Multiscale systematic risk

Gençay, Ramazan; Selçuk, Faruk; Whitcher, Brandon

doi:10.1016/j.jimonfin.2004.10.003

Cited by 260 publications

(145 citation statements)

References 30 publications

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“…The systematic risk in a Capital Asset Pricing Model was estimated over different granularities, [27], where it was shown that the return of a portfolio and its beta b becomes stronger as the scale increases for the S&P 500. This technique was then applied to the markets of various other countries, [28], with similar results also found. The dependence of stock return cross-correlations on the sampling time scale is known as the Epps effect [29].…”

Section: Introductionmentioning

confidence: 59%

Multiscaled Cross-Correlation Dynamics in Financial Time-Series

Conlon

Ruskin

Crane

2009

Advs. Complex Syst.

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The cross-correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform (MODWT) to calculate correlation matrices over different time scales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different scales provides insight into the interactions between the numerous constituents involved.A study of the eigenvalue spectrum in its entirety provides further insight. On partitioning the eigenvalue time series, we show that negative index returns, (drawdowns), are associated with periods where the largest eigenvalue is greatest, while positive index returns, (drawups), are associated with periods where the largest eigenvalue is smallest. Furthermore, through the study of the small eigenvalues of the correlation matrix, we show that information about the correlation dynamics is visible at both ends of the eigenspectrum across all scales.

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Section: Introductionmentioning

confidence: 59%

Multiscaled Cross-Correlation Dynamics in Financial Time-Series

Conlon

Ruskin

Crane

2009

Advs. Complex Syst.

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“…They show that both regression components have scale-specific predictability on low frequency. Other studies using the frequency domain in asset pricing models include Otrok et al (2002), Gençay et al (2005) and Yu (2012).…”

Section: Recent Surge Of Frequency-based Methods In Financial Economicsmentioning

confidence: 99%

Do Co-Jumps Impact Correlations in Currency Markets?

Baruník

Vácha

2016

SSRN Journal

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We quantify how co-jumps impact correlations in currency markets. To disentangle the continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps on correlations, we propose a new wavelet-based estimator. The proposed estimation framework is able to localize the co-jumps very precisely through wavelet coefficients and identify statistically significant co-jumps. Empirical findings reveal the different behaviors of co-jumps during Asian, European and U.S. trading sessions. Importantly, we document that co-jumps significantly influence correlation in currency markets.

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“…Wavelets in finance are primarily used as a signal decomposition tool (e.g. [32], [16], [14], [17], [18], [51]), or a tool to detect and return comovement between CEE and developed European stock markets. To our knowledge, this is the first study to apply this methodology to CEE stock markets.…”

Section: Introductionmentioning

confidence: 99%