The reaction of stock markets to crashes and events: A comparison study between emerging and mature markets using wavelet transforms

Sharkasi, Adel; Crane, Martin; Ruskin, Heather J.; Matos, J. A. O.

doi:10.1016/j.physa.2005.12.048

Cited by 37 publications

(24 citation statements)

References 18 publications

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“…In financial data, it has been known for some time that the largest Eigenvalue (λ 1 ) contains information on risk associated with the particular assets of which the covariance matrix is comprised, (i.e. the 'market' factor) [30]. Similarly we would expect the largest Eigenvalue to present information from the image that reflects the largest change in the SenseCam recording.…”

Section: Wavelet Multiscale Analysismentioning

confidence: 99%

Multiscaled Cross-Correlation Dynamics on SenseCam Lifelogged Images

Crane

Ruskin

et al. 2013

Lecture Notes in Computer Science

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Abstract. In this paper, we introduce and evaluate a novel approach, namely the use of the cross correlation matrix and Maximum Overlap Discrete Wavelet Transform (MODWT) to analyse SenseCam lifelog data streams. SenseCam is a device that can automatically record images and other data from the wearer's whole day. It is a significant challenge to deconstruct a sizeable collection of images into meaningful events for users. The cross-correlation matrix was used, to characterise dynamical changes in non-stationary multivariate SenseCam images. MODWT was then applied to equal-time Correlation Matrices over different time scales and used to explore the granularity of the largest Eigenvalue and changes, in the ratio of the sub-dominant Eigenvalue spectrum dynamics, over sliding time windows. By examination of the eigenspectrum, we show that these approaches can identify "Distinct Significant Events" for the wearers. The dynamics of the Eigenvalue spectrum across multiple scales provide useful insight on details of major events in SenseCam logged images.

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Section: Wavelet Multiscale Analysismentioning

confidence: 99%

Multiscaled Cross-Correlation Dynamics on SenseCam Lifelogged Images

Crane

Ruskin

et al. 2013

Lecture Notes in Computer Science

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“…the 'market' factor) [20,21]. Similarly we would expect the largest eigenvalue here to present information from the image that reflects the largest change in the SenseCam recording.…”

Section: Eigenvalue Dynamicsmentioning

confidence: 84%

Visual Experience for Recognising Human Activities

Crane

Ruskin

2013

Communications in Computer and Information Science

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Abstract. Technologies for Ambient Assisted Living (AAL) combine new Information and Communication Technologies (ICT) to improve and increase the quality of life of the elderly. The SenseCam visual lifelogging device is now used not only to support memory recall, but also by research groups in other fields in order to investigate human lifestyle. Recent and continuing work in Dublin City University's SCI-SYM centre has been an application and evaluation of a novel approach, namely use of the cross correlation matrix and Maximum Overlap Discrete Wavelet Transform (MODWT) to analyse SenseCam lifelog data streams. By examination of the eigenspectrum, we show that these approaches enable detection of key sources or major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. In this paper, we analyse the data collected from the EvAAL (Evaluating AAL System Through Competitive Benchmarking) competition. The results confirmed our previous findings [1,2]. We believe using this highlight key episodes to identify the boundaries can be used to develop automatic classifiers for visual lifelogs to infer different lifestyle characteristics.

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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%

“…It firstly projects the original data series into different scales using wavelet transform as in (10).…”

Section: Multivariate Wavelet Denoising Algorithmmentioning

confidence: 99%

“…Firstly, wavelet analysis has been used to identify time varying behaviours of market behaviours, key economic variables and their correlation evolutions. A typical example would be the studies on multi-scale price movements and lead lag relationships in various financial markets [3,[6][7][8][9][10]. Empirical studies in these markets reveal the multi-scale structure of prices and evolution of their correlation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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The reaction of stock markets to crashes and events: A comparison study between emerging and mature markets using wavelet transforms

Cited by 37 publications

References 18 publications

Multiscaled Cross-Correlation Dynamics on SenseCam Lifelogged Images

Multiscaled Cross-Correlation Dynamics on SenseCam Lifelogged Images

Visual Experience for Recognising Human Activities

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

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