3,471 research outputs found
Crude oil risk forecasting : new evidence from multiscale analysis approach
Fluctuations in the crude oil price allied to risk have increased significantly over the last decade frequently varying at different risk levels. Although existing models partially predict such variations, so far, they have been unable to predict oil prices accurately in this highly volatile market. The development of an effective, predictive model has therefore become a prime objective of research in this field. Our approach, albeit based in part on previous research, develops an original methodology, in that we have created a risk forecasting model with the ability to predict oil price fluctuations caused by changes in both fundamental and transient risk factors. We achieve this by disintegrating the multi-scale risk-structure of the crude oil market using Variational Mode Decomposition. Normal and transient risk factors are then extracted from the crude oil price using Variational Mode Decomposition and modelled separately using the Quantile Regression Neural Network (QRNN) model. Both risk factors are integrated and ensembled to produce the risk estimates. We then apply our proposed risk forecasting model to predicting future downside risk level in three major crude oil markets, namely the West Taxes Intermediate (WTI), the Brent Market, and the OPEC market. The results demonstrate that our model has the ability to capture downside risk estimates with significantly improved precision, thus reducing estimation errors and increasing forecasting reliability
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations
Modeling and forecasting exchange rate volatility in time-frequency domain
This paper proposes an enhanced approach to modeling and forecasting
volatility using high frequency data. Using a forecasting model based on
Realized GARCH with multiple time-frequency decomposed realized volatility
measures, we study the influence of different timescales on volatility
forecasts. The decomposition of volatility into several timescales approximates
the behaviour of traders at corresponding investment horizons. The proposed
methodology is moreover able to account for impact of jumps due to a recently
proposed jump wavelet two scale realized volatility estimator. We propose a
realized Jump-GARCH models estimated in two versions using maximum likelihood
as well as observation-driven estimation framework of generalized
autoregressive score. We compare forecasts using several popular realized
volatility measures on foreign exchange rate futures data covering the recent
financial crisis. Our results indicate that disentangling jump variation from
the integrated variation is important for forecasting performance. An
interesting insight into the volatility process is also provided by its
multiscale decomposition. We find that most of the information for future
volatility comes from high frequency part of the spectra representing very
short investment horizons. Our newly proposed models outperform statistically
the popular as well conventional models in both one-day and multi-period-ahead
forecasting
The prediction of future from the past: an old problem from a modern perspective
The idea of predicting the future from the knowledge of the past is quite
natural when dealing with systems whose equations of motion are not known. Such
a long-standing issue is revisited in the light of modern ergodic theory of
dynamical systems and becomes particularly interesting from a pedagogical
perspective due to its close link with Poincar\'e's recurrence. Using such a
connection, a very general result of ergodic theory - Kac's lemma - can be used
to establish the intrinsic limitations to the possibility of predicting the
future from the past. In spite of a naive expectation, predictability results
to be hindered rather by the effective number of degrees of freedom of a system
than by the presence of chaos. If the effective number of degrees of freedom
becomes large enough, regardless the regular or chaotic nature of the system,
predictions turn out to be practically impossible. The discussion of these
issues is illustrated with the help of the numerical study of simple models.Comment: 9 pages, 4 figure
Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model
Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH-) based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models
Multiscale oil-stocks dynamics: the case of Visegrad group and Russia
This paper tries to determine the strength of the interdependence
between Brent oil market and the stock markets of oil importing
Visegrad group countries and oil exporting Russia in different
time-horizons. The paper uses several novel and elaborate methodologies
– bivariate DCC-EGARCH model, wavelet correlations
and phase difference. The results of DCC model show that all
dynamic correlations between Brent oil and the selected stock
indices are low at daily-frequency level. The magnitude of mutual
correlations does not exceed 20% for Visegrad countries, while
for Russia it goes little bit over 30%. Wavelet correlations in shortterm
confirms DCC results, whereby this relatively weak connection
is found up to 32 days. However, in midterm and long-term,
wavelet correlations strengthen, and go above 50% in midterm
and even beyond 80% in long-term for majority of the indices.
Slovakian SAX index has stronger wavelet correlation in 32 days
than in 64 days, and it goes around 23%. This means that SAX
can be coupled with Brent oil for diversification purposes in both
short-term and midterm portfolios. Besides, phase-difference
methodology provides an evidence that SAX was in anti-phase
position in two separate occasions, meaning that SAX can also
serve well for hedging purposes
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