: 99%

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Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Lai

Xiang

2012

Energies

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In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR). The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporates the mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark Exponential Weighted Moving Average (EWMA) and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Multivariate Wavelet Denoising Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Lai

Xiang

2012

Energies

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“…Metode ini menggunakan pola hubungan antara variabel yang akan diramal dengan variabel waktu. Sebagian besar metode runtun waktu cocok digunakan ketika akan memeriksa setiap pola data secara sistematis dan memiliki banyak variabel bebas [1], seperti pada kasus harga minyak mentah. Harga minyak ini dipengaruhi variabel yang berasal dari peristiwa masa lalu, sekarang, dan masa depan yang tidak teratur seperti perang, resesi ekonomi global [2], ketidakseimbangan antara permintaan dan persedian [3] [4], aspek politik [5], dan sebagainya.…”

Section: Pendahuluanunclassified

Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network

Herawati

Latif

2016

INFOTEL

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Abstrak -Metode runtun waktu cocok digunakan ketika akan memeriksa setiap pola data secara sistematis dan memiliki banyak variabel bebas, seperti pada kasus harga minyak mentah. Salah satu penelitian yang memanfaatkan metode runtun waktu adalah integrasi antara Ensemble Empirical Mode Decomposition (EEMD) dan jaringan syaraf berdasarkan algoritma Polak-Ribiére Conjugate Gradient (PCG). Jenis jaringan syaraf menggunakan FeedForward Neural Network (FNN). Namun, FNN memerlukan pengaturan parameter bebas dalam proses pembelajarannya. Sementara, parameter yang sesuai sangat dibutuhkan untuk mendapatkan hasil peramalan yang akurat. Penelitian ini mengusulkan integrasi antara EEMD dan Generalized Regression Neural Network (GRNN). GRNN memiliki keunggulan, yaitu: tidak memerlukan pengaturan parameter dan proses pembelajaran yang cepat. Untuk evaluasi, kinerja metode EEMD-GRNN dibandingkan dengan GRNN. Hasil eksperimen menunjukkan bahwa metode EEMD-GRNN menghasilkan peramalan yang lebih baik dari GRNN. Metode EEMD-GRNN memiliki nilai MSE dan RMSE lebih kecil daripada GRNN. Nilai MSE dan RMSE menggunakan data pengujian untuk WTI berturut-turut sebesar 0,0032 dan 0,0569. Sementara, Nilai MSE dan RMSE menggunakan data pengujian Brent berturut-turut sebesar 0,0017 dan 0,0415.Kata kunci -Peramalan Harga Minyak Mentah, EEMD, GRNN.Abstract -The method of time series suitable for use when it checks each data patterns systematically and has many variables, such as in the case of crude oil prices. One study that utilizes the methods of time series is the integration between Ensemble Empirical Mode Decomposition (EEMD) and neural network algorithms based on Polak-Ribiere Conjugate Gradient (PCG). Type of neural network using Feedforward Neural Network (FNN). However, FNN requires setting free parameters in the learning process. Meanwhile, the appropriate parameters are needed to get accurate forecasting results. This research proposes the integration between EEMD and Generalized Regression Neural Network (GRNN). GRNN has advantages, such as: does not require any parameter settings and a quick learning process. For the evaluation, the performance of the method EEMD-GRNN compared with GRNN. The experimental results showed that the method EEMD-GRNN produce better forecasting of GRNN. Method of EEMD-GRNN has a value of MSE and RMSE smaller than GRNN. The value of MSE and RMSE used test data for WTI successively equal 0.0032 and 0.0569. While the value of MSE and RMSE used test data for Brent successively equal 0,0017 and 0,0415. Beberapa penelitian telah dilakukan dengan menggunakan metode runtun waktu untuk peramalan harga minyak mentah. Salah satu contohnya, penelitian menggunakan GARCH yang dibandingkan dengan model volatilitas [6]. Kemudian, penelitian mengenai integrasi antara ARIMA dan GARCH [7].

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“…For commodities product, discrete wavelet transform (DWT) based method exists in forecasting of crude oil price [12], oil price [13], and natural gas price [14] that are the most interesting products in views of many researchers. There also has a work for forecasting metal prices that consists of aluminum, copper, lead, and zinc [15].…”

Section: B Wavelet Transform In Commodities Price Time Series Forecamentioning

confidence: 99%

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

Bunrit¹,

Kerdprasop²,

Kerdprasop³

2018

IJMLC

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Abstract-This research proposes time series forecasting of commodity prices by using multiresolution analysis from wavelet transform. In this work, discrete wavelet transform based multiresolution decomposition of Deubechies family is used. Firstly, Deubechies wavelet transform is applied to the training set of time series data up to level four. The reconstruction values of the approximation part of wavelet from each level are then used for the forecasting process by ARIMA model. The validation set of data is used to analyze and select the best model from all 4 levels of multiresolution decomposition. Finally, best selected validating model is used for evaluating the remaining testing data set. The forecasting results by using multiresolution analysis are compared to the case where the original data are directly modeled and forecasted by ARIMA. Results based on the mean absolute percentage error evaluation from using multiresolution analysis are better for both of the two studied data including daily gold price and rubber price. By applying multiresolution analysis, the improvement is 10.83% for gold price and 42.68% for rubber price. The variances of errors from the proposed method on both data sets are also much less than directly use the original time series data for forecasting.

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Forecasting oil price trends using wavelets and hidden Markov models

Cited by 74 publications

References 17 publications

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach

Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

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