World Financial 2014-2016 Market Bubbles: Oil Negative - US Dollar Positive

Wątorek, Marcin; Dróżdż, S.; Oświęcimka, Paweł

doi:10.12693/aphyspola.129.932

Cited by 12 publications

(8 citation statements)

References 40 publications

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“…The speed of information processing in contemporary markets is systematically increasing and therefore in order to better capture the underlying dynamics the present analysis is performed for higher frequency intraday 5 min recordings in the period between January 02, 2012 and December 29 2017. This period is particularly interesting because of the positive bubble on the US dollar and the negative bubble on the oil market as reported by Tokic (2015), Fantazzini (2016), Fomin et al (2016) and Watorek et al (2016). In the present study the MFCCA proposed by Oświȩcimka et al (2014) and the q-dependent detrended cross-correlation coefficient ρ q proposed by Kwapień et al (2015) are employed to analyse the cross-correlations between WTI Crude Oil futures (CL) and thirteen most important financial instruments: E-mini S&P500 futures (ES) as an appropriate representation of the world stock market, gold futures (GC) and 11 currencies expressed in the US dollar.…”

Section: Introductionmentioning

confidence: 89%

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

et al. 2019

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Statistical and multiscaling characteristics of WTI Crude Oil futures prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012 -December 2017 are studied. It is shown that in most of the cases the tails of return distributions of the considered financial instruments follow the inverse cubic power law. The only exception is the Russian ruble for which the distribution tail is heavier and scales with the exponent close to 2. From the perspective of multiscaling the analysed time series reveal the multifractal organization with the left-sided asymmetry of the corresponding singularity spectra. Even more, all the considered financial instruments appear to be multifractally crosscorrelated with oil, especially on the level of medium-size fluctuations, as the multifractal cross-correlation analysis carried out by means of the multifractal cross-correlation analysis (MFCCA) and detrended cross-correlation coefficient ρ q show. The degree of such cross-correlations is however varying among the financial instruments. The strongest ties to the oil characterize currencies of the oil extracting countries. Strength of this multifractal coupling appears to depend also on the oil market trend. In the analysed time period the level of cross-correlations systematically increases during the bear phase on the oil market and it saturates after the trend reversal in 1st half of * Corresponding author Email address: marcin.watorek@ifj.edu.pl (Marcin Watorek) 2016. The same methodology is also applied to identify possible causal relations between considered observables. Searching for some related asymmetry in the information flow mediating cross-correlations indicates that it was the oil price that led the Russian ruble over the time period here considered rather than vice versa.

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Section: Introductionmentioning

confidence: 89%

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

et al. 2019

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“…It should be noted that solu� on (4) describes the dynamics of the average log-price only up to the cri� cal � me t c and cannot be used beyond it. This crash-� me t c corresponds to the termina� on of the bubble and indicates the change to another regime, which could be either a large crash with accelera� ng oscilla� ons (nega� ve bubble - Wątorek, Drożdż & Oświęcimka, 2016) or decelera� ng oscilla� ons (an� -bubble -Johansen & Sorne� e, 1999) or a change of the average growth rate.…”

Section: -P P L Mmentioning

confidence: 99%

Log-Periodic Power Law and Generalized Hurst Exponent Analysis in Estimating an Asset Bubble Bursting Time

Wątorek

Stawiarski

2016

E-Finanse

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We closely examine and compare two promising techniques helpful in es� ma� ng the moment an asset bubble bursts. Namely, the Log-Periodic Power Law model and Generalized Hurst Exponent approaches are considered. Sequen� al LPPL fi � ng to empirical fi nancial � me series exhibi� ng evident bubble behavior is presented. Es� ma� ng the cri� cal crash-� me works sa� sfactorily well also in the case of GHE, when substan� al "decorrela� on" prior to the event is visible. An extensive simula� on study carried out on empirical data: stock indices and commodi� es, confi rms very good performance of the two approaches.

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“…With such symbolic statistics, a symbol tree can be formed by graphical representation. The number of branches and layers of symbol tree depends on the size of symbol set n and word length L. As shown in Figure 1, the symbol sequence length and probability of the three-level symbol tree are shown [11][12]. Figure 1 shows a symbol tree with three layers when n = 2.…”

Section: B Symbol Treementioning

confidence: 99%

Financial Market Sequence Prediction Based on Image Processing

Liu

2024

IEEE Access

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The rapid development of image and video processing technology has a significant impact on various industries. The analysis and forecast of the financial market income can not only provide investors with investment decisions, but also can correctly formulate various economic control policies for the government. The purpose of this study is to analyze and predict the financial market returns and various indexes based on deep learning CNN neural network algorithm in image processing technology. This study uses the time series method, using the convolution pooling process in CNN to effectively capture the local correlation characteristics of financial market data, then extract the important information hidden in the time series data, draw the trend curve of this information, and combine the features through image processing technology, finally realize the prediction of the financial market time series income index. The results show that in the deep learning algorithm of this study, the highest actual value of stock price after image processing is 3374, and the highest error value is 5.176%, which is nearly 20% less than other algorithms. When N1 = 1600, 3032 sliding windows are obtained, and the Euclidean norm of this point is 0.1586. The conclusion is that the deep learning algorithm of this study is effective and accurate for the prediction of financial market series. Image processing and data analysis technology provide effective methods and make important contributions to the research of financial field.

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World Financial 2014-2016 Market Bubbles: Oil Negative - US Dollar Positive

Cited by 12 publications

References 40 publications

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

Multifractal cross-correlations between the world oil and other financial markets in 2012–2017

Log-Periodic Power Law and Generalized Hurst Exponent Analysis in Estimating an Asset Bubble Bursting Time

Financial Market Sequence Prediction Based on Image Processing

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