Tests of Financial Market Contagion: Evolutionary Cospectral Analysis Versus Wavelet Analysis

Ftiti, Zied; Tiwari, Aviral Kumar; Belanes, Amel; Guesmi, Khaled

doi:10.1007/s10614-014-9461-8

Cited by 33 publications

(13 citation statements)

References 56 publications

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“…As Torrence and Compo (1998) wrote, the COI is a usual problem for finite-length time series and may occur at the beginning and at the end of the spectrogram or the PWS representation. The second reason is the nature of the Fourier transformation (Ftiti et al, 2014). The persistence of a long-term trend component could be also expected with respect to the FOD transform, as we mentioned at the beginning of the Results section.…”

Section: Cwt and Stft Spectrograms Tested By Ge07 Testmentioning

confidence: 85%

“…we use a quantity-based measure and not a price-based measure of the cycle. As proved by relevant empirical studies, the chosen methodology is applicable to our date range (see Aloui et al, 2016;Altar et al, 2017;Berdiev & Chang, 2015;Fidrmuc et al, 2014;Ftiti et al, 2014;Galati et al, 2016or Kunovac et al, 2018. From the first overview of an input time series in Figure 2a-b, we can see that the time series of Households and Corporates contain a long-term trend which goes through visible expansion and recession phases.…”

Section: Datamentioning

confidence: 99%

“…In our empirical analysis, we focus on the CWT modelling mainly due to its wide range of economic applications and its popularity among economists. Further, the CWT has a better time resolution compared to the STFT and the TFAR (Aloui et al, 2016;Ftiti et al, 2014;Marsalek, Pomenkova, & Kapounek, 2013) which is important for economic applications in general. To confirm the CWT results, we use the STFT method which estimates the spectrogram in the moving time window, therefore, it can differ in low frequencies (due to the window used).…”

Section: Cwt and Stft Spectrograms Tested By Ge07 Testmentioning

confidence: 99%

See 2 more Smart Citations

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

Poměnková

Klejmova

Kučerová

2019

Baltic Journal of Economics

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The paper deals with the identification of time-frequency regions describing cyclicality of bank loans before, during and after the 2008 crisis via wavelets. We bring new methods and findings about the short and medium cycles of loans provided to corporates and households in the Euro Area in 2000-2017 using seasonally unadjusted monthly data. We have recognized an impact of the crisis on data volatility which further influences the type of significance testing of wavelet spectrograms. To avoid this influence we propose: (1) an adaptive spectrogram testing based on Torrence and Compo approach and (2) robustness analysis via enhanced spectrogram modelling tested by the MC simulations. Both cross-checked approaches prove the sensitivity of standard wavelet tests on data volatility. The results confirm the usability of the new approaches and show that the crisis in 2008 influenced the cyclical behaviour of both categories of economic sectors, but in a different way.

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Section: Cwt and Stft Spectrograms Tested By Ge07 Testmentioning

confidence: 85%

Section: Datamentioning

confidence: 99%

Section: Cwt and Stft Spectrograms Tested By Ge07 Testmentioning

confidence: 99%

See 1 more Smart Citation

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

Poměnková

Klejmova

Kučerová

2019

Baltic Journal of Economics

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“…In this way we can capture a temporal character of co-movement and obtain more precise evaluation of co-movement from time as well as frequency point of view. As many experts agreed, the main advantages of wavelets are: the applicability on stationary and non-stationary time series; the flexibility of choice of mother wavelet with respect to the character of inputs; the ability to uncover unique complicated patterns over time and a good time resolution [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries

2019

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The paper deals with the identification and the description of the co-movement between the US and G8 countries with regard to the impact of the structural change, i.e. the financial crisis in 2008. For the identification of the co-movement, we use an optimized segmentation-adaptive-based approach (SAB) of significance testing of the power wavelet cross-spectrum. The SAB testing is based on the standard testing for the power wavelet cross-spectrum adapted for the case if the data have several levels of volatility during the time evolution, i.e. the data can be split into several segments with different volatility. The number of segments is set by the heteroscedasticity test and the test for comparing variances in the segments of the time series. The SAB testing allows us to identify significant co-movement with respect to the local variance, which can reveal additional significant co-movement areas. We apply this approach to the monthly data of industrial production index for G8 countries in 1993–2017.

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“…If we focus on exploring economic phenomena, CWT is at the forefront of economic interest [19,20]. Application of CWT helps with modelling and exploration of the joint movement of economic indicators.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Co-movement Significance Testing with Respect to Gaussian White Noise Background

2018

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Abstract. The paper deals with significance testing of time series co-movement measured via wavelet analysis, namely via the wavelet cross-spectra. This technique is very popular for its better time resolution compare to other techniques. Such approach put in evidence the existence of both long-run and short-run co-movement. In order to have better predictive power it is suitable to support and validate obtained results via some testing approach. We investigate the test of wavelet power cross-spectrum with respect to the Gaussian white noise background with the use of the Bessel function. Our experiment is performed on real data, i.e. seasonally adjusted quarterly data of gross domestic product of the United Kingdom, Korea and G7 countries. To validate the test results we perform Monte Carlo simulation. We describe the advantages and disadvantages of both approaches and formulate recommendations for its using.

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Tests of Financial Market Contagion: Evolutionary Cospectral Analysis Versus Wavelet Analysis

Cited by 33 publications

References 56 publications

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

Cyclicality in lending activity of Euro area in pre- and post- 2008 crisis: a local-adaptive-based testing of wavelets

Optimized Segmentation-Adaptive-Based Testing of the Wavelet Co-movement Analysis: the Case of US and G8 Countries

Wavelet Co-movement Significance Testing with Respect to Gaussian White Noise Background

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